TBclimateBrunei_Apr2022
2022-04-12
Loading dataset & setup codes
Installing packages & exploratory analysis
##Includes codes for Table 1 and S1 Table
## Loading required package: car## Loading required package: carData## Loading required package: lmtest## Loading required package: zoo##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Loading required package: sandwich## Loading required package: survival## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo## This is dlnm 2.4.7. For details: help(dlnm) and vignette('dlnmOverview').##
## Attaching package: 'dplyr'## The following object is masked from 'package:MASS':
##
## select## The following object is masked from 'package:car':
##
## recode## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
## [1] "epiYr" "epiWk" "avgWindSp" "sun" "RF" "rainyD"
## [7] "minRH" "meanRH" "maxRH" "minT" "aveT" "maxT"
## [13] "AH" "eptb" "ptb" "allTB" "eptbInW" "ptbInW"
## [19] "allTBInW" "ptbBM" "smptbBM" "date" "time"## avgWindSp sun RF minRH
## Min. : 2.964 Min. :11.15 Min. : 0.00 Min. :45.00
## 1st Qu.: 4.028 1st Qu.:42.40 1st Qu.: 18.43 1st Qu.:59.43
## Median : 4.476 Median :50.70 Median : 44.90 Median :63.00
## Mean : 4.579 Mean :49.64 Mean : 61.05 Mean :62.55
## 3rd Qu.: 4.881 3rd Qu.:57.48 3rd Qu.: 91.80 3rd Qu.:65.79
## Max. :10.173 Max. :76.00 Max. :414.50 Max. :80.57
## meanRH maxRH minT aveT
## Min. :70.00 Min. : 87.71 Min. :21.84 Min. :24.51
## 1st Qu.:80.29 1st Qu.: 95.14 1st Qu.:23.49 1st Qu.:27.19
## Median :82.71 Median : 96.86 Median :23.96 Median :27.57
## Mean :82.45 Mean : 96.27 Mean :23.97 Mean :27.63
## 3rd Qu.:84.86 3rd Qu.: 98.00 3rd Qu.:24.40 3rd Qu.:28.10
## Max. :92.14 Max. :100.00 Max. :26.33 Max. :30.01
## maxT AH
## Min. :27.13 Min. :26.39
## 1st Qu.:31.53 1st Qu.:30.40
## Median :32.17 Median :31.23
## Mean :32.13 Mean :31.14
## 3rd Qu.:32.70 3rd Qu.:32.01
## Max. :35.46 Max. :35.11## avgWindSp sun RF minRH meanRH maxRH minT
## 0.8125501 11.0464294 56.1497573 4.9608453 3.4318471 2.4541159 0.6828157
## aveT maxT AH
## 0.7206880 0.9957562 1.3097508## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 3.000 3.376 5.000 13.000## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 2.095 3.000 8.000## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.504 2.000 8.000## ptb
## epiYr 0 1 2 3 4 5 6 7 8 9 10 11 12 13
## 2001 2 5 9 14 8 6 4 2 0 1 0 0 0 1
## 2002 3 6 12 9 6 5 4 3 4 0 0 0 0 0
## 2003 3 6 11 10 14 4 2 3 0 0 0 0 0 0
## 2004 2 9 15 9 5 8 1 2 0 1 0 0 0 0
## 2005 4 13 10 5 13 3 3 1 0 0 0 0 0 0
## 2006 1 7 10 13 11 7 0 0 2 1 0 0 0 0
## 2007 1 12 12 9 10 1 4 1 0 1 1 0 0 0
## 2008 0 5 16 11 8 6 4 2 1 0 0 0 0 0
## 2009 1 9 12 14 1 10 4 0 1 0 0 0 0 0
## 2010 0 5 10 13 9 8 1 3 2 0 0 1 0 0
## 2011 3 5 11 13 4 7 4 3 1 1 0 0 0 0
## 2012 4 5 5 9 9 5 8 4 2 1 0 0 0 0
## 2013 5 8 5 12 11 4 5 0 2 0 0 0 0 0
## 2014 3 7 14 10 11 3 0 2 3 0 0 0 0 0
## 2015 3 6 11 11 7 5 4 2 2 0 1 0 0 0
## 2016 0 4 7 16 10 11 1 2 0 1 0 0 0 0
## 2017 4 6 9 5 10 5 5 3 3 1 1 0 0 0
## 2018 4 7 6 8 11 5 2 3 2 2 0 1 1 0## ptbBM
## epiYr 0 1 2 3 4 5 6 7 8
## 2001 10 9 16 7 5 2 3 0 0
## 2002 11 14 14 7 2 0 3 1 0
## 2003 6 18 15 9 4 1 0 0 0
## 2004 9 14 13 6 8 2 0 0 0
## 2005 9 20 10 8 3 2 0 0 0
## 2006 6 11 15 10 9 0 1 0 0
## 2007 12 15 9 10 4 1 1 0 0
## 2008 7 10 16 13 5 2 0 0 0
## 2009 5 19 13 9 3 3 0 0 0
## 2010 3 14 16 9 4 4 1 0 1
## 2011 9 13 16 5 5 3 0 0 1
## 2012 6 12 8 15 7 3 1 0 0
## 2013 10 10 13 9 9 1 0 0 0
## 2014 5 14 18 10 2 1 2 1 0
## 2015 7 11 11 11 7 2 3 0 0
## 2016 4 9 15 12 6 5 1 0 0
## 2017 9 11 8 11 6 4 3 0 0
## 2018 7 9 11 16 3 1 3 2 0## smptbBM
## epiYr 0 1 2 3 4 5 7 8
## 2001 20 13 12 6 0 1 0 0
## 2002 13 19 12 4 1 3 0 0
## 2003 8 23 14 8 0 0 0 0
## 2004 12 14 13 8 5 0 0 0
## 2005 10 24 10 4 3 1 0 0
## 2006 7 14 17 8 5 1 0 0
## 2007 13 15 10 10 2 2 0 0
## 2008 11 18 9 9 4 2 0 0
## 2009 8 20 11 8 4 1 0 0
## 2010 4 20 17 2 6 2 0 1
## 2011 19 15 13 2 0 2 1 0
## 2012 13 16 16 5 1 1 0 0
## 2013 18 14 12 7 1 0 0 0
## 2014 13 19 14 5 1 1 0 0
## 2015 10 13 15 9 4 1 0 0
## 2016 9 16 13 12 2 0 0 0
## 2017 20 9 14 7 1 1 0 0
## 2018 15 17 9 6 4 1 0 0## [1] 3170## [1] 1967## [1] 1412## [1] 1.519793## [1] 2## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 0 0 1 1 2 2 2 3 3 4 8## [1] 1.263198## [1] 1## 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
## 0 0 0 1 1 1 2 2 2 3 8## 0% 5% 10% 50% 90% 95% 100%
## 2.964286 3.571429 3.742857 4.476190 5.448810 6.139881 10.172619## 0% 5% 10% 50% 90% 95% 100%
## 11.150 30.835 34.470 50.700 62.810 66.170 76.000## 0% 5% 10% 50% 90% 95% 100%
## 0.00 0.83 4.45 44.90 134.58 160.71 414.50## 0% 5% 10% 50% 90% 95% 100%
## 45.00000 53.71429 56.00000 63.00000 68.57143 70.30000 80.57143## 0% 5% 10% 50% 90% 95% 100%
## 70.00000 76.00000 77.71429 82.71429 86.57143 87.57143 92.14286## 0% 5% 10% 50% 90% 95% 100%
## 87.71429 90.98571 92.85714 96.85714 99.00000 99.28571 100.00000## 0% 5% 10% 50% 90% 95% 100%
## 26.39459 28.76145 29.35032 31.22532 32.66536 33.15713 35.11290## 0% 5% 10% 50% 90% 95% 100%
## 21.84286 22.85571 23.15714 23.95714 24.84286 25.10000 26.32857## 0% 5% 10% 50% 90% 95% 100%
## 24.51429 26.50000 26.78286 27.57429 28.57429 28.85714 30.01429## 0% 5% 10% 50% 90% 95% 100%
## 27.12857 30.58429 30.92857 32.17143 33.41429 33.67143 35.45714## 0% 5% 10% 50% 90% 95% 100%
## 0 1 1 3 6 7 13## 0% 5% 10% 50% 90% 95% 100%
## 0 0 0 2 4 5 8## 0% 5% 10% 50% 90% 95% 100%
## 0 0 0 1 3 4 8##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha## The following object is masked from 'package:car':
##
## logit## Loading required package: lattice## Loading required package: Formula##
## Attaching package: 'Hmisc'## The following object is masked from 'package:psych':
##
## describe## The following objects are masked from 'package:dplyr':
##
## src, summarize## The following objects are masked from 'package:base':
##
## format.pval, units
## avgWindSp sun RF rainyD minRH meanRH maxRH minT aveT maxT
## avgWindSp 1.00 -0.01 0.05 0.05 0.13 0.06 -0.16 0.02 -0.17 -0.22
## sun -0.01 1.00 -0.26 -0.26 -0.30 -0.33 -0.13 0.34 0.57 0.45
## RF 0.05 -0.26 1.00 0.87 0.38 0.49 0.46 -0.35 -0.42 -0.32
## rainyD 0.05 -0.26 0.87 1.00 0.36 0.45 0.42 -0.33 -0.42 -0.31
## minRH 0.13 -0.30 0.38 0.36 1.00 0.79 0.53 -0.04 -0.52 -0.77
## meanRH 0.06 -0.33 0.49 0.45 0.79 1.00 0.68 -0.08 -0.49 -0.59
## maxRH -0.16 -0.13 0.46 0.42 0.53 0.68 1.00 -0.10 -0.32 -0.35
## minT 0.02 0.34 -0.35 -0.33 -0.04 -0.08 -0.10 1.00 0.70 0.39
## aveT -0.17 0.57 -0.42 -0.42 -0.52 -0.49 -0.32 0.70 1.00 0.82
## maxT -0.22 0.45 -0.32 -0.31 -0.77 -0.59 -0.35 0.39 0.82 1.00
## AH -0.13 0.25 0.04 0.00 0.23 0.43 0.34 0.65 0.51 0.25
## ptbBM -0.04 -0.01 0.01 0.04 0.03 0.08 0.05 0.04 0.02 0.00
## smptbBM -0.02 0.01 -0.02 0.01 -0.03 -0.02 -0.02 0.04 0.03 0.03
## AH ptbBM smptbBM
## avgWindSp -0.13 -0.04 -0.02
## sun 0.25 -0.01 0.01
## RF 0.04 0.01 -0.02
## rainyD 0.00 0.04 0.01
## minRH 0.23 0.03 -0.03
## meanRH 0.43 0.08 -0.02
## maxRH 0.34 0.05 -0.02
## minT 0.65 0.04 0.04
## aveT 0.51 0.02 0.03
## maxT 0.25 0.00 0.03
## AH 1.00 0.11 0.01
## ptbBM 0.11 1.00 0.81
## smptbBM 0.01 0.81 1.00
##
## n= 939
##
##
## P
## avgWindSp sun RF rainyD minRH meanRH maxRH minT aveT
## avgWindSp 0.6796 0.1341 0.1332 0.0001 0.0575 0.0000 0.4540 0.0000
## sun 0.6796 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## RF 0.1341 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## rainyD 0.1332 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## minRH 0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.2469 0.0000
## meanRH 0.0575 0.0000 0.0000 0.0000 0.0000 0.0000 0.0116 0.0000
## maxRH 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0025 0.0000
## minT 0.4540 0.0000 0.0000 0.0000 0.2469 0.0116 0.0025 0.0000
## aveT 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## maxT 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
## AH 0.0000 0.0000 0.2161 0.9627 0.0000 0.0000 0.0000 0.0000 0.0000
## ptbBM 0.2093 0.6817 0.6638 0.2360 0.3328 0.0105 0.1099 0.2402 0.4590
## smptbBM 0.4920 0.7409 0.5051 0.8599 0.4249 0.5255 0.4490 0.2252 0.3224
## maxT AH ptbBM smptbBM
## avgWindSp 0.0000 0.0000 0.2093 0.4920
## sun 0.0000 0.0000 0.6817 0.7409
## RF 0.0000 0.2161 0.6638 0.5051
## rainyD 0.0000 0.9627 0.2360 0.8599
## minRH 0.0000 0.0000 0.3328 0.4249
## meanRH 0.0000 0.0000 0.0105 0.5255
## maxRH 0.0000 0.0000 0.1099 0.4490
## minT 0.0000 0.0000 0.2402 0.2252
## aveT 0.0000 0.0000 0.4590 0.3224
## maxT 0.0000 0.8802 0.3192
## AH 0.0000 0.0011 0.6952
## ptbBM 0.8802 0.0011 0.0000
## smptbBM 0.3192 0.6952 0.0000## Warning in adf.test(week$ptb): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$ptb
## Dickey-Fuller = -9.1934, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$ptbBM): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$ptbBM
## Dickey-Fuller = -9.5192, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$smptbBM): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$smptbBM
## Dickey-Fuller = -9.064, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$avgWindSp): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$avgWindSp
## Dickey-Fuller = -7.3964, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$sun): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$sun
## Dickey-Fuller = -7.8712, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$RF): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$RF
## Dickey-Fuller = -8.559, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$rainyD): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$rainyD
## Dickey-Fuller = -9.33, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$minRH): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$minRH
## Dickey-Fuller = -6.7584, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$meanRH): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$meanRH
## Dickey-Fuller = -5.8153, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$maxRH): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$maxRH
## Dickey-Fuller = -4.7739, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$AH): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$AH
## Dickey-Fuller = -5.3508, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$minT): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$minT
## Dickey-Fuller = -4.6279, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$aveT): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$aveT
## Dickey-Fuller = -6.7573, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary## Warning in adf.test(week$maxT): p-value smaller than printed p-value##
## Augmented Dickey-Fuller Test
##
## data: week$maxT
## Dickey-Fuller = -7.4593, Lag order = 9, p-value = 0.01
## alternative hypothesis: stationary
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 2.000 2.095 3.000 8.000## [1] 2.30977
##
## Overdispersion test
##
## data: test1
## z = 2.0695, p-value = 0.01925
## alternative hypothesis: true alpha is greater than 0
## sample estimates:
## alpha
## 0.1014562
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.504 2.000 8.000## [1] 1.595668
##
## Overdispersion test
##
## data: test2
## z = 1.1918, p-value = 0.1167
## alternative hypothesis: true alpha is greater than 0
## sample estimates:
## alpha
## 0.06001201## [1] model aic theta
## <0 rows> (or 0-length row.names)##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 1), data = week,
## init.theta = 29.52230207, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.35406 -0.79927 -0.06845 0.57730 2.82853
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.653050 0.201843 3.235 0.00121 **
## ns(time, df = 18 * 1)1 -0.244104 0.258448 -0.944 0.34492
## ns(time, df = 18 * 1)2 0.318754 0.328302 0.971 0.33159
## ns(time, df = 18 * 1)3 -0.524208 0.303650 -1.726 0.08428 .
## ns(time, df = 18 * 1)4 0.279397 0.312652 0.894 0.37152
## ns(time, df = 18 * 1)5 -0.216288 0.303879 -0.712 0.47661
## ns(time, df = 18 * 1)6 0.220681 0.303638 0.727 0.46736
## ns(time, df = 18 * 1)7 -0.073592 0.303204 -0.243 0.80823
## ns(time, df = 18 * 1)8 0.008738 0.301901 0.029 0.97691
## ns(time, df = 18 * 1)9 0.250586 0.294890 0.850 0.39546
## ns(time, df = 18 * 1)10 0.058639 0.295705 0.198 0.84281
## ns(time, df = 18 * 1)11 0.311273 0.295230 1.054 0.29173
## ns(time, df = 18 * 1)12 -0.218212 0.303157 -0.720 0.47165
## ns(time, df = 18 * 1)13 0.213599 0.294766 0.725 0.46867
## ns(time, df = 18 * 1)14 0.259764 0.288802 0.899 0.36841
## ns(time, df = 18 * 1)15 0.297542 0.287292 1.036 0.30035
## ns(time, df = 18 * 1)16 -0.010036 0.235495 -0.043 0.96601
## ns(time, df = 18 * 1)17 0.470497 0.509205 0.924 0.35550
## ns(time, df = 18 * 1)18 0.349525 0.201300 1.736 0.08250 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(29.5223) family taken to be 1)
##
## Null deviance: 1111.3 on 938 degrees of freedom
## Residual deviance: 1083.0 on 920 degrees of freedom
## AIC: 3334.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 29.5
## Std. Err.: 21.2
##
## 2 x log-likelihood: -3294.422##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 2), data = week,
## init.theta = 80.78772003, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3802 -0.7795 -0.0975 0.6000 3.3961
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 9.738e-01 2.558e-01 3.807 0.00014 ***
## ns(time, df = 18 * 2)1 7.139e-01 3.285e-01 2.173 0.02978 *
## ns(time, df = 18 * 2)2 -1.497e+00 4.638e-01 -3.227 0.00125 **
## ns(time, df = 18 * 2)3 5.091e-01 3.837e-01 1.327 0.18459
## ns(time, df = 18 * 2)4 -9.245e-01 4.282e-01 -2.159 0.03085 *
## ns(time, df = 18 * 2)5 1.637e-03 3.996e-01 0.004 0.99673
## ns(time, df = 18 * 2)6 -2.237e-01 4.173e-01 -0.536 0.59202
## ns(time, df = 18 * 2)7 -1.125e+00 4.408e-01 -2.553 0.01069 *
## ns(time, df = 18 * 2)8 1.986e-01 4.136e-01 0.480 0.63112
## ns(time, df = 18 * 2)9 -1.037e+00 4.232e-01 -2.452 0.01422 *
## ns(time, df = 18 * 2)10 4.936e-01 3.955e-01 1.248 0.21203
## ns(time, df = 18 * 2)11 -9.522e-01 4.210e-01 -2.262 0.02372 *
## ns(time, df = 18 * 2)12 -5.817e-02 4.079e-01 -0.143 0.88659
## ns(time, df = 18 * 2)13 -5.178e-01 4.053e-01 -1.278 0.20137
## ns(time, df = 18 * 2)14 1.760e-01 3.942e-01 0.446 0.65529
## ns(time, df = 18 * 2)15 -6.705e-01 4.127e-01 -1.625 0.10422
## ns(time, df = 18 * 2)16 -1.159e-01 4.090e-01 -0.283 0.77686
## ns(time, df = 18 * 2)17 -6.915e-01 4.126e-01 -1.676 0.09375 .
## ns(time, df = 18 * 2)18 1.017e-01 3.869e-01 0.263 0.79266
## ns(time, df = 18 * 2)19 1.098e-01 3.876e-01 0.283 0.77694
## ns(time, df = 18 * 2)20 -8.494e-01 4.124e-01 -2.059 0.03945 *
## ns(time, df = 18 * 2)21 8.500e-02 3.873e-01 0.219 0.82627
## ns(time, df = 18 * 2)22 1.201e-01 3.831e-01 0.314 0.75380
## ns(time, df = 18 * 2)23 -5.500e-01 4.017e-01 -1.369 0.17087
## ns(time, df = 18 * 2)24 4.817e-04 3.984e-01 0.001 0.99904
## ns(time, df = 18 * 2)25 -5.537e-01 4.095e-01 -1.352 0.17636
## ns(time, df = 18 * 2)26 -1.604e-01 4.028e-01 -0.398 0.69040
## ns(time, df = 18 * 2)27 -4.287e-01 3.979e-01 -1.077 0.28126
## ns(time, df = 18 * 2)28 1.606e-01 3.800e-01 0.423 0.67263
## ns(time, df = 18 * 2)29 -1.025e-01 3.814e-01 -0.269 0.78804
## ns(time, df = 18 * 2)30 -1.647e-01 3.844e-01 -0.428 0.66837
## ns(time, df = 18 * 2)31 -3.316e-02 3.808e-01 -0.087 0.93061
## ns(time, df = 18 * 2)32 -4.612e-05 3.823e-01 0.000 0.99990
## ns(time, df = 18 * 2)33 -3.803e-01 3.886e-01 -0.979 0.32778
## ns(time, df = 18 * 2)34 2.846e-01 3.112e-01 0.915 0.36041
## ns(time, df = 18 * 2)35 -7.084e-01 6.598e-01 -1.074 0.28300
## ns(time, df = 18 * 2)36 6.221e-01 2.766e-01 2.249 0.02450 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(80.7877) family taken to be 1)
##
## Null deviance: 1153.1 on 938 degrees of freedom
## Residual deviance: 1081.9 on 902 degrees of freedom
## AIC: 3329.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 81
## Std. Err.: 149
##
## 2 x log-likelihood: -3253.643## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 5), data = week,
## init.theta = 13518.66729, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3965 -0.8154 -0.1146 0.5483 2.9246
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.978311 0.362016 2.702 0.00688 **
## ns(time, df = 18 * 5)1 -0.916744 0.573236 -1.599 0.10977
## ns(time, df = 18 * 5)2 -0.541213 0.654556 -0.827 0.40833
## ns(time, df = 18 * 5)3 0.539151 0.538751 1.001 0.31695
## ns(time, df = 18 * 5)4 -0.342646 0.598201 -0.573 0.56678
## ns(time, df = 18 * 5)5 -0.281275 0.605235 -0.465 0.64212
## ns(time, df = 18 * 5)6 -0.191498 0.654094 -0.293 0.76970
## ns(time, df = 18 * 5)7 -1.772333 0.731248 -2.424 0.01536 *
## ns(time, df = 18 * 5)8 0.250614 0.611687 0.410 0.68202
## ns(time, df = 18 * 5)9 -0.153271 0.590485 -0.260 0.79520
## ns(time, df = 18 * 5)10 -0.245183 0.606886 -0.404 0.68621
## ns(time, df = 18 * 5)11 -0.453957 0.629261 -0.721 0.47066
## ns(time, df = 18 * 5)12 -0.525048 0.636302 -0.825 0.40928
## ns(time, df = 18 * 5)13 -0.264962 0.622602 -0.426 0.67042
## ns(time, df = 18 * 5)14 -0.572574 0.610690 -0.938 0.34846
## ns(time, df = 18 * 5)15 0.444277 0.579397 0.767 0.44321
## ns(time, df = 18 * 5)16 -0.749714 0.632012 -1.186 0.23553
## ns(time, df = 18 * 5)17 -0.513897 0.642958 -0.799 0.42413
## ns(time, df = 18 * 5)18 0.082642 0.653266 0.127 0.89933
## ns(time, df = 18 * 5)19 -1.942792 0.771753 -2.517 0.01182 *
## ns(time, df = 18 * 5)20 -0.190972 0.659166 -0.290 0.77203
## ns(time, df = 18 * 5)21 -0.242934 0.611051 -0.398 0.69095
## ns(time, df = 18 * 5)22 0.106385 0.615053 0.173 0.86268
## ns(time, df = 18 * 5)23 -1.277215 0.695482 -1.836 0.06629 .
## ns(time, df = 18 * 5)24 -0.483159 0.648286 -0.745 0.45610
## ns(time, df = 18 * 5)25 -0.027095 0.594489 -0.046 0.96365
## ns(time, df = 18 * 5)26 -0.113935 0.585427 -0.195 0.84569
## ns(time, df = 18 * 5)27 -0.218272 0.594086 -0.367 0.71331
## ns(time, df = 18 * 5)28 -0.228045 0.606311 -0.376 0.70683
## ns(time, df = 18 * 5)29 -0.306576 0.636302 -0.482 0.62994
## ns(time, df = 18 * 5)30 -1.109825 0.678132 -1.637 0.10172
## ns(time, df = 18 * 5)31 -0.073355 0.616435 -0.119 0.90528
## ns(time, df = 18 * 5)32 -0.058936 0.606061 -0.097 0.92253
## ns(time, df = 18 * 5)33 -0.855023 0.633984 -1.349 0.17745
## ns(time, df = 18 * 5)34 0.181277 0.597627 0.303 0.76164
## ns(time, df = 18 * 5)35 -0.705830 0.605901 -1.165 0.24405
## ns(time, df = 18 * 5)36 0.386167 0.571379 0.676 0.49914
## ns(time, df = 18 * 5)37 -0.465338 0.595503 -0.781 0.43456
## ns(time, df = 18 * 5)38 -0.032186 0.608411 -0.053 0.95781
## ns(time, df = 18 * 5)39 -0.885005 0.646566 -1.369 0.17107
## ns(time, df = 18 * 5)40 -0.131853 0.613262 -0.215 0.82976
## ns(time, df = 18 * 5)41 -0.075080 0.613883 -0.122 0.90266
## ns(time, df = 18 * 5)42 -1.025443 0.653735 -1.569 0.11674
## ns(time, df = 18 * 5)43 0.216475 0.615190 0.352 0.72493
## ns(time, df = 18 * 5)44 -0.933064 0.646166 -1.444 0.14874
## ns(time, df = 18 * 5)45 -0.166151 0.607021 -0.274 0.78430
## ns(time, df = 18 * 5)46 -0.053432 0.581606 -0.092 0.92680
## ns(time, df = 18 * 5)47 -0.268826 0.571756 -0.470 0.63823
## ns(time, df = 18 * 5)48 0.345414 0.546709 0.632 0.52751
## ns(time, df = 18 * 5)49 0.104537 0.566795 0.184 0.85367
## ns(time, df = 18 * 5)50 -0.585007 0.632507 -0.925 0.35502
## ns(time, df = 18 * 5)51 -0.857056 0.666199 -1.286 0.19827
## ns(time, df = 18 * 5)52 -0.303565 0.617762 -0.491 0.62315
## ns(time, df = 18 * 5)53 0.094271 0.586220 0.161 0.87224
## ns(time, df = 18 * 5)54 -0.523742 0.594374 -0.881 0.37823
## ns(time, df = 18 * 5)55 0.103321 0.560145 0.184 0.85366
## ns(time, df = 18 * 5)56 0.318603 0.550717 0.579 0.56291
## ns(time, df = 18 * 5)57 -0.437228 0.583432 -0.749 0.45361
## ns(time, df = 18 * 5)58 0.005814 0.584464 0.010 0.99206
## ns(time, df = 18 * 5)59 -0.243766 0.607447 -0.401 0.68820
## ns(time, df = 18 * 5)60 -0.787015 0.634970 -1.239 0.21518
## ns(time, df = 18 * 5)61 -0.024145 0.591017 -0.041 0.96741
## ns(time, df = 18 * 5)62 0.192309 0.587403 0.327 0.74337
## ns(time, df = 18 * 5)63 -0.999734 0.647192 -1.545 0.12241
## ns(time, df = 18 * 5)64 -0.121481 0.620825 -0.196 0.84486
## ns(time, df = 18 * 5)65 -0.471848 0.611378 -0.772 0.44025
## ns(time, df = 18 * 5)66 0.238077 0.605028 0.393 0.69395
## ns(time, df = 18 * 5)67 -1.421516 0.660033 -2.154 0.03126 *
## ns(time, df = 18 * 5)68 0.627575 0.582743 1.077 0.28151
## ns(time, df = 18 * 5)69 -0.644190 0.607869 -1.060 0.28926
## ns(time, df = 18 * 5)70 -0.271272 0.599347 -0.453 0.65083
## ns(time, df = 18 * 5)71 -0.085756 0.569180 -0.151 0.88024
## ns(time, df = 18 * 5)72 0.275578 0.550048 0.501 0.61637
## ns(time, df = 18 * 5)73 -0.178287 0.563648 -0.316 0.75177
## ns(time, df = 18 * 5)74 0.013726 0.565821 0.024 0.98065
## ns(time, df = 18 * 5)75 -0.079980 0.575672 -0.139 0.88950
## ns(time, df = 18 * 5)76 -0.325803 0.590367 -0.552 0.58104
## ns(time, df = 18 * 5)77 -0.201005 0.576851 -0.348 0.72750
## ns(time, df = 18 * 5)78 0.270680 0.557068 0.486 0.62704
## ns(time, df = 18 * 5)79 -0.282734 0.573799 -0.493 0.62220
## ns(time, df = 18 * 5)80 -0.049359 0.571030 -0.086 0.93112
## ns(time, df = 18 * 5)81 0.002628 0.570766 0.005 0.99633
## ns(time, df = 18 * 5)82 -0.307116 0.581223 -0.528 0.59722
## ns(time, df = 18 * 5)83 0.067712 0.571192 0.119 0.90564
## ns(time, df = 18 * 5)84 -0.071086 0.586898 -0.121 0.90359
## ns(time, df = 18 * 5)85 -0.700224 0.618864 -1.131 0.25786
## ns(time, df = 18 * 5)86 -0.092954 0.582906 -0.159 0.87330
## ns(time, df = 18 * 5)87 0.012368 0.548562 0.023 0.98201
## ns(time, df = 18 * 5)88 0.545028 0.440956 1.236 0.21645
## ns(time, df = 18 * 5)89 -0.271709 0.961975 -0.282 0.77760
## ns(time, df = 18 * 5)90 -0.220990 0.445755 -0.496 0.62006
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(13518.67) family taken to be 1)
##
## Null deviance: 1179.0 on 938 degrees of freedom
## Residual deviance: 1054.6 on 848 degrees of freedom
## AIC: 3386.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 13519
## Std. Err.: 119708
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3202.524## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 6), data = week,
## init.theta = 17043.33421, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5533 -0.8230 -0.1055 0.5705 2.6739
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.834068 0.403093 2.069 0.0385 *
## ns(time, df = 18 * 6)1 -0.927636 0.637273 -1.456 0.1455
## ns(time, df = 18 * 6)2 -0.489553 0.763588 -0.641 0.5214
## ns(time, df = 18 * 6)3 -0.033997 0.625651 -0.054 0.9567
## ns(time, df = 18 * 6)4 0.579882 0.624207 0.929 0.3529
## ns(time, df = 18 * 6)5 -0.196154 0.638269 -0.307 0.7586
## ns(time, df = 18 * 6)6 -0.140780 0.671504 -0.210 0.8339
## ns(time, df = 18 * 6)7 0.044422 0.693083 0.064 0.9489
## ns(time, df = 18 * 6)8 -1.054079 0.791028 -1.333 0.1827
## ns(time, df = 18 * 6)9 -0.807224 0.760182 -1.062 0.2883
## ns(time, df = 18 * 6)10 0.194587 0.662513 0.294 0.7690
## ns(time, df = 18 * 6)11 0.176506 0.642236 0.275 0.7834
## ns(time, df = 18 * 6)12 -0.267999 0.668601 -0.401 0.6885
## ns(time, df = 18 * 6)13 -0.087656 0.681419 -0.129 0.8976
## ns(time, df = 18 * 6)14 -0.457238 0.705543 -0.648 0.5169
## ns(time, df = 18 * 6)15 -0.263993 0.697440 -0.379 0.7050
## ns(time, df = 18 * 6)16 -0.157032 0.685606 -0.229 0.8188
## ns(time, df = 18 * 6)17 -0.417304 0.672843 -0.620 0.5351
## ns(time, df = 18 * 6)18 0.504009 0.633632 0.795 0.4264
## ns(time, df = 18 * 6)19 -0.267402 0.671199 -0.398 0.6903
## ns(time, df = 18 * 6)20 -0.455820 0.709256 -0.643 0.5204
## ns(time, df = 18 * 6)21 -0.265396 0.705696 -0.376 0.7069
## ns(time, df = 18 * 6)22 0.057302 0.730717 0.078 0.9375
## ns(time, df = 18 * 6)23 -1.645644 0.863344 -1.906 0.0566 .
## ns(time, df = 18 * 6)24 -0.544527 0.761062 -0.715 0.4743
## ns(time, df = 18 * 6)25 0.220007 0.670556 0.328 0.7428
## ns(time, df = 18 * 6)26 -0.157441 0.667679 -0.236 0.8136
## ns(time, df = 18 * 6)27 0.080203 0.695706 0.115 0.9082
## ns(time, df = 18 * 6)28 -1.278306 0.785471 -1.627 0.1036
## ns(time, df = 18 * 6)29 -0.157474 0.712714 -0.221 0.8251
## ns(time, df = 18 * 6)30 -0.115019 0.663929 -0.173 0.8625
## ns(time, df = 18 * 6)31 0.180563 0.638391 0.283 0.7773
## ns(time, df = 18 * 6)32 -0.024091 0.647026 -0.037 0.9703
## ns(time, df = 18 * 6)33 -0.188119 0.661754 -0.284 0.7762
## ns(time, df = 18 * 6)34 0.035254 0.665994 0.053 0.9578
## ns(time, df = 18 * 6)35 -0.267380 0.705058 -0.379 0.7045
## ns(time, df = 18 * 6)36 -0.812799 0.753927 -1.078 0.2810
## ns(time, df = 18 * 6)37 -0.406503 0.706426 -0.575 0.5650
## ns(time, df = 18 * 6)38 0.361204 0.656246 0.550 0.5820
## ns(time, df = 18 * 6)39 -0.432124 0.687052 -0.629 0.5294
## ns(time, df = 18 * 6)40 -0.331637 0.689217 -0.481 0.6304
## ns(time, df = 18 * 6)41 0.017725 0.663301 0.027 0.9787
## ns(time, df = 18 * 6)42 -0.149136 0.659785 -0.226 0.8212
## ns(time, df = 18 * 6)43 -0.121294 0.645268 -0.188 0.8509
## ns(time, df = 18 * 6)44 0.447848 0.628394 0.713 0.4760
## ns(time, df = 18 * 6)45 -0.471237 0.668183 -0.705 0.4807
## ns(time, df = 18 * 6)46 0.182126 0.674321 0.270 0.7871
## ns(time, df = 18 * 6)47 -0.838612 0.720676 -1.164 0.2446
## ns(time, df = 18 * 6)48 0.012619 0.678331 0.019 0.9852
## ns(time, df = 18 * 6)49 0.004361 0.668550 0.007 0.9948
## ns(time, df = 18 * 6)50 -0.321352 0.700046 -0.459 0.6462
## ns(time, df = 18 * 6)51 -0.647271 0.713198 -0.908 0.3641
## ns(time, df = 18 * 6)52 0.318767 0.675465 0.472 0.6370
## ns(time, df = 18 * 6)53 -0.734014 0.715151 -1.026 0.3047
## ns(time, df = 18 * 6)54 -0.235229 0.683179 -0.344 0.7306
## ns(time, df = 18 * 6)55 0.206572 0.643051 0.321 0.7480
## ns(time, df = 18 * 6)56 -0.176656 0.640095 -0.276 0.7826
## ns(time, df = 18 * 6)57 0.221681 0.614963 0.360 0.7185
## ns(time, df = 18 * 6)58 0.286771 0.601668 0.477 0.6336
## ns(time, df = 18 * 6)59 0.484128 0.618163 0.783 0.4335
## ns(time, df = 18 * 6)60 -0.756905 0.699266 -1.082 0.2791
## ns(time, df = 18 * 6)61 0.010506 0.717168 0.015 0.9883
## ns(time, df = 18 * 6)62 -1.317108 0.754601 -1.745 0.0809 .
## ns(time, df = 18 * 6)63 0.690797 0.648558 1.065 0.2868
## ns(time, df = 18 * 6)64 -0.371754 0.658226 -0.565 0.5722
## ns(time, df = 18 * 6)65 0.031473 0.648597 0.049 0.9613
## ns(time, df = 18 * 6)66 -0.148119 0.628897 -0.236 0.8138
## ns(time, df = 18 * 6)67 0.700340 0.597594 1.172 0.2412
## ns(time, df = 18 * 6)68 -0.158995 0.628960 -0.253 0.8004
## ns(time, df = 18 * 6)69 -0.013997 0.641944 -0.022 0.9826
## ns(time, df = 18 * 6)70 0.078681 0.647198 0.122 0.9032
## ns(time, df = 18 * 6)71 -0.235618 0.673187 -0.350 0.7263
## ns(time, df = 18 * 6)72 -0.225930 0.691121 -0.327 0.7437
## ns(time, df = 18 * 6)73 -0.613796 0.684555 -0.897 0.3699
## ns(time, df = 18 * 6)74 0.703713 0.628383 1.120 0.2628
## ns(time, df = 18 * 6)75 -0.322899 0.674440 -0.479 0.6321
## ns(time, df = 18 * 6)76 -0.674298 0.717303 -0.940 0.3472
## ns(time, df = 18 * 6)77 0.068421 0.682769 0.100 0.9202
## ns(time, df = 18 * 6)78 -0.481611 0.680283 -0.708 0.4790
## ns(time, df = 18 * 6)79 0.441931 0.654950 0.675 0.4998
## ns(time, df = 18 * 6)80 -0.672825 0.711678 -0.945 0.3445
## ns(time, df = 18 * 6)81 -0.577779 0.698948 -0.827 0.4084
## ns(time, df = 18 * 6)82 0.586410 0.636815 0.921 0.3571
## ns(time, df = 18 * 6)83 -0.354730 0.668265 -0.531 0.5955
## ns(time, df = 18 * 6)84 -0.359643 0.675026 -0.533 0.5942
## ns(time, df = 18 * 6)85 0.160799 0.634232 0.254 0.7999
## ns(time, df = 18 * 6)86 0.126921 0.614199 0.207 0.8363
## ns(time, df = 18 * 6)87 0.383552 0.605362 0.634 0.5263
## ns(time, df = 18 * 6)88 -0.035301 0.624225 -0.057 0.9549
## ns(time, df = 18 * 6)89 0.031665 0.625985 0.051 0.9597
## ns(time, df = 18 * 6)90 0.368123 0.626367 0.588 0.5567
## ns(time, df = 18 * 6)91 -0.573304 0.664518 -0.863 0.3883
## ns(time, df = 18 * 6)92 0.342863 0.635267 0.540 0.5894
## ns(time, df = 18 * 6)93 -0.262387 0.636430 -0.412 0.6801
## ns(time, df = 18 * 6)94 0.466035 0.610290 0.764 0.4451
## ns(time, df = 18 * 6)95 0.116972 0.630201 0.186 0.8528
## ns(time, df = 18 * 6)96 -0.662618 0.653273 -1.014 0.3104
## ns(time, df = 18 * 6)97 1.191958 0.609953 1.954 0.0507 .
## ns(time, df = 18 * 6)98 -1.422223 0.687202 -2.070 0.0385 *
## ns(time, df = 18 * 6)99 1.206425 0.613849 1.965 0.0494 *
## ns(time, df = 18 * 6)100 -0.916125 0.658791 -1.391 0.1643
## ns(time, df = 18 * 6)101 0.941667 0.632774 1.488 0.1367
## ns(time, df = 18 * 6)102 -1.276846 0.713323 -1.790 0.0735 .
## ns(time, df = 18 * 6)103 0.434524 0.648727 0.670 0.5030
## ns(time, df = 18 * 6)104 -0.301056 0.633122 -0.476 0.6344
## ns(time, df = 18 * 6)105 0.558432 0.588996 0.948 0.3431
## ns(time, df = 18 * 6)106 0.317039 0.485545 0.653 0.5138
## ns(time, df = 18 * 6)107 0.240603 1.054643 0.228 0.8195
## ns(time, df = 18 * 6)108 -0.308728 0.475765 -0.649 0.5164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17043.33) family taken to be 1)
##
## Null deviance: 1179.1 on 938 degrees of freedom
## Residual deviance: 1037.8 on 830 degrees of freedom
## AIC: 3405.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17043
## Std. Err.: 128028
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3185.698## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 7), data = week,
## init.theta = 20836.35921, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5190 -0.7841 -0.1129 0.5375 2.6057
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.486238 0.469293 1.036 0.30015
## ns(time, df = 18 * 7)1 -1.588027 0.741935 -2.140 0.03232 *
## ns(time, df = 18 * 7)2 0.970116 0.838987 1.156 0.24756
## ns(time, df = 18 * 7)3 -1.093662 0.785856 -1.392 0.16402
## ns(time, df = 18 * 7)4 1.147347 0.706555 1.624 0.10441
## ns(time, df = 18 * 7)5 0.541891 0.678956 0.798 0.42480
## ns(time, df = 18 * 7)6 0.130561 0.723924 0.180 0.85688
## ns(time, df = 18 * 7)7 0.592039 0.726070 0.815 0.41484
## ns(time, df = 18 * 7)8 -0.375236 0.782072 -0.480 0.63137
## ns(time, df = 18 * 7)9 0.870542 0.799398 1.089 0.27616
## ns(time, df = 18 * 7)10 -2.175056 0.997667 -2.180 0.02925 *
## ns(time, df = 18 * 7)11 0.860855 0.784561 1.097 0.27253
## ns(time, df = 18 * 7)12 -0.171572 0.742920 -0.231 0.81736
## ns(time, df = 18 * 7)13 1.092676 0.701965 1.557 0.11957
## ns(time, df = 18 * 7)14 -0.459542 0.756832 -0.607 0.54372
## ns(time, df = 18 * 7)15 0.772398 0.738271 1.046 0.29546
## ns(time, df = 18 * 7)16 -0.437264 0.791466 -0.552 0.58062
## ns(time, df = 18 * 7)17 0.255596 0.775150 0.330 0.74160
## ns(time, df = 18 * 7)18 -0.020877 0.773608 -0.027 0.97847
## ns(time, df = 18 * 7)19 0.180575 0.760537 0.237 0.81232
## ns(time, df = 18 * 7)20 0.062110 0.745043 0.083 0.93356
## ns(time, df = 18 * 7)21 0.517897 0.709226 0.730 0.46525
## ns(time, df = 18 * 7)22 0.702192 0.717776 0.978 0.32793
## ns(time, df = 18 * 7)23 -0.601707 0.799383 -0.753 0.45162
## ns(time, df = 18 * 7)24 0.406757 0.772572 0.526 0.59854
## ns(time, df = 18 * 7)25 -0.100842 0.787434 -0.128 0.89810
## ns(time, df = 18 * 7)26 0.357505 0.818974 0.437 0.66245
## ns(time, df = 18 * 7)27 -1.154876 0.961257 -1.201 0.22959
## ns(time, df = 18 * 7)28 -0.706324 0.886162 -0.797 0.42542
## ns(time, df = 18 * 7)29 0.861399 0.749028 1.150 0.25013
## ns(time, df = 18 * 7)30 -0.197850 0.748620 -0.264 0.79156
## ns(time, df = 18 * 7)31 0.916635 0.730850 1.254 0.20977
## ns(time, df = 18 * 7)32 -0.530401 0.826468 -0.642 0.52102
## ns(time, df = 18 * 7)33 -0.489275 0.854801 -0.572 0.56706
## ns(time, df = 18 * 7)34 0.256622 0.789428 0.325 0.74513
## ns(time, df = 18 * 7)35 -0.215216 0.756358 -0.285 0.77599
## ns(time, df = 18 * 7)36 1.096100 0.699147 1.568 0.11694
## ns(time, df = 18 * 7)37 -0.350273 0.736383 -0.476 0.63431
## ns(time, df = 18 * 7)38 0.966609 0.709697 1.362 0.17320
## ns(time, df = 18 * 7)39 -0.390179 0.756454 -0.516 0.60599
## ns(time, df = 18 * 7)40 0.698436 0.734226 0.951 0.34148
## ns(time, df = 18 * 7)41 0.001372 0.785723 0.002 0.99861
## ns(time, df = 18 * 7)42 -0.637697 0.852852 -0.748 0.45463
## ns(time, df = 18 * 7)43 0.112413 0.793505 0.142 0.88734
## ns(time, df = 18 * 7)44 0.154971 0.747651 0.207 0.83579
## ns(time, df = 18 * 7)45 0.696233 0.728796 0.955 0.33942
## ns(time, df = 18 * 7)46 -0.420292 0.781550 -0.538 0.59074
## ns(time, df = 18 * 7)47 0.395607 0.754791 0.524 0.60019
## ns(time, df = 18 * 7)48 -0.047712 0.746773 -0.064 0.94906
## ns(time, df = 18 * 7)49 0.754843 0.722513 1.045 0.29614
## ns(time, df = 18 * 7)50 -0.531263 0.749591 -0.709 0.47849
## ns(time, df = 18 * 7)51 1.330813 0.687108 1.937 0.05277 .
## ns(time, df = 18 * 7)52 -0.313355 0.736992 -0.425 0.67070
## ns(time, df = 18 * 7)53 0.595292 0.730388 0.815 0.41505
## ns(time, df = 18 * 7)54 0.055320 0.766040 0.072 0.94243
## ns(time, df = 18 * 7)55 -0.312124 0.798352 -0.391 0.69583
## ns(time, df = 18 * 7)56 0.315590 0.756644 0.417 0.67661
## ns(time, df = 18 * 7)57 0.215089 0.742915 0.290 0.77218
## ns(time, df = 18 * 7)58 0.402926 0.751731 0.536 0.59196
## ns(time, df = 18 * 7)59 -0.380714 0.802078 -0.475 0.63503
## ns(time, df = 18 * 7)60 0.110842 0.773444 0.143 0.88605
## ns(time, df = 18 * 7)61 0.431108 0.752211 0.573 0.56656
## ns(time, df = 18 * 7)62 -0.148550 0.788325 -0.188 0.85053
## ns(time, df = 18 * 7)63 -0.209487 0.780590 -0.268 0.78841
## ns(time, df = 18 * 7)64 0.602032 0.718310 0.838 0.40196
## ns(time, df = 18 * 7)65 0.398930 0.712573 0.560 0.57559
## ns(time, df = 18 * 7)66 0.031145 0.714096 0.044 0.96521
## ns(time, df = 18 * 7)67 0.923345 0.671109 1.376 0.16887
## ns(time, df = 18 * 7)68 0.426046 0.674881 0.631 0.52785
## ns(time, df = 18 * 7)69 0.875800 0.685838 1.277 0.20161
## ns(time, df = 18 * 7)70 -0.146042 0.761851 -0.192 0.84798
## ns(time, df = 18 * 7)71 -0.137520 0.807691 -0.170 0.86480
## ns(time, df = 18 * 7)72 -0.060410 0.814041 -0.074 0.94084
## ns(time, df = 18 * 7)73 -0.410526 0.790567 -0.519 0.60357
## ns(time, df = 18 * 7)74 0.971200 0.710951 1.366 0.17192
## ns(time, df = 18 * 7)75 -0.027913 0.731953 -0.038 0.96958
## ns(time, df = 18 * 7)76 0.358017 0.724921 0.494 0.62140
## ns(time, df = 18 * 7)77 0.196847 0.706933 0.278 0.78067
## ns(time, df = 18 * 7)78 0.833556 0.670000 1.244 0.21346
## ns(time, df = 18 * 7)79 0.687532 0.678890 1.013 0.31119
## ns(time, df = 18 * 7)80 0.007935 0.719288 0.011 0.99120
## ns(time, df = 18 * 7)81 0.556316 0.709871 0.784 0.43322
## ns(time, df = 18 * 7)82 0.315564 0.726806 0.434 0.66416
## ns(time, df = 18 * 7)83 -0.032977 0.752767 -0.044 0.96506
## ns(time, df = 18 * 7)84 0.566616 0.755934 0.750 0.45352
## ns(time, df = 18 * 7)85 -0.926981 0.806143 -1.150 0.25019
## ns(time, df = 18 * 7)86 1.159649 0.702172 1.652 0.09863 .
## ns(time, df = 18 * 7)87 0.167426 0.718776 0.233 0.81581
## ns(time, df = 18 * 7)88 0.357741 0.756371 0.473 0.63623
## ns(time, df = 18 * 7)89 -0.651687 0.811693 -0.803 0.42205
## ns(time, df = 18 * 7)90 0.663076 0.752510 0.881 0.37824
## ns(time, df = 18 * 7)91 -0.282401 0.762788 -0.370 0.71122
## ns(time, df = 18 * 7)92 0.689499 0.725176 0.951 0.34171
## ns(time, df = 18 * 7)93 0.173082 0.757352 0.229 0.81923
## ns(time, df = 18 * 7)94 -0.456494 0.808621 -0.565 0.57239
## ns(time, df = 18 * 7)95 0.197956 0.749113 0.264 0.79158
## ns(time, df = 18 * 7)96 0.732565 0.706674 1.037 0.29990
## ns(time, df = 18 * 7)97 0.222634 0.738529 0.301 0.76307
## ns(time, df = 18 * 7)98 -0.362343 0.771208 -0.470 0.63847
## ns(time, df = 18 * 7)99 0.693245 0.710799 0.975 0.32941
## ns(time, df = 18 * 7)100 0.255518 0.697890 0.366 0.71427
## ns(time, df = 18 * 7)101 0.735029 0.675150 1.089 0.27629
## ns(time, df = 18 * 7)102 0.567751 0.680658 0.834 0.40421
## ns(time, df = 18 * 7)103 0.309847 0.698877 0.443 0.65751
## ns(time, df = 18 * 7)104 0.450801 0.696302 0.647 0.51736
## ns(time, df = 18 * 7)105 0.585494 0.698482 0.838 0.40190
## ns(time, df = 18 * 7)106 0.113550 0.727717 0.156 0.87600
## ns(time, df = 18 * 7)107 0.223522 0.725118 0.308 0.75789
## ns(time, df = 18 * 7)108 0.525250 0.706846 0.743 0.45743
## ns(time, df = 18 * 7)109 0.162720 0.703578 0.231 0.81710
## ns(time, df = 18 * 7)110 0.902023 0.677238 1.332 0.18289
## ns(time, df = 18 * 7)111 0.324071 0.706154 0.459 0.64629
## ns(time, df = 18 * 7)112 -0.085975 0.728118 -0.118 0.90601
## ns(time, df = 18 * 7)113 0.864650 0.685583 1.261 0.20724
## ns(time, df = 18 * 7)114 0.559891 0.709973 0.789 0.43034
## ns(time, df = 18 * 7)115 -0.839379 0.759101 -1.106 0.26883
## ns(time, df = 18 * 7)116 1.930400 0.678015 2.847 0.00441 **
## ns(time, df = 18 * 7)117 -1.311229 0.767319 -1.709 0.08748 .
## ns(time, df = 18 * 7)118 1.953909 0.699130 2.795 0.00519 **
## ns(time, df = 18 * 7)119 -1.601237 0.826551 -1.937 0.05271 .
## ns(time, df = 18 * 7)120 1.189566 0.726541 1.637 0.10157
## ns(time, df = 18 * 7)121 -0.419184 0.737209 -0.569 0.56962
## ns(time, df = 18 * 7)122 0.951735 0.673356 1.413 0.15753
## ns(time, df = 18 * 7)123 0.552849 0.659093 0.839 0.40158
## ns(time, df = 18 * 7)124 0.494977 0.531099 0.932 0.35134
## ns(time, df = 18 * 7)125 1.483167 1.197745 1.238 0.21561
## ns(time, df = 18 * 7)126 -0.664647 0.511619 -1.299 0.19391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(20836.36) family taken to be 1)
##
## Null deviance: 1179.1 on 938 degrees of freedom
## Residual deviance: 1008.8 on 812 degrees of freedom
## AIC: 3412.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 20836
## Std. Err.: 137306
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3156.745## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 8), data = week,
## init.theta = 23590.18306, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.58836 -0.76385 -0.08913 0.56775 2.61981
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.232439 0.530333 0.438 0.6612
## ns(time, df = 18 * 8)1 -1.601161 0.808665 -1.980 0.0477 *
## ns(time, df = 18 * 8)2 1.285216 0.926095 1.388 0.1652
## ns(time, df = 18 * 8)3 -0.701280 0.878883 -0.798 0.4249
## ns(time, df = 18 * 8)4 0.552112 0.834603 0.662 0.5083
## ns(time, df = 18 * 8)5 0.989025 0.740740 1.335 0.1818
## ns(time, df = 18 * 8)6 1.017031 0.754584 1.348 0.1777
## ns(time, df = 18 * 8)7 0.145941 0.791369 0.184 0.8537
## ns(time, df = 18 * 8)8 1.066568 0.789925 1.350 0.1769
## ns(time, df = 18 * 8)9 -0.217759 0.850390 -0.256 0.7979
## ns(time, df = 18 * 8)10 0.937533 0.846624 1.107 0.2681
## ns(time, df = 18 * 8)11 -0.309473 0.971243 -0.319 0.7500
## ns(time, df = 18 * 8)12 -1.314163 1.040625 -1.263 0.2066
## ns(time, df = 18 * 8)13 1.410023 0.827559 1.704 0.0884 .
## ns(time, df = 18 * 8)14 -0.253358 0.816574 -0.310 0.7564
## ns(time, df = 18 * 8)15 1.746895 0.760977 2.296 0.0217 *
## ns(time, df = 18 * 8)16 -0.706514 0.846147 -0.835 0.4037
## ns(time, df = 18 * 8)17 1.492532 0.794133 1.879 0.0602 .
## ns(time, df = 18 * 8)18 -0.540035 0.873509 -0.618 0.5364
## ns(time, df = 18 * 8)19 0.810813 0.840036 0.965 0.3344
## ns(time, df = 18 * 8)20 -0.080921 0.861834 -0.094 0.9252
## ns(time, df = 18 * 8)21 0.617987 0.833259 0.742 0.4583
## ns(time, df = 18 * 8)22 0.200016 0.837032 0.239 0.8111
## ns(time, df = 18 * 8)23 0.464695 0.812699 0.572 0.5675
## ns(time, df = 18 * 8)24 0.589765 0.779685 0.756 0.4494
## ns(time, df = 18 * 8)25 1.158283 0.770624 1.503 0.1328
## ns(time, df = 18 * 8)26 -0.181013 0.852828 -0.212 0.8319
## ns(time, df = 18 * 8)27 0.410271 0.853928 0.480 0.6309
## ns(time, df = 18 * 8)28 0.257994 0.850750 0.303 0.7617
## ns(time, df = 18 * 8)29 0.490546 0.851736 0.576 0.5647
## ns(time, df = 18 * 8)30 0.264737 0.912826 0.290 0.7718
## ns(time, df = 18 * 8)31 -0.749424 1.046227 -0.716 0.4738
## ns(time, df = 18 * 8)32 -0.466205 0.989652 -0.471 0.6376
## ns(time, df = 18 * 8)33 0.616966 0.841254 0.733 0.4633
## ns(time, df = 18 * 8)34 0.807078 0.799636 1.009 0.3128
## ns(time, df = 18 * 8)35 0.056633 0.816968 0.069 0.9447
## ns(time, df = 18 * 8)36 1.412330 0.808398 1.747 0.0806 .
## ns(time, df = 18 * 8)37 -1.138614 0.979198 -1.163 0.2449
## ns(time, df = 18 * 8)38 0.299293 0.913619 0.328 0.7432
## ns(time, df = 18 * 8)39 0.340159 0.867453 0.392 0.6950
## ns(time, df = 18 * 8)40 -0.028955 0.837876 -0.035 0.9724
## ns(time, df = 18 * 8)41 1.314172 0.766613 1.714 0.0865 .
## ns(time, df = 18 * 8)42 0.142075 0.795927 0.179 0.8583
## ns(time, df = 18 * 8)43 0.822177 0.781002 1.053 0.2925
## ns(time, df = 18 * 8)44 0.582909 0.795293 0.733 0.4636
## ns(time, df = 18 * 8)45 0.199063 0.821984 0.242 0.8086
## ns(time, df = 18 * 8)46 0.722347 0.805804 0.896 0.3700
## ns(time, df = 18 * 8)47 0.639202 0.851466 0.751 0.4528
## ns(time, df = 18 * 8)48 -1.117356 0.980712 -1.139 0.2546
## ns(time, df = 18 * 8)49 1.051156 0.858698 1.224 0.2209
## ns(time, df = 18 * 8)50 -0.411366 0.863790 -0.476 0.6339
## ns(time, df = 18 * 8)51 1.267623 0.785605 1.614 0.1066
## ns(time, df = 18 * 8)52 0.245111 0.823208 0.298 0.7659
## ns(time, df = 18 * 8)53 0.063858 0.853799 0.075 0.9404
## ns(time, df = 18 * 8)54 0.656676 0.822709 0.798 0.4248
## ns(time, df = 18 * 8)55 0.122040 0.820029 0.149 0.8817
## ns(time, df = 18 * 8)56 1.103269 0.787270 1.401 0.1611
## ns(time, df = 18 * 8)57 -0.214525 0.828049 -0.259 0.7956
## ns(time, df = 18 * 8)58 1.011506 0.766253 1.320 0.1868
## ns(time, df = 18 * 8)59 0.925045 0.765633 1.208 0.2270
## ns(time, df = 18 * 8)60 0.007025 0.817515 0.009 0.9931
## ns(time, df = 18 * 8)61 0.923839 0.799372 1.156 0.2478
## ns(time, df = 18 * 8)62 0.192435 0.847111 0.227 0.8203
## ns(time, df = 18 * 8)63 -0.077683 0.876919 -0.089 0.9294
## ns(time, df = 18 * 8)64 0.621858 0.828826 0.750 0.4531
## ns(time, df = 18 * 8)65 0.306873 0.817100 0.376 0.7072
## ns(time, df = 18 * 8)66 0.856310 0.805286 1.063 0.2876
## ns(time, df = 18 * 8)67 0.010545 0.860532 0.012 0.9902
## ns(time, df = 18 * 8)68 0.165681 0.866275 0.191 0.8483
## ns(time, df = 18 * 8)69 0.450797 0.835662 0.539 0.5896
## ns(time, df = 18 * 8)70 0.461293 0.825791 0.559 0.5764
## ns(time, df = 18 * 8)71 0.589173 0.851518 0.692 0.4890
## ns(time, df = 18 * 8)72 -0.887129 0.906729 -0.978 0.3279
## ns(time, df = 18 * 8)73 1.697530 0.778613 2.180 0.0292 *
## ns(time, df = 18 * 8)74 -0.355942 0.813635 -0.437 0.6618
## ns(time, df = 18 * 8)75 1.457287 0.762046 1.912 0.0558 .
## ns(time, df = 18 * 8)76 -0.280273 0.792503 -0.354 0.7236
## ns(time, df = 18 * 8)77 1.682545 0.724097 2.324 0.0201 *
## ns(time, df = 18 * 8)78 0.352471 0.748658 0.471 0.6378
## ns(time, df = 18 * 8)79 1.207859 0.749331 1.612 0.1070
## ns(time, df = 18 * 8)80 0.393787 0.820574 0.480 0.6313
## ns(time, df = 18 * 8)81 -0.548005 0.907499 -0.604 0.5459
## ns(time, df = 18 * 8)82 1.238779 0.864304 1.433 0.1518
## ns(time, df = 18 * 8)83 -1.589114 0.981091 -1.620 0.1053
## ns(time, df = 18 * 8)84 1.692492 0.794720 2.130 0.0332 *
## ns(time, df = 18 * 8)85 -0.084164 0.808132 -0.104 0.9171
## ns(time, df = 18 * 8)86 1.089324 0.779990 1.397 0.1625
## ns(time, df = 18 * 8)87 0.100306 0.809684 0.124 0.9014
## ns(time, df = 18 * 8)88 0.641387 0.777452 0.825 0.4094
## ns(time, df = 18 * 8)89 0.903743 0.741307 1.219 0.2228
## ns(time, df = 18 * 8)90 1.030094 0.734270 1.403 0.1607
## ns(time, df = 18 * 8)91 0.690464 0.765797 0.902 0.3673
## ns(time, df = 18 * 8)92 0.180521 0.797408 0.226 0.8209
## ns(time, df = 18 * 8)93 1.075634 0.770786 1.396 0.1629
## ns(time, df = 18 * 8)94 0.285169 0.808861 0.353 0.7244
## ns(time, df = 18 * 8)95 0.319628 0.821815 0.389 0.6973
## ns(time, df = 18 * 8)96 0.857998 0.821632 1.044 0.2964
## ns(time, df = 18 * 8)97 -0.565520 0.893065 -0.633 0.5266
## ns(time, df = 18 * 8)98 0.745140 0.797496 0.934 0.3501
## ns(time, df = 18 * 8)99 1.080701 0.761677 1.419 0.1559
## ns(time, df = 18 * 8)100 0.276002 0.804597 0.343 0.7316
## ns(time, df = 18 * 8)101 0.645545 0.837287 0.771 0.4407
## ns(time, df = 18 * 8)102 -0.563352 0.898868 -0.627 0.5308
## ns(time, df = 18 * 8)103 1.059269 0.818694 1.294 0.1957
## ns(time, df = 18 * 8)104 -0.065442 0.837308 -0.078 0.9377
## ns(time, df = 18 * 8)105 0.776549 0.797936 0.973 0.3305
## ns(time, df = 18 * 8)106 0.663453 0.804335 0.825 0.4095
## ns(time, df = 18 * 8)107 0.138997 0.863471 0.161 0.8721
## ns(time, df = 18 * 8)108 -0.201035 0.879668 -0.229 0.8192
## ns(time, df = 18 * 8)109 0.826488 0.796413 1.038 0.2994
## ns(time, df = 18 * 8)110 0.688789 0.775924 0.888 0.3747
## ns(time, df = 18 * 8)111 0.875657 0.800962 1.093 0.2743
## ns(time, df = 18 * 8)112 -0.745649 0.881951 -0.845 0.3979
## ns(time, df = 18 * 8)113 1.384081 0.777919 1.779 0.0752 .
## ns(time, df = 18 * 8)114 0.095373 0.785743 0.121 0.9034
## ns(time, df = 18 * 8)115 1.126725 0.743740 1.515 0.1298
## ns(time, df = 18 * 8)116 0.727151 0.743303 0.978 0.3279
## ns(time, df = 18 * 8)117 0.902773 0.750274 1.203 0.2289
## ns(time, df = 18 * 8)118 0.405406 0.770625 0.526 0.5988
## ns(time, df = 18 * 8)119 0.992971 0.758087 1.310 0.1903
## ns(time, df = 18 * 8)120 0.355992 0.776025 0.459 0.6464
## ns(time, df = 18 * 8)121 1.120684 0.773026 1.450 0.1471
## ns(time, df = 18 * 8)122 -0.308155 0.830439 -0.371 0.7106
## ns(time, df = 18 * 8)123 1.146616 0.772168 1.485 0.1376
## ns(time, df = 18 * 8)124 0.377961 0.781524 0.484 0.6287
## ns(time, df = 18 * 8)125 0.637823 0.763554 0.835 0.4035
## ns(time, df = 18 * 8)126 1.131075 0.741395 1.526 0.1271
## ns(time, df = 18 * 8)127 0.552585 0.775938 0.712 0.4764
## ns(time, df = 18 * 8)128 0.219696 0.802176 0.274 0.7842
## ns(time, df = 18 * 8)129 0.817106 0.760308 1.075 0.2825
## ns(time, df = 18 * 8)130 1.195228 0.754887 1.583 0.1133
## ns(time, df = 18 * 8)131 -0.191526 0.831261 -0.230 0.8178
## ns(time, df = 18 * 8)132 0.479851 0.782283 0.613 0.5396
## ns(time, df = 18 * 8)133 1.742573 0.748043 2.330 0.0198 *
## ns(time, df = 18 * 8)134 -1.116304 0.852455 -1.310 0.1904
## ns(time, df = 18 * 8)135 2.319145 0.764070 3.035 0.0024 **
## ns(time, df = 18 * 8)136 -1.270215 0.902768 -1.407 0.1594
## ns(time, df = 18 * 8)137 1.040838 0.815586 1.276 0.2019
## ns(time, df = 18 * 8)138 0.285771 0.801819 0.356 0.7215
## ns(time, df = 18 * 8)139 0.668573 0.769315 0.869 0.3848
## ns(time, df = 18 * 8)140 0.888939 0.733666 1.212 0.2256
## ns(time, df = 18 * 8)141 1.115140 0.715506 1.559 0.1191
## ns(time, df = 18 * 8)142 0.292513 0.582171 0.502 0.6153
## ns(time, df = 18 * 8)143 2.252180 1.328502 1.695 0.0900 .
## ns(time, df = 18 * 8)144 -0.666020 0.527051 -1.264 0.2063
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(23590.18) family taken to be 1)
##
## Null deviance: 1179.09 on 938 degrees of freedom
## Residual deviance: 983.67 on 794 degrees of freedom
## AIC: 3423.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 23590
## Std. Err.: 144004
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3131.554## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 9), data = week,
## init.theta = 26527.14967, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.00478 -0.75199 -0.09029 0.54830 2.37999
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.2442304 0.5566023 0.439 0.660815
## ns(time, df = 18 * 9)1 -0.6654526 0.7915974 -0.841 0.400547
## ns(time, df = 18 * 9)2 -0.0959116 1.0291517 -0.093 0.925749
## ns(time, df = 18 * 9)3 0.8440672 0.8769900 0.962 0.335819
## ns(time, df = 18 * 9)4 -0.9478481 0.9951252 -0.952 0.340848
## ns(time, df = 18 * 9)5 1.2287209 0.8104648 1.516 0.129502
## ns(time, df = 18 * 9)6 0.6507819 0.7944560 0.819 0.412698
## ns(time, df = 18 * 9)7 1.1934942 0.7844544 1.521 0.128151
## ns(time, df = 18 * 9)8 -0.0545333 0.8463633 -0.064 0.948626
## ns(time, df = 18 * 9)9 1.1952241 0.8254770 1.448 0.147640
## ns(time, df = 18 * 9)10 -0.2583109 0.8966022 -0.288 0.773270
## ns(time, df = 18 * 9)11 0.8012148 0.8791706 0.911 0.362121
## ns(time, df = 18 * 9)12 0.2393744 0.9566656 0.250 0.802419
## ns(time, df = 18 * 9)13 -0.9680410 1.1267446 -0.859 0.390259
## ns(time, df = 18 * 9)14 -0.4014133 1.0049373 -0.399 0.689568
## ns(time, df = 18 * 9)15 1.2554599 0.8556150 1.467 0.142289
## ns(time, df = 18 * 9)16 -0.3183519 0.8599276 -0.370 0.711228
## ns(time, df = 18 * 9)17 1.8986600 0.7969367 2.382 0.017198 *
## ns(time, df = 18 * 9)18 -0.8875649 0.9041817 -0.982 0.326286
## ns(time, df = 18 * 9)19 1.4555598 0.8352700 1.743 0.081400 .
## ns(time, df = 18 * 9)20 -0.2514360 0.9003095 -0.279 0.780032
## ns(time, df = 18 * 9)21 0.5221704 0.8954905 0.583 0.559819
## ns(time, df = 18 * 9)22 0.1523230 0.9063723 0.168 0.866538
## ns(time, df = 18 * 9)23 0.2947041 0.8965716 0.329 0.742382
## ns(time, df = 18 * 9)24 0.4366813 0.8805198 0.496 0.619940
## ns(time, df = 18 * 9)25 0.3263396 0.8820015 0.370 0.711383
## ns(time, df = 18 * 9)26 0.2348166 0.8656391 0.271 0.786188
## ns(time, df = 18 * 9)27 0.8552265 0.8156921 1.048 0.294423
## ns(time, df = 18 * 9)28 0.7192944 0.8135138 0.884 0.376598
## ns(time, df = 18 * 9)29 0.6946066 0.8527758 0.815 0.415345
## ns(time, df = 18 * 9)30 -0.4331074 0.9364070 -0.463 0.643708
## ns(time, df = 18 * 9)31 0.8324267 0.8866557 0.939 0.347814
## ns(time, df = 18 * 9)32 -0.1643136 0.9123255 -0.180 0.857071
## ns(time, df = 18 * 9)33 0.9161347 0.8908142 1.028 0.303750
## ns(time, df = 18 * 9)34 -0.2905841 0.9994181 -0.291 0.771240
## ns(time, df = 18 * 9)35 -0.3771443 1.0973234 -0.344 0.731076
## ns(time, df = 18 * 9)36 -0.7349884 1.0841589 -0.678 0.497813
## ns(time, df = 18 * 9)37 0.4625211 0.9116543 0.507 0.611914
## ns(time, df = 18 * 9)38 0.8060781 0.8435584 0.956 0.339290
## ns(time, df = 18 * 9)39 0.3189093 0.8538480 0.373 0.708779
## ns(time, df = 18 * 9)40 0.5991080 0.8452124 0.709 0.478433
## ns(time, df = 18 * 9)41 0.9312405 0.8847852 1.053 0.292568
## ns(time, df = 18 * 9)42 -1.3665419 1.0848547 -1.260 0.207794
## ns(time, df = 18 * 9)43 0.5968805 0.9541840 0.626 0.531617
## ns(time, df = 18 * 9)44 0.0930436 0.9235751 0.101 0.919755
## ns(time, df = 18 * 9)45 0.2310328 0.8850782 0.261 0.794069
## ns(time, df = 18 * 9)46 0.7287817 0.8225433 0.886 0.375612
## ns(time, df = 18 * 9)47 1.0040915 0.8100952 1.239 0.215170
## ns(time, df = 18 * 9)48 -0.0950190 0.8561019 -0.111 0.911624
## ns(time, df = 18 * 9)49 1.1975921 0.8151905 1.469 0.141807
## ns(time, df = 18 * 9)50 0.1304987 0.8598176 0.152 0.879364
## ns(time, df = 18 * 9)51 0.4603479 0.8637637 0.533 0.594064
## ns(time, df = 18 * 9)52 0.4862069 0.8541298 0.569 0.569192
## ns(time, df = 18 * 9)53 1.0439332 0.8961598 1.165 0.244061
## ns(time, df = 18 * 9)54 -2.0264586 1.1228850 -1.805 0.071123 .
## ns(time, df = 18 * 9)55 1.7624645 0.9073112 1.943 0.052075 .
## ns(time, df = 18 * 9)56 -1.2170197 0.9763206 -1.247 0.212567
## ns(time, df = 18 * 9)57 1.4517735 0.8341088 1.741 0.081770 .
## ns(time, df = 18 * 9)58 0.2147623 0.8511913 0.252 0.800803
## ns(time, df = 18 * 9)59 0.6149629 0.8724303 0.705 0.480882
## ns(time, df = 18 * 9)60 -0.1817771 0.9100735 -0.200 0.841685
## ns(time, df = 18 * 9)61 0.8361760 0.8627258 0.969 0.332433
## ns(time, df = 18 * 9)62 -0.0258693 0.8712791 -0.030 0.976313
## ns(time, df = 18 * 9)63 1.1231491 0.8277782 1.357 0.174837
## ns(time, df = 18 * 9)64 0.0001954 0.8715331 0.000 0.999821
## ns(time, df = 18 * 9)65 0.4233622 0.8348971 0.507 0.612097
## ns(time, df = 18 * 9)66 1.3199121 0.7903154 1.670 0.094898 .
## ns(time, df = 18 * 9)67 0.1505957 0.8435665 0.179 0.858313
## ns(time, df = 18 * 9)68 0.5448140 0.8493612 0.641 0.521237
## ns(time, df = 18 * 9)69 0.5912128 0.8536078 0.693 0.488558
## ns(time, df = 18 * 9)70 0.3221114 0.8962071 0.359 0.719284
## ns(time, df = 18 * 9)71 -0.1866035 0.9354169 -0.199 0.841882
## ns(time, df = 18 * 9)72 0.5869093 0.8794462 0.667 0.504541
## ns(time, df = 18 * 9)73 0.3635356 0.8626488 0.421 0.673450
## ns(time, df = 18 * 9)74 0.6290428 0.8487741 0.741 0.458621
## ns(time, df = 18 * 9)75 0.5244634 0.8729985 0.601 0.547999
## ns(time, df = 18 * 9)76 -0.1101616 0.9319180 -0.118 0.905902
## ns(time, df = 18 * 9)77 0.2430488 0.9070600 0.268 0.788736
## ns(time, df = 18 * 9)78 0.5856194 0.8728519 0.671 0.502267
## ns(time, df = 18 * 9)79 0.2121949 0.8781845 0.242 0.809068
## ns(time, df = 18 * 9)80 0.9752709 0.8945334 1.090 0.275600
## ns(time, df = 18 * 9)81 -1.6141990 1.0237707 -1.577 0.114860
## ns(time, df = 18 * 9)82 2.0222856 0.8258272 2.449 0.014333 *
## ns(time, df = 18 * 9)83 -0.5435907 0.8709539 -0.624 0.532541
## ns(time, df = 18 * 9)84 1.3725900 0.8075950 1.700 0.089206 .
## ns(time, df = 18 * 9)85 0.1172678 0.8360007 0.140 0.888445
## ns(time, df = 18 * 9)86 0.6662712 0.7992369 0.834 0.404487
## ns(time, df = 18 * 9)87 1.3209132 0.7629285 1.731 0.083385 .
## ns(time, df = 18 * 9)88 0.4046742 0.7887996 0.513 0.607934
## ns(time, df = 18 * 9)89 1.2921347 0.7854341 1.645 0.099945 .
## ns(time, df = 18 * 9)90 0.1556451 0.8637574 0.180 0.856999
## ns(time, df = 18 * 9)91 0.1875399 0.9271637 0.202 0.839704
## ns(time, df = 18 * 9)92 -0.1604091 0.9475563 -0.169 0.865571
## ns(time, df = 18 * 9)93 0.9518553 0.9359771 1.017 0.309170
## ns(time, df = 18 * 9)94 -1.9549935 1.0415207 -1.877 0.060510 .
## ns(time, df = 18 * 9)95 2.7517723 0.8155633 3.374 0.000741 ***
## ns(time, df = 18 * 9)96 -1.4916759 0.9257852 -1.611 0.107124
## ns(time, df = 18 * 9)97 2.0110770 0.8101193 2.482 0.013048 *
## ns(time, df = 18 * 9)98 -0.5520267 0.8826780 -0.625 0.531709
## ns(time, df = 18 * 9)99 0.8732786 0.8242656 1.059 0.289389
## ns(time, df = 18 * 9)100 0.6697110 0.7895447 0.848 0.396313
## ns(time, df = 18 * 9)101 1.1689000 0.7677167 1.523 0.127867
## ns(time, df = 18 * 9)102 0.6360461 0.7929890 0.802 0.422503
## ns(time, df = 18 * 9)103 0.7483662 0.8208294 0.912 0.361916
## ns(time, df = 18 * 9)104 -0.0348202 0.8505231 -0.041 0.967344
## ns(time, df = 18 * 9)105 1.4300851 0.8053200 1.776 0.075766 .
## ns(time, df = 18 * 9)106 -0.1702092 0.8774046 -0.194 0.846182
## ns(time, df = 18 * 9)107 0.5319590 0.8637793 0.616 0.537993
## ns(time, df = 18 * 9)108 0.7438736 0.8639773 0.861 0.389245
## ns(time, df = 18 * 9)109 -0.2644606 0.9384641 -0.282 0.778096
## ns(time, df = 18 * 9)110 0.1326311 0.8869853 0.150 0.881135
## ns(time, df = 18 * 9)111 1.1147838 0.8025508 1.389 0.164817
## ns(time, df = 18 * 9)112 0.7796789 0.8143564 0.957 0.338357
## ns(time, df = 18 * 9)113 0.1019054 0.8720684 0.117 0.906975
## ns(time, df = 18 * 9)114 0.7642698 0.8897733 0.859 0.390369
## ns(time, df = 18 * 9)115 -0.7926951 0.9679305 -0.819 0.412810
## ns(time, df = 18 * 9)116 1.1779423 0.8607283 1.369 0.171143
## ns(time, df = 18 * 9)117 -0.0441898 0.8855611 -0.050 0.960202
## ns(time, df = 18 * 9)118 0.5168096 0.8532990 0.606 0.544740
## ns(time, df = 18 * 9)119 0.8805189 0.8326812 1.057 0.290306
## ns(time, df = 18 * 9)120 0.2107525 0.8880839 0.237 0.812415
## ns(time, df = 18 * 9)121 0.0547566 0.9334730 0.059 0.953224
## ns(time, df = 18 * 9)122 -0.0313863 0.9067639 -0.035 0.972388
## ns(time, df = 18 * 9)123 0.9823780 0.8260092 1.189 0.234319
## ns(time, df = 18 * 9)124 0.4534979 0.8208911 0.552 0.580643
## ns(time, df = 18 * 9)125 1.2661748 0.8411329 1.505 0.132242
## ns(time, df = 18 * 9)126 -1.5323350 0.9950538 -1.540 0.123572
## ns(time, df = 18 * 9)127 1.8624652 0.8224393 2.265 0.023540 *
## ns(time, df = 18 * 9)128 -0.3606978 0.8536908 -0.423 0.672649
## ns(time, df = 18 * 9)129 1.3109971 0.7889486 1.662 0.096573 .
## ns(time, df = 18 * 9)130 0.4514083 0.7911874 0.571 0.568308
## ns(time, df = 18 * 9)131 1.1889495 0.7752383 1.534 0.125114
## ns(time, df = 18 * 9)132 0.4760909 0.8072319 0.590 0.555337
## ns(time, df = 18 * 9)133 0.5416026 0.8097714 0.669 0.503601
## ns(time, df = 18 * 9)134 1.1346529 0.7975093 1.423 0.154810
## ns(time, df = 18 * 9)135 -0.1529164 0.8362902 -0.183 0.854915
## ns(time, df = 18 * 9)136 1.8309214 0.7988357 2.292 0.021906 *
## ns(time, df = 18 * 9)137 -1.1142054 0.9233188 -1.207 0.227532
## ns(time, df = 18 * 9)138 1.5443264 0.8169038 1.890 0.058696 .
## ns(time, df = 18 * 9)139 -0.0252911 0.8400177 -0.030 0.975981
## ns(time, df = 18 * 9)140 1.0632398 0.8058351 1.319 0.187027
## ns(time, df = 18 * 9)141 0.2816580 0.8090579 0.348 0.727742
## ns(time, df = 18 * 9)142 1.3544183 0.7763939 1.744 0.081072 .
## ns(time, df = 18 * 9)143 0.3971711 0.8237045 0.482 0.629680
## ns(time, df = 18 * 9)144 0.2485938 0.8522256 0.292 0.770516
## ns(time, df = 18 * 9)145 0.6867715 0.8103211 0.848 0.396700
## ns(time, df = 18 * 9)146 1.0982549 0.7869816 1.396 0.162857
## ns(time, df = 18 * 9)147 0.5718362 0.8353503 0.685 0.493630
## ns(time, df = 18 * 9)148 -0.3304494 0.8891133 -0.372 0.710145
## ns(time, df = 18 * 9)149 1.1943916 0.7940783 1.504 0.132550
## ns(time, df = 18 * 9)150 1.1404570 0.8031827 1.420 0.155630
## ns(time, df = 18 * 9)151 -0.8805716 0.9002917 -0.978 0.328027
## ns(time, df = 18 * 9)152 2.0989352 0.8038414 2.611 0.009024 **
## ns(time, df = 18 * 9)153 -0.6826506 0.9302006 -0.734 0.463025
## ns(time, df = 18 * 9)154 -0.0313917 0.9123902 -0.034 0.972553
## ns(time, df = 18 * 9)155 1.3515400 0.8295648 1.629 0.103267
## ns(time, df = 18 * 9)156 -0.5528514 0.8726880 -0.634 0.526405
## ns(time, df = 18 * 9)157 1.6418414 0.7753219 2.118 0.034207 *
## ns(time, df = 18 * 9)158 0.2134523 0.7830372 0.273 0.785164
## ns(time, df = 18 * 9)159 1.6193401 0.7442411 2.176 0.029568 *
## ns(time, df = 18 * 9)160 -0.0983570 0.6309164 -0.156 0.876115
## ns(time, df = 18 * 9)161 2.0257809 1.3815813 1.466 0.142573
## ns(time, df = 18 * 9)162 -0.3955311 0.5345344 -0.740 0.459328
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(26527.15) family taken to be 1)
##
## Null deviance: 1179.10 on 938 degrees of freedom
## Residual deviance: 951.76 on 776 degrees of freedom
## AIC: 3427.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 26527
## Std. Err.: 145815
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3099.643## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 10), data = week,
## init.theta = 27991.48742, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.91056 -0.74039 -0.08845 0.52603 2.34097
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.328177 0.570090 0.576 0.56485
## ns(time, df = 18 * 10)1 0.272710 0.774593 0.352 0.72479
## ns(time, df = 18 * 10)2 -1.567626 1.187767 -1.320 0.18690
## ns(time, df = 18 * 10)3 1.512668 0.904639 1.672 0.09450 .
## ns(time, df = 18 * 10)4 -0.992449 1.070074 -0.927 0.35369
## ns(time, df = 18 * 10)5 0.047675 0.936392 0.051 0.95939
## ns(time, df = 18 * 10)6 1.084460 0.834033 1.300 0.19351
## ns(time, df = 18 * 10)7 0.572883 0.814315 0.704 0.48173
## ns(time, df = 18 * 10)8 1.053133 0.820679 1.283 0.19941
## ns(time, df = 18 * 10)9 -0.051862 0.877024 -0.059 0.95285
## ns(time, df = 18 * 10)10 0.925157 0.862069 1.073 0.28319
## ns(time, df = 18 * 10)11 0.008382 0.910878 0.009 0.99266
## ns(time, df = 18 * 10)12 0.433568 0.918831 0.472 0.63702
## ns(time, df = 18 * 10)13 0.231104 0.955639 0.242 0.80891
## ns(time, df = 18 * 10)14 0.123373 1.065874 0.116 0.90785
## ns(time, df = 18 * 10)15 -1.845005 1.273205 -1.449 0.14731
## ns(time, df = 18 * 10)16 0.560543 0.973809 0.576 0.56487
## ns(time, df = 18 * 10)17 0.719146 0.889132 0.809 0.41862
## ns(time, df = 18 * 10)18 -0.206866 0.887275 -0.233 0.81565
## ns(time, df = 18 * 10)19 1.753487 0.824609 2.126 0.03347 *
## ns(time, df = 18 * 10)20 -0.796009 0.940264 -0.847 0.39723
## ns(time, df = 18 * 10)21 0.876414 0.879657 0.996 0.31910
## ns(time, df = 18 * 10)22 0.522330 0.897410 0.582 0.56054
## ns(time, df = 18 * 10)23 -0.430867 0.973537 -0.443 0.65807
## ns(time, df = 18 * 10)24 0.709871 0.923011 0.769 0.44184
## ns(time, df = 18 * 10)25 -0.134075 0.953789 -0.141 0.88821
## ns(time, df = 18 * 10)26 0.312515 0.931105 0.336 0.73714
## ns(time, df = 18 * 10)27 0.326391 0.914759 0.357 0.72124
## ns(time, df = 18 * 10)28 0.341566 0.919558 0.371 0.71031
## ns(time, df = 18 * 10)29 -0.148800 0.914625 -0.163 0.87076
## ns(time, df = 18 * 10)30 1.203262 0.839157 1.434 0.15160
## ns(time, df = 18 * 10)31 0.001167 0.862145 0.001 0.99892
## ns(time, df = 18 * 10)32 1.422742 0.850694 1.672 0.09444 .
## ns(time, df = 18 * 10)33 -0.926597 0.999836 -0.927 0.35406
## ns(time, df = 18 * 10)34 0.489351 0.941039 0.520 0.60306
## ns(time, df = 18 * 10)35 0.413466 0.928593 0.445 0.65613
## ns(time, df = 18 * 10)36 -0.300243 0.958737 -0.313 0.75416
## ns(time, df = 18 * 10)37 1.117747 0.925215 1.208 0.22701
## ns(time, df = 18 * 10)38 -0.920292 1.092789 -0.842 0.39970
## ns(time, df = 18 * 10)39 -0.067065 1.138210 -0.059 0.95301
## ns(time, df = 18 * 10)40 -1.079589 1.171461 -0.922 0.35675
## ns(time, df = 18 * 10)41 0.319869 0.976926 0.327 0.74335
## ns(time, df = 18 * 10)42 0.542887 0.887986 0.611 0.54096
## ns(time, df = 18 * 10)43 0.613417 0.873939 0.702 0.48274
## ns(time, df = 18 * 10)44 0.062842 0.892488 0.070 0.94387
## ns(time, df = 18 * 10)45 1.030521 0.872070 1.182 0.23733
## ns(time, df = 18 * 10)46 -0.049246 0.985800 -0.050 0.96016
## ns(time, df = 18 * 10)47 -1.010258 1.122700 -0.900 0.36820
## ns(time, df = 18 * 10)48 0.457300 0.991422 0.461 0.64461
## ns(time, df = 18 * 10)49 -0.010812 0.963552 -0.011 0.99105
## ns(time, df = 18 * 10)50 0.278779 0.926800 0.301 0.76357
## ns(time, df = 18 * 10)51 0.208357 0.874364 0.238 0.81165
## ns(time, df = 18 * 10)52 1.435340 0.827038 1.736 0.08265 .
## ns(time, df = 18 * 10)53 -0.567000 0.909509 -0.623 0.53301
## ns(time, df = 18 * 10)54 1.204806 0.845515 1.425 0.15418
## ns(time, df = 18 * 10)55 0.140214 0.877840 0.160 0.87310
## ns(time, df = 18 * 10)56 0.546845 0.887344 0.616 0.53772
## ns(time, df = 18 * 10)57 0.123678 0.907049 0.136 0.89154
## ns(time, df = 18 * 10)58 0.516711 0.886165 0.583 0.55984
## ns(time, df = 18 * 10)59 1.021955 0.937228 1.090 0.27554
## ns(time, df = 18 * 10)60 -2.427143 1.234881 -1.965 0.04936 *
## ns(time, df = 18 * 10)61 1.649351 0.957725 1.722 0.08504 .
## ns(time, df = 18 * 10)62 -0.997708 1.018619 -0.979 0.32735
## ns(time, df = 18 * 10)63 0.686467 0.903782 0.760 0.44752
## ns(time, df = 18 * 10)64 0.610324 0.864364 0.706 0.48013
## ns(time, df = 18 * 10)65 0.511046 0.885056 0.577 0.56366
## ns(time, df = 18 * 10)66 0.042854 0.937961 0.046 0.96356
## ns(time, df = 18 * 10)67 0.054872 0.936789 0.059 0.95329
## ns(time, df = 18 * 10)68 0.652577 0.899113 0.726 0.46796
## ns(time, df = 18 * 10)69 -0.112425 0.910007 -0.124 0.90168
## ns(time, df = 18 * 10)70 1.035580 0.858759 1.206 0.22786
## ns(time, df = 18 * 10)71 0.038789 0.902406 0.043 0.96571
## ns(time, df = 18 * 10)72 0.223378 0.889506 0.251 0.80172
## ns(time, df = 18 * 10)73 0.815314 0.830364 0.982 0.32616
## ns(time, df = 18 * 10)74 1.009421 0.832338 1.213 0.22522
## ns(time, df = 18 * 10)75 -0.342665 0.912080 -0.376 0.70714
## ns(time, df = 18 * 10)76 0.962918 0.869827 1.107 0.26828
## ns(time, df = 18 * 10)77 0.073930 0.907454 0.081 0.93507
## ns(time, df = 18 * 10)78 0.489452 0.931366 0.526 0.59922
## ns(time, df = 18 * 10)79 -0.424919 0.988502 -0.430 0.66730
## ns(time, df = 18 * 10)80 0.478794 0.921648 0.519 0.60341
## ns(time, df = 18 * 10)81 0.380801 0.897356 0.424 0.67130
## ns(time, df = 18 * 10)82 0.319501 0.888986 0.359 0.71930
## ns(time, df = 18 * 10)83 0.754130 0.885699 0.851 0.39452
## ns(time, df = 18 * 10)84 -0.217452 0.958402 -0.227 0.82051
## ns(time, df = 18 * 10)85 0.270341 0.956684 0.283 0.77750
## ns(time, df = 18 * 10)86 -0.072185 0.946614 -0.076 0.93922
## ns(time, df = 18 * 10)87 0.737736 0.898627 0.821 0.41167
## ns(time, df = 18 * 10)88 -0.069319 0.920806 -0.075 0.93999
## ns(time, df = 18 * 10)89 1.097032 0.930749 1.179 0.23854
## ns(time, df = 18 * 10)90 -1.907408 1.115767 -1.710 0.08736 .
## ns(time, df = 18 * 10)91 1.574354 0.872993 1.803 0.07133 .
## ns(time, df = 18 * 10)92 0.163170 0.879469 0.186 0.85281
## ns(time, df = 18 * 10)93 0.210025 0.875579 0.240 0.81043
## ns(time, df = 18 * 10)94 1.206228 0.836396 1.442 0.14925
## ns(time, df = 18 * 10)95 -0.399162 0.889747 -0.449 0.65370
## ns(time, df = 18 * 10)96 1.216464 0.804281 1.512 0.13041
## ns(time, df = 18 * 10)97 0.861732 0.797726 1.080 0.28004
## ns(time, df = 18 * 10)98 0.451094 0.817538 0.552 0.58111
## ns(time, df = 18 * 10)99 1.217072 0.812834 1.497 0.13431
## ns(time, df = 18 * 10)100 0.001087 0.895458 0.001 0.99903
## ns(time, df = 18 * 10)101 0.433840 0.948731 0.457 0.64747
## ns(time, df = 18 * 10)102 -0.698838 1.021318 -0.684 0.49382
## ns(time, df = 18 * 10)103 0.990910 0.955846 1.037 0.29988
## ns(time, df = 18 * 10)104 -0.853298 1.077933 -0.792 0.42859
## ns(time, df = 18 * 10)105 -0.485285 0.960397 -0.505 0.61335
## ns(time, df = 18 * 10)106 2.400622 0.838642 2.863 0.00420 **
## ns(time, df = 18 * 10)107 -2.166108 1.026625 -2.110 0.03486 *
## ns(time, df = 18 * 10)108 2.418014 0.839737 2.879 0.00398 **
## ns(time, df = 18 * 10)109 -0.988585 0.945211 -1.046 0.29561
## ns(time, df = 18 * 10)110 0.737629 0.869904 0.848 0.39647
## ns(time, df = 18 * 10)111 0.721191 0.821942 0.877 0.38026
## ns(time, df = 18 * 10)112 0.762757 0.803299 0.950 0.34235
## ns(time, df = 18 * 10)113 1.064630 0.801760 1.328 0.18422
## ns(time, df = 18 * 10)114 0.225586 0.850452 0.265 0.79081
## ns(time, df = 18 * 10)115 0.794130 0.859448 0.924 0.35549
## ns(time, df = 18 * 10)116 -0.232794 0.887265 -0.262 0.79303
## ns(time, df = 18 * 10)117 1.601175 0.833610 1.921 0.05476 .
## ns(time, df = 18 * 10)118 -0.654219 0.942995 -0.694 0.48783
## ns(time, df = 18 * 10)119 0.619972 0.897633 0.691 0.48977
## ns(time, df = 18 * 10)120 0.614874 0.894654 0.687 0.49191
## ns(time, df = 18 * 10)121 -0.120912 0.974756 -0.124 0.90128
## ns(time, df = 18 * 10)122 -0.401090 0.971204 -0.413 0.67962
## ns(time, df = 18 * 10)123 1.065976 0.848233 1.257 0.20886
## ns(time, df = 18 * 10)124 0.506642 0.837465 0.605 0.54520
## ns(time, df = 18 * 10)125 0.915880 0.853851 1.073 0.28343
## ns(time, df = 18 * 10)126 -0.379470 0.939596 -0.404 0.68631
## ns(time, df = 18 * 10)127 0.929758 0.930425 0.999 0.31766
## ns(time, df = 18 * 10)128 -1.143034 1.031222 -1.108 0.26768
## ns(time, df = 18 * 10)129 1.249728 0.892319 1.401 0.16135
## ns(time, df = 18 * 10)130 -0.132228 0.924488 -0.143 0.88627
## ns(time, df = 18 * 10)131 0.285051 0.900623 0.317 0.75162
## ns(time, df = 18 * 10)132 0.818538 0.860711 0.951 0.34160
## ns(time, df = 18 * 10)133 0.318588 0.897167 0.355 0.72251
## ns(time, df = 18 * 10)134 0.173323 0.954220 0.182 0.85587
## ns(time, df = 18 * 10)135 -0.348022 0.993590 -0.350 0.72614
## ns(time, df = 18 * 10)136 0.384840 0.908534 0.424 0.67187
## ns(time, df = 18 * 10)137 0.741875 0.855150 0.868 0.38565
## ns(time, df = 18 * 10)138 0.383524 0.851726 0.450 0.65250
## ns(time, df = 18 * 10)139 1.364973 0.876464 1.557 0.11938
## ns(time, df = 18 * 10)140 -2.118734 1.104669 -1.918 0.05511 .
## ns(time, df = 18 * 10)141 1.874829 0.864891 2.168 0.03018 *
## ns(time, df = 18 * 10)142 -0.339252 0.892539 -0.380 0.70387
## ns(time, df = 18 * 10)143 0.864738 0.837499 1.033 0.30183
## ns(time, df = 18 * 10)144 0.748667 0.818124 0.915 0.36014
## ns(time, df = 18 * 10)145 0.527599 0.816736 0.646 0.51829
## ns(time, df = 18 * 10)146 1.191234 0.803997 1.482 0.13844
## ns(time, df = 18 * 10)147 0.077700 0.857947 0.091 0.92784
## ns(time, df = 18 * 10)148 0.681009 0.835904 0.815 0.41524
## ns(time, df = 18 * 10)149 0.979650 0.830125 1.180 0.23795
## ns(time, df = 18 * 10)150 -0.271570 0.875270 -0.310 0.75636
## ns(time, df = 18 * 10)151 1.681203 0.823613 2.041 0.04123 *
## ns(time, df = 18 * 10)152 -0.641633 0.933063 -0.688 0.49166
## ns(time, df = 18 * 10)153 0.586907 0.882800 0.665 0.50616
## ns(time, df = 18 * 10)154 0.740416 0.851078 0.870 0.38431
## ns(time, df = 18 * 10)155 0.218570 0.864917 0.253 0.80049
## ns(time, df = 18 * 10)156 0.829017 0.836585 0.991 0.32171
## ns(time, df = 18 * 10)157 0.352551 0.832940 0.423 0.67210
## ns(time, df = 18 * 10)158 1.201222 0.805587 1.491 0.13593
## ns(time, df = 18 * 10)159 0.387350 0.856534 0.452 0.65110
## ns(time, df = 18 * 10)160 0.034485 0.897850 0.038 0.96936
## ns(time, df = 18 * 10)161 0.667589 0.847841 0.787 0.43105
## ns(time, df = 18 * 10)162 0.760227 0.819992 0.927 0.35387
## ns(time, df = 18 * 10)163 0.969120 0.834938 1.161 0.24576
## ns(time, df = 18 * 10)164 -0.271604 0.926637 -0.293 0.76944
## ns(time, df = 18 * 10)165 0.260189 0.881266 0.295 0.76781
## ns(time, df = 18 * 10)166 1.185052 0.812105 1.459 0.14450
## ns(time, df = 18 * 10)167 0.748411 0.851208 0.879 0.37927
## ns(time, df = 18 * 10)168 -0.831895 0.938881 -0.886 0.37559
## ns(time, df = 18 * 10)169 1.902182 0.832076 2.286 0.02225 *
## ns(time, df = 18 * 10)170 -0.379558 0.950262 -0.399 0.68958
## ns(time, df = 18 * 10)171 -0.652353 1.004247 -0.650 0.51595
## ns(time, df = 18 * 10)172 1.273461 0.868894 1.466 0.14275
## ns(time, df = 18 * 10)173 -0.065542 0.897050 -0.073 0.94176
## ns(time, df = 18 * 10)174 0.410781 0.852979 0.482 0.63010
## ns(time, df = 18 * 10)175 1.234058 0.797146 1.548 0.12160
## ns(time, df = 18 * 10)176 0.263424 0.805926 0.327 0.74377
## ns(time, df = 18 * 10)177 1.592224 0.769621 2.069 0.03856 *
## ns(time, df = 18 * 10)178 -0.252187 0.672789 -0.375 0.70778
## ns(time, df = 18 * 10)179 1.564804 1.410393 1.109 0.26722
## ns(time, df = 18 * 10)180 -0.170103 0.550610 -0.309 0.75737
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(27991.49) family taken to be 1)
##
## Null deviance: 1179.10 on 938 degrees of freedom
## Residual deviance: 932.82 on 758 degrees of freedom
## AIC: 3444.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 27991
## Std. Err.: 146817
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3080.692## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 14), data = week,
## init.theta = 32829.99587, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.77979 -0.76453 -0.07699 0.50521 2.32975
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.259662 0.775115 -0.335 0.73763
## ns(time, df = 18 * 14)1 0.362418 0.911854 0.397 0.69103
## ns(time, df = 18 * 14)2 1.896747 1.280550 1.481 0.13855
## ns(time, df = 18 * 14)3 -2.090643 1.548733 -1.350 0.17705
## ns(time, df = 18 * 14)4 1.485666 1.252078 1.187 0.23540
## ns(time, df = 18 * 14)5 1.249822 1.174186 1.064 0.28714
## ns(time, df = 18 * 14)6 -0.678867 1.367417 -0.496 0.61957
## ns(time, df = 18 * 14)7 1.422046 1.197886 1.187 0.23518
## ns(time, df = 18 * 14)8 -0.125061 1.168317 -0.107 0.91475
## ns(time, df = 18 * 14)9 2.588344 1.016677 2.546 0.01090 *
## ns(time, df = 18 * 14)10 0.241197 1.064191 0.227 0.82070
## ns(time, df = 18 * 14)11 2.419154 1.010638 2.394 0.01668 *
## ns(time, df = 18 * 14)12 0.149411 1.094703 0.136 0.89144
## ns(time, df = 18 * 14)13 2.184688 1.070836 2.040 0.04133 *
## ns(time, df = 18 * 14)14 -0.760698 1.198972 -0.634 0.52578
## ns(time, df = 18 * 14)15 2.894325 1.066808 2.713 0.00667 **
## ns(time, df = 18 * 14)16 -0.672447 1.253963 -0.536 0.59178
## ns(time, df = 18 * 14)17 0.868575 1.177362 0.738 0.46068
## ns(time, df = 18 * 14)18 1.753266 1.128236 1.554 0.12019
## ns(time, df = 18 * 14)19 -0.453108 1.293802 -0.350 0.72618
## ns(time, df = 18 * 14)20 1.789387 1.304307 1.372 0.17009
## ns(time, df = 18 * 14)21 -2.021298 1.770771 -1.141 0.25367
## ns(time, df = 18 * 14)22 0.372409 1.413091 0.264 0.79213
## ns(time, df = 18 * 14)23 0.974144 1.185555 0.822 0.41126
## ns(time, df = 18 * 14)24 1.173056 1.112745 1.054 0.29179
## ns(time, df = 18 * 14)25 1.043230 1.113594 0.937 0.34885
## ns(time, df = 18 * 14)26 0.486330 1.097206 0.443 0.65759
## ns(time, df = 18 * 14)27 2.596256 1.028895 2.523 0.01162 *
## ns(time, df = 18 * 14)28 -0.392315 1.189153 -0.330 0.74147
## ns(time, df = 18 * 14)29 1.397361 1.131793 1.235 0.21696
## ns(time, df = 18 * 14)30 0.643549 1.121201 0.574 0.56598
## ns(time, df = 18 * 14)31 1.742700 1.097757 1.588 0.11240
## ns(time, df = 18 * 14)32 0.070448 1.231399 0.057 0.95438
## ns(time, df = 18 * 14)33 0.437784 1.208391 0.362 0.71714
## ns(time, df = 18 * 14)34 1.822547 1.141568 1.597 0.11037
## ns(time, df = 18 * 14)35 -0.742580 1.281562 -0.579 0.56230
## ns(time, df = 18 * 14)36 2.115764 1.137210 1.860 0.06282 .
## ns(time, df = 18 * 14)37 -0.384107 1.232180 -0.312 0.75525
## ns(time, df = 18 * 14)38 1.678236 1.128155 1.488 0.13686
## ns(time, df = 18 * 14)39 0.415661 1.162101 0.358 0.72058
## ns(time, df = 18 * 14)40 1.340659 1.157283 1.158 0.24668
## ns(time, df = 18 * 14)41 -0.352434 1.205853 -0.292 0.77008
## ns(time, df = 18 * 14)42 2.417564 1.049536 2.303 0.02125 *
## ns(time, df = 18 * 14)43 0.305605 1.111309 0.275 0.78332
## ns(time, df = 18 * 14)44 1.297448 1.065388 1.218 0.22329
## ns(time, df = 18 * 14)45 1.849729 1.057313 1.749 0.08021 .
## ns(time, df = 18 * 14)46 0.047327 1.213887 0.039 0.96890
## ns(time, df = 18 * 14)47 0.597327 1.222199 0.489 0.62503
## ns(time, df = 18 * 14)48 1.040929 1.175573 0.885 0.37591
## ns(time, df = 18 * 14)49 0.442261 1.186041 0.373 0.70923
## ns(time, df = 18 * 14)50 1.356091 1.143872 1.186 0.23581
## ns(time, df = 18 * 14)51 0.330569 1.203472 0.275 0.78356
## ns(time, df = 18 * 14)52 0.878953 1.166109 0.754 0.45100
## ns(time, df = 18 * 14)53 1.733473 1.202698 1.441 0.14949
## ns(time, df = 18 * 14)54 -1.832056 1.636300 -1.120 0.26287
## ns(time, df = 18 * 14)55 1.161269 1.402805 0.828 0.40777
## ns(time, df = 18 * 14)56 -0.138088 1.470913 -0.094 0.92520
## ns(time, df = 18 * 14)57 -0.606456 1.447016 -0.419 0.67514
## ns(time, df = 18 * 14)58 1.563404 1.173578 1.332 0.18280
## ns(time, df = 18 * 14)59 0.510122 1.135243 0.449 0.65318
## ns(time, df = 18 * 14)60 1.739220 1.076214 1.616 0.10608
## ns(time, df = 18 * 14)61 0.442802 1.128820 0.392 0.69486
## ns(time, df = 18 * 14)62 1.382464 1.098772 1.258 0.20832
## ns(time, df = 18 * 14)63 0.681103 1.109277 0.614 0.53921
## ns(time, df = 18 * 14)64 1.866381 1.104657 1.690 0.09111 .
## ns(time, df = 18 * 14)65 -0.166130 1.324861 -0.125 0.90021
## ns(time, df = 18 * 14)66 -0.325025 1.440893 -0.226 0.82153
## ns(time, df = 18 * 14)67 0.896887 1.282322 0.699 0.48429
## ns(time, df = 18 * 14)68 0.293665 1.238061 0.237 0.81250
## ns(time, df = 18 * 14)69 1.444852 1.174495 1.230 0.21863
## ns(time, df = 18 * 14)70 -0.285161 1.245424 -0.229 0.81889
## ns(time, df = 18 * 14)71 1.695023 1.106943 1.531 0.12570
## ns(time, df = 18 * 14)72 0.582159 1.102295 0.528 0.59741
## ns(time, df = 18 * 14)73 1.666135 1.043088 1.597 0.11020
## ns(time, df = 18 * 14)74 1.425156 1.077258 1.323 0.18585
## ns(time, df = 18 * 14)75 -0.251582 1.191801 -0.211 0.83281
## ns(time, df = 18 * 14)76 2.001430 1.060890 1.887 0.05922 .
## ns(time, df = 18 * 14)77 0.899897 1.092384 0.824 0.41006
## ns(time, df = 18 * 14)78 0.735309 1.125007 0.654 0.51337
## ns(time, df = 18 * 14)79 1.480445 1.110938 1.333 0.18266
## ns(time, df = 18 * 14)80 0.020375 1.180471 0.017 0.98623
## ns(time, df = 18 * 14)81 1.768044 1.089137 1.623 0.10452
## ns(time, df = 18 * 14)82 0.588277 1.126640 0.522 0.60156
## ns(time, df = 18 * 14)83 1.593327 1.160889 1.373 0.16991
## ns(time, df = 18 * 14)84 -0.370006 1.441335 -0.257 0.79740
## ns(time, df = 18 * 14)85 -1.246202 1.510798 -0.825 0.40945
## ns(time, df = 18 * 14)86 2.521808 1.174467 2.147 0.03178 *
## ns(time, df = 18 * 14)87 -0.525409 1.302504 -0.403 0.68667
## ns(time, df = 18 * 14)88 0.707954 1.216414 0.582 0.56057
## ns(time, df = 18 * 14)89 1.141713 1.103838 1.034 0.30099
## ns(time, df = 18 * 14)90 1.508573 1.072331 1.407 0.15948
## ns(time, df = 18 * 14)91 0.576444 1.122364 0.514 0.60753
## ns(time, df = 18 * 14)92 1.482098 1.116770 1.327 0.18447
## ns(time, df = 18 * 14)93 0.207345 1.212085 0.171 0.86417
## ns(time, df = 18 * 14)94 0.797628 1.183745 0.674 0.50043
## ns(time, df = 18 * 14)95 0.995214 1.138436 0.874 0.38201
## ns(time, df = 18 * 14)96 1.058238 1.136660 0.931 0.35185
## ns(time, df = 18 * 14)97 0.407778 1.153592 0.353 0.72372
## ns(time, df = 18 * 14)98 1.639016 1.078049 1.520 0.12842
## ns(time, df = 18 * 14)99 0.818957 1.107740 0.739 0.45972
## ns(time, df = 18 * 14)100 1.093109 1.131145 0.966 0.33386
## ns(time, df = 18 * 14)101 0.352585 1.155393 0.305 0.76024
## ns(time, df = 18 * 14)102 1.565558 1.063818 1.472 0.14112
## ns(time, df = 18 * 14)103 1.079766 1.045465 1.033 0.30169
## ns(time, df = 18 * 14)104 1.866623 1.041187 1.793 0.07301 .
## ns(time, df = 18 * 14)105 0.205754 1.150710 0.179 0.85809
## ns(time, df = 18 * 14)106 1.207025 1.121328 1.076 0.28174
## ns(time, df = 18 * 14)107 0.839201 1.107291 0.758 0.44852
## ns(time, df = 18 * 14)108 1.651230 1.103265 1.497 0.13448
## ns(time, df = 18 * 14)109 -0.297510 1.224490 -0.243 0.80803
## ns(time, df = 18 * 14)110 1.974245 1.154264 1.710 0.08719 .
## ns(time, df = 18 * 14)111 -0.764772 1.316492 -0.581 0.56130
## ns(time, df = 18 * 14)112 1.467904 1.165341 1.260 0.20780
## ns(time, df = 18 * 14)113 0.579544 1.149151 0.504 0.61403
## ns(time, df = 18 * 14)114 1.320149 1.112989 1.186 0.23557
## ns(time, df = 18 * 14)115 0.595612 1.129684 0.527 0.59803
## ns(time, df = 18 * 14)116 1.472855 1.093544 1.347 0.17802
## ns(time, df = 18 * 14)117 0.713751 1.138830 0.627 0.53083
## ns(time, df = 18 * 14)118 1.063673 1.181636 0.900 0.36803
## ns(time, df = 18 * 14)119 -0.188818 1.265055 -0.149 0.88135
## ns(time, df = 18 * 14)120 1.649852 1.164853 1.416 0.15667
## ns(time, df = 18 * 14)121 -0.284685 1.223682 -0.233 0.81604
## ns(time, df = 18 * 14)122 2.024112 1.107615 1.827 0.06763 .
## ns(time, df = 18 * 14)123 -0.058827 1.184248 -0.050 0.96038
## ns(time, df = 18 * 14)124 1.627605 1.115642 1.459 0.14459
## ns(time, df = 18 * 14)125 0.773599 1.189528 0.650 0.51547
## ns(time, df = 18 * 14)126 -0.035333 1.349603 -0.026 0.97911
## ns(time, df = 18 * 14)127 -0.050286 1.268896 -0.040 0.96839
## ns(time, df = 18 * 14)128 1.935605 1.074012 1.802 0.07151 .
## ns(time, df = 18 * 14)129 1.068843 1.091194 0.980 0.32732
## ns(time, df = 18 * 14)130 0.444194 1.141698 0.389 0.69723
## ns(time, df = 18 * 14)131 1.479120 1.070159 1.382 0.16693
## ns(time, df = 18 * 14)132 1.183126 1.066921 1.109 0.26747
## ns(time, df = 18 * 14)133 1.184990 1.099148 1.078 0.28099
## ns(time, df = 18 * 14)134 0.139190 1.121694 0.124 0.90124
## ns(time, df = 18 * 14)135 2.429599 1.001338 2.426 0.01525 *
## ns(time, df = 18 * 14)136 0.683406 1.032261 0.662 0.50794
## ns(time, df = 18 * 14)137 2.062265 1.008509 2.045 0.04087 *
## ns(time, df = 18 * 14)138 0.511609 1.061480 0.482 0.62982
## ns(time, df = 18 * 14)139 1.994992 1.021199 1.954 0.05075 .
## ns(time, df = 18 * 14)140 0.991355 1.088504 0.911 0.36243
## ns(time, df = 18 * 14)141 0.421777 1.186161 0.356 0.72215
## ns(time, df = 18 * 14)142 1.172710 1.195445 0.981 0.32660
## ns(time, df = 18 * 14)143 -0.134796 1.299930 -0.104 0.91741
## ns(time, df = 18 * 14)144 0.857853 1.208296 0.710 0.47772
## ns(time, df = 18 * 14)145 1.447703 1.208762 1.198 0.23104
## ns(time, df = 18 * 14)146 -1.183501 1.456132 -0.813 0.41635
## ns(time, df = 18 * 14)147 1.329292 1.218060 1.091 0.27513
## ns(time, df = 18 * 14)148 0.247008 1.123436 0.220 0.82597
## ns(time, df = 18 * 14)149 3.196230 1.050731 3.042 0.00235 **
## ns(time, df = 18 * 14)150 -1.876272 1.385128 -1.355 0.17555
## ns(time, df = 18 * 14)151 1.543915 1.119054 1.380 0.16769
## ns(time, df = 18 * 14)152 1.846747 1.057804 1.746 0.08084 .
## ns(time, df = 18 * 14)153 0.328679 1.182168 0.278 0.78099
## ns(time, df = 18 * 14)154 0.232972 1.189516 0.196 0.84472
## ns(time, df = 18 * 14)155 1.790334 1.052044 1.702 0.08880 .
## ns(time, df = 18 * 14)156 1.127898 1.044152 1.080 0.28005
## ns(time, df = 18 * 14)157 1.215657 1.027622 1.183 0.23682
## ns(time, df = 18 * 14)158 1.869919 1.001937 1.866 0.06200 .
## ns(time, df = 18 * 14)159 0.953742 1.045896 0.912 0.36183
## ns(time, df = 18 * 14)160 1.468252 1.060258 1.385 0.16611
## ns(time, df = 18 * 14)161 0.640271 1.109402 0.577 0.56385
## ns(time, df = 18 * 14)162 1.385243 1.093762 1.266 0.20534
## ns(time, df = 18 * 14)163 0.514830 1.114568 0.462 0.64415
## ns(time, df = 18 * 14)164 1.695027 1.050734 1.613 0.10670
## ns(time, df = 18 * 14)165 1.339063 1.096708 1.221 0.22209
## ns(time, df = 18 * 14)166 -0.234471 1.255973 -0.187 0.85191
## ns(time, df = 18 * 14)167 1.030518 1.137084 0.906 0.36479
## ns(time, df = 18 * 14)168 1.770427 1.089491 1.625 0.10416
## ns(time, df = 18 * 14)169 0.023423 1.217193 0.019 0.98465
## ns(time, df = 18 * 14)170 0.999944 1.207852 0.828 0.40774
## ns(time, df = 18 * 14)171 0.376769 1.234313 0.305 0.76018
## ns(time, df = 18 * 14)172 0.476340 1.154046 0.413 0.67979
## ns(time, df = 18 * 14)173 2.033877 1.036383 1.962 0.04971 *
## ns(time, df = 18 * 14)174 0.782087 1.064253 0.735 0.46242
## ns(time, df = 18 * 14)175 1.577693 1.057738 1.492 0.13581
## ns(time, df = 18 * 14)176 0.888256 1.121574 0.792 0.42838
## ns(time, df = 18 * 14)177 0.441671 1.186060 0.372 0.70961
## ns(time, df = 18 * 14)178 1.238445 1.162374 1.065 0.28668
## ns(time, df = 18 * 14)179 0.502508 1.251110 0.402 0.68794
## ns(time, df = 18 * 14)180 -0.377988 1.286434 -0.294 0.76889
## ns(time, df = 18 * 14)181 2.146552 1.104650 1.943 0.05199 .
## ns(time, df = 18 * 14)182 0.131282 1.170872 0.112 0.91073
## ns(time, df = 18 * 14)183 1.294290 1.136516 1.139 0.25478
## ns(time, df = 18 * 14)184 0.471345 1.141773 0.413 0.67974
## ns(time, df = 18 * 14)185 1.553493 1.074685 1.446 0.14831
## ns(time, df = 18 * 14)186 1.023656 1.095750 0.934 0.35020
## ns(time, df = 18 * 14)187 0.949696 1.145224 0.829 0.40695
## ns(time, df = 18 * 14)188 0.545917 1.210135 0.451 0.65190
## ns(time, df = 18 * 14)189 0.653969 1.232857 0.530 0.59580
## ns(time, df = 18 * 14)190 0.402661 1.231007 0.327 0.74359
## ns(time, df = 18 * 14)191 0.814305 1.134821 0.718 0.47303
## ns(time, df = 18 * 14)192 1.762939 1.063938 1.657 0.09752 .
## ns(time, df = 18 * 14)193 0.422679 1.107156 0.382 0.70263
## ns(time, df = 18 * 14)194 1.717457 1.049655 1.636 0.10180
## ns(time, df = 18 * 14)195 1.624076 1.119136 1.451 0.14673
## ns(time, df = 18 * 14)196 -1.403539 1.486751 -0.944 0.34515
## ns(time, df = 18 * 14)197 0.822556 1.208148 0.681 0.49597
## ns(time, df = 18 * 14)198 1.823399 1.075978 1.695 0.09014 .
## ns(time, df = 18 * 14)199 0.552704 1.121725 0.493 0.62221
## ns(time, df = 18 * 14)200 1.046036 1.088405 0.961 0.33652
## ns(time, df = 18 * 14)201 1.554187 1.046255 1.485 0.13742
## ns(time, df = 18 * 14)202 0.791065 1.052999 0.751 0.45250
## ns(time, df = 18 * 14)203 2.123546 1.016075 2.090 0.03662 *
## ns(time, df = 18 * 14)204 0.117444 1.074526 0.109 0.91297
## ns(time, df = 18 * 14)205 2.799845 1.007334 2.779 0.00544 **
## ns(time, df = 18 * 14)206 -0.392535 1.145073 -0.343 0.73175
## ns(time, df = 18 * 14)207 1.832315 1.058556 1.731 0.08346 .
## ns(time, df = 18 * 14)208 0.979392 1.063140 0.921 0.35693
## ns(time, df = 18 * 14)209 1.256037 1.057383 1.188 0.23488
## ns(time, df = 18 * 14)210 1.450289 1.069386 1.356 0.17504
## ns(time, df = 18 * 14)211 0.133956 1.117511 0.120 0.90459
## ns(time, df = 18 * 14)212 2.537770 1.033592 2.455 0.01408 *
## ns(time, df = 18 * 14)213 0.081670 1.173080 0.070 0.94450
## ns(time, df = 18 * 14)214 0.416249 1.200658 0.347 0.72883
## ns(time, df = 18 * 14)215 1.354094 1.088712 1.244 0.21359
## ns(time, df = 18 * 14)216 1.393844 1.070639 1.302 0.19296
## ns(time, df = 18 * 14)217 0.597425 1.111533 0.537 0.59094
## ns(time, df = 18 * 14)218 1.425070 1.066436 1.336 0.18145
## ns(time, df = 18 * 14)219 1.137518 1.066994 1.066 0.28638
## ns(time, df = 18 * 14)220 0.975207 1.062685 0.918 0.35879
## ns(time, df = 18 * 14)221 1.640697 1.021354 1.606 0.10819
## ns(time, df = 18 * 14)222 1.299661 1.033376 1.258 0.20851
## ns(time, df = 18 * 14)223 1.364408 1.074488 1.270 0.20415
## ns(time, df = 18 * 14)224 0.328040 1.166115 0.281 0.77847
## ns(time, df = 18 * 14)225 0.997236 1.111060 0.898 0.36942
## ns(time, df = 18 * 14)226 1.497112 1.058549 1.414 0.15727
## ns(time, df = 18 * 14)227 0.764085 1.057159 0.723 0.46982
## ns(time, df = 18 * 14)228 2.242558 1.017117 2.205 0.02747 *
## ns(time, df = 18 * 14)229 0.249941 1.120763 0.223 0.82353
## ns(time, df = 18 * 14)230 1.538109 1.135960 1.354 0.17573
## ns(time, df = 18 * 14)231 -0.493153 1.226767 -0.402 0.68769
## ns(time, df = 18 * 14)232 2.047990 1.043797 1.962 0.04976 *
## ns(time, df = 18 * 14)233 1.192901 1.030562 1.158 0.24706
## ns(time, df = 18 * 14)234 1.641887 1.054045 1.558 0.11930
## ns(time, df = 18 * 14)235 0.326463 1.170052 0.279 0.78023
## ns(time, df = 18 * 14)236 0.545995 1.140081 0.479 0.63200
## ns(time, df = 18 * 14)237 1.976773 1.040833 1.899 0.05754 .
## ns(time, df = 18 * 14)238 1.284162 1.115521 1.151 0.24966
## ns(time, df = 18 * 14)239 -0.700971 1.376879 -0.509 0.61068
## ns(time, df = 18 * 14)240 0.689533 1.223277 0.564 0.57297
## ns(time, df = 18 * 14)241 1.367572 1.101009 1.242 0.21420
## ns(time, df = 18 * 14)242 1.148413 1.097117 1.047 0.29521
## ns(time, df = 18 * 14)243 0.789152 1.136751 0.694 0.48755
## ns(time, df = 18 * 14)244 0.540523 1.104202 0.490 0.62448
## ns(time, df = 18 * 14)245 2.232277 1.007515 2.216 0.02672 *
## ns(time, df = 18 * 14)246 0.735248 1.044296 0.704 0.48139
## ns(time, df = 18 * 14)247 1.554842 1.008855 1.541 0.12327
## ns(time, df = 18 * 14)248 1.665570 0.988680 1.685 0.09206 .
## ns(time, df = 18 * 14)249 1.538081 1.010219 1.523 0.12788
## ns(time, df = 18 * 14)250 0.004517 0.846507 0.005 0.99574
## ns(time, df = 18 * 14)251 3.130414 1.836755 1.704 0.08832 .
## ns(time, df = 18 * 14)252 -0.186752 0.612740 -0.305 0.76053
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(32830) family taken to be 1)
##
## Null deviance: 1179.11 on 938 degrees of freedom
## Residual deviance: 861.69 on 686 degrees of freedom
## AIC: 3517.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 32830
## Std. Err.: 153456
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3009.555## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 15), data = week,
## init.theta = 33805.25556, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.70939 -0.75578 -0.03452 0.50500 2.42892
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.443263 0.841886 -0.527 0.59853
## ns(time, df = 18 * 15)1 0.380749 0.962786 0.395 0.69250
## ns(time, df = 18 * 15)2 2.373539 1.323327 1.794 0.07287 .
## ns(time, df = 18 * 15)3 -1.500659 1.585080 -0.947 0.34377
## ns(time, df = 18 * 15)4 0.338441 1.437296 0.235 0.81384
## ns(time, df = 18 * 15)5 2.319432 1.211052 1.915 0.05546 .
## ns(time, df = 18 * 15)6 -0.446395 1.417803 -0.315 0.75288
## ns(time, df = 18 * 15)7 1.000544 1.323849 0.756 0.44978
## ns(time, df = 18 * 15)8 0.783140 1.269764 0.617 0.53739
## ns(time, df = 18 * 15)9 0.922306 1.164524 0.792 0.42836
## ns(time, df = 18 * 15)10 2.486673 1.077291 2.308 0.02098 *
## ns(time, df = 18 * 15)11 0.471835 1.129344 0.418 0.67610
## ns(time, df = 18 * 15)12 2.637101 1.076602 2.449 0.01431 *
## ns(time, df = 18 * 15)13 0.248680 1.169145 0.213 0.83156
## ns(time, df = 18 * 15)14 2.333039 1.137082 2.052 0.04019 *
## ns(time, df = 18 * 15)15 -0.277312 1.256958 -0.221 0.82539
## ns(time, df = 18 * 15)16 2.351778 1.135764 2.071 0.03839 *
## ns(time, df = 18 * 15)17 1.054785 1.230053 0.858 0.39116
## ns(time, df = 18 * 15)18 -0.940585 1.401822 -0.671 0.50224
## ns(time, df = 18 * 15)19 3.211485 1.178502 2.725 0.00643 **
## ns(time, df = 18 * 15)20 -0.983478 1.387732 -0.709 0.47851
## ns(time, df = 18 * 15)21 2.096555 1.311118 1.599 0.10981
## ns(time, df = 18 * 15)22 -0.377742 1.584407 -0.238 0.81156
## ns(time, df = 18 * 15)23 -0.937516 1.777730 -0.527 0.59794
## ns(time, df = 18 * 15)24 0.981917 1.378613 0.712 0.47631
## ns(time, df = 18 * 15)25 1.074378 1.225629 0.877 0.38071
## ns(time, df = 18 * 15)26 1.534906 1.167604 1.315 0.18865
## ns(time, df = 18 * 15)27 0.950278 1.192504 0.797 0.42552
## ns(time, df = 18 * 15)28 0.921354 1.151039 0.800 0.42345
## ns(time, df = 18 * 15)29 2.630514 1.092406 2.408 0.01604 *
## ns(time, df = 18 * 15)30 0.049328 1.244549 0.040 0.96838
## ns(time, df = 18 * 15)31 1.184576 1.218096 0.972 0.33081
## ns(time, df = 18 * 15)32 1.268228 1.177222 1.077 0.28134
## ns(time, df = 18 * 15)33 1.235642 1.172310 1.054 0.29187
## ns(time, df = 18 * 15)34 1.558597 1.216881 1.281 0.20026
## ns(time, df = 18 * 15)35 -0.650901 1.384852 -0.470 0.63834
## ns(time, df = 18 * 15)36 2.314583 1.203023 1.924 0.05436 .
## ns(time, df = 18 * 15)37 0.134256 1.294485 0.104 0.91740
## ns(time, df = 18 * 15)38 1.067707 1.253404 0.852 0.39430
## ns(time, df = 18 * 15)39 1.281563 1.225368 1.046 0.29563
## ns(time, df = 18 * 15)40 0.493412 1.260952 0.391 0.69557
## ns(time, df = 18 * 15)41 1.539379 1.197974 1.285 0.19880
## ns(time, df = 18 * 15)42 0.795178 1.222862 0.650 0.51552
## ns(time, df = 18 * 15)43 1.404816 1.232832 1.140 0.25449
## ns(time, df = 18 * 15)44 -0.186294 1.282555 -0.145 0.88451
## ns(time, df = 18 * 15)45 2.571257 1.115184 2.306 0.02113 *
## ns(time, df = 18 * 15)46 0.643181 1.169364 0.550 0.58230
## ns(time, df = 18 * 15)47 1.312519 1.142797 1.149 0.25076
## ns(time, df = 18 * 15)48 1.873916 1.112148 1.685 0.09200 .
## ns(time, df = 18 * 15)49 1.229987 1.203172 1.022 0.30665
## ns(time, df = 18 * 15)50 -0.319072 1.373058 -0.232 0.81624
## ns(time, df = 18 * 15)51 1.836542 1.236677 1.485 0.13753
## ns(time, df = 18 * 15)52 0.212280 1.282550 0.166 0.86854
## ns(time, df = 18 * 15)53 1.622956 1.210453 1.341 0.17999
## ns(time, df = 18 * 15)54 0.663494 1.249854 0.531 0.59552
## ns(time, df = 18 * 15)55 1.084311 1.241769 0.873 0.38255
## ns(time, df = 18 * 15)56 0.919888 1.233226 0.746 0.45572
## ns(time, df = 18 * 15)57 1.920891 1.290455 1.489 0.13661
## ns(time, df = 18 * 15)58 -1.963720 1.788038 -1.098 0.27209
## ns(time, df = 18 * 15)59 1.537848 1.473939 1.043 0.29678
## ns(time, df = 18 * 15)60 -0.033976 1.557807 -0.022 0.98260
## ns(time, df = 18 * 15)61 -0.461669 1.558587 -0.296 0.76707
## ns(time, df = 18 * 15)62 1.529720 1.268804 1.206 0.22796
## ns(time, df = 18 * 15)63 0.851597 1.209149 0.704 0.48125
## ns(time, df = 18 * 15)64 1.573823 1.150704 1.368 0.17140
## ns(time, df = 18 * 15)65 1.284246 1.164009 1.103 0.26990
## ns(time, df = 18 * 15)66 0.837712 1.194458 0.701 0.48310
## ns(time, df = 18 * 15)67 1.555516 1.158077 1.343 0.17921
## ns(time, df = 18 * 15)68 0.979607 1.171997 0.836 0.40324
## ns(time, df = 18 * 15)69 2.028685 1.203319 1.686 0.09181 .
## ns(time, df = 18 * 15)70 -0.821114 1.529658 -0.537 0.59141
## ns(time, df = 18 * 15)71 0.524888 1.464285 0.358 0.72000
## ns(time, df = 18 * 15)72 0.788882 1.353592 0.583 0.56002
## ns(time, df = 18 * 15)73 0.672296 1.293658 0.520 0.60328
## ns(time, df = 18 * 15)74 1.542694 1.243669 1.240 0.21481
## ns(time, df = 18 * 15)75 -0.065322 1.319444 -0.050 0.96052
## ns(time, df = 18 * 15)76 1.770421 1.182977 1.497 0.13450
## ns(time, df = 18 * 15)77 0.877298 1.173281 0.748 0.45462
## ns(time, df = 18 * 15)78 1.534938 1.118534 1.372 0.16998
## ns(time, df = 18 * 15)79 1.971322 1.115327 1.767 0.07715 .
## ns(time, df = 18 * 15)80 0.281049 1.240969 0.226 0.82083
## ns(time, df = 18 * 15)81 1.157136 1.176362 0.984 0.32528
## ns(time, df = 18 * 15)82 1.987852 1.119690 1.775 0.07584 .
## ns(time, df = 18 * 15)83 0.719912 1.190156 0.605 0.54525
## ns(time, df = 18 * 15)84 1.236507 1.182807 1.045 0.29584
## ns(time, df = 18 * 15)85 1.463331 1.188845 1.231 0.21837
## ns(time, df = 18 * 15)86 0.179826 1.252135 0.144 0.88580
## ns(time, df = 18 * 15)87 2.111767 1.147237 1.841 0.06566 .
## ns(time, df = 18 * 15)88 0.655858 1.200242 0.546 0.58476
## ns(time, df = 18 * 15)89 1.735886 1.228425 1.413 0.15763
## ns(time, df = 18 * 15)90 0.213647 1.497077 0.143 0.88652
## ns(time, df = 18 * 15)91 -2.074724 1.779645 -1.166 0.24369
## ns(time, df = 18 * 15)92 3.027062 1.260172 2.402 0.01630 *
## ns(time, df = 18 * 15)93 -0.427889 1.358526 -0.315 0.75279
## ns(time, df = 18 * 15)94 1.401835 1.285631 1.090 0.27554
## ns(time, df = 18 * 15)95 0.114077 1.253590 0.091 0.92749
## ns(time, df = 18 * 15)96 2.646049 1.121103 2.360 0.01826 *
## ns(time, df = 18 * 15)97 0.001017 1.212596 0.001 0.99933
## ns(time, df = 18 * 15)98 2.341534 1.149331 2.037 0.04162 *
## ns(time, df = 18 * 15)99 0.031059 1.270187 0.024 0.98049
## ns(time, df = 18 * 15)100 1.580893 1.236447 1.279 0.20105
## ns(time, df = 18 * 15)101 0.174929 1.284535 0.136 0.89168
## ns(time, df = 18 * 15)102 1.750152 1.186528 1.475 0.14021
## ns(time, df = 18 * 15)103 0.846595 1.219660 0.694 0.48760
## ns(time, df = 18 * 15)104 0.809474 1.215552 0.666 0.50546
## ns(time, df = 18 * 15)105 1.663670 1.147171 1.450 0.14699
## ns(time, df = 18 * 15)106 1.174252 1.165364 1.008 0.31363
## ns(time, df = 18 * 15)107 1.145596 1.196676 0.957 0.33841
## ns(time, df = 18 * 15)108 0.860390 1.219091 0.706 0.48034
## ns(time, df = 18 * 15)109 1.090623 1.169079 0.933 0.35088
## ns(time, df = 18 * 15)110 1.754586 1.104078 1.589 0.11202
## ns(time, df = 18 * 15)111 1.475671 1.105449 1.335 0.18191
## ns(time, df = 18 * 15)112 1.578768 1.140788 1.384 0.16638
## ns(time, df = 18 * 15)113 0.472107 1.231297 0.383 0.70141
## ns(time, df = 18 * 15)114 1.405717 1.180504 1.191 0.23374
## ns(time, df = 18 * 15)115 1.205083 1.164602 1.035 0.30078
## ns(time, df = 18 * 15)116 1.581514 1.185714 1.334 0.18227
## ns(time, df = 18 * 15)117 -0.025002 1.293234 -0.019 0.98458
## ns(time, df = 18 * 15)118 2.194557 1.223633 1.793 0.07290 .
## ns(time, df = 18 * 15)119 -0.747027 1.407074 -0.531 0.59548
## ns(time, df = 18 * 15)120 1.791655 1.234196 1.452 0.14659
## ns(time, df = 18 * 15)121 0.543704 1.227520 0.443 0.65782
## ns(time, df = 18 * 15)122 1.793066 1.173644 1.528 0.12657
## ns(time, df = 18 * 15)123 0.367048 1.218293 0.301 0.76320
## ns(time, df = 18 * 15)124 2.067149 1.149514 1.798 0.07213 .
## ns(time, df = 18 * 15)125 0.374158 1.215955 0.308 0.75831
## ns(time, df = 18 * 15)126 2.068835 1.197844 1.727 0.08414 .
## ns(time, df = 18 * 15)127 -0.511222 1.382180 -0.370 0.71148
## ns(time, df = 18 * 15)128 1.537469 1.253985 1.226 0.22017
## ns(time, df = 18 * 15)129 0.844508 1.265190 0.667 0.50446
## ns(time, df = 18 * 15)130 0.425505 1.260173 0.338 0.73562
## ns(time, df = 18 * 15)131 2.186886 1.169946 1.869 0.06159 .
## ns(time, df = 18 * 15)132 -0.163753 1.274430 -0.128 0.89776
## ns(time, df = 18 * 15)133 2.159213 1.173649 1.840 0.06581 .
## ns(time, df = 18 * 15)134 0.532820 1.272111 0.419 0.67533
## ns(time, df = 18 * 15)135 0.840939 1.390545 0.605 0.54534
## ns(time, df = 18 * 15)136 -0.947342 1.456470 -0.650 0.51541
## ns(time, df = 18 * 15)137 2.701804 1.146450 2.357 0.01844 *
## ns(time, df = 18 * 15)138 0.606084 1.170121 0.518 0.60448
## ns(time, df = 18 * 15)139 1.868304 1.161622 1.608 0.10776
## ns(time, df = 18 * 15)140 -0.038578 1.225774 -0.031 0.97489
## ns(time, df = 18 * 15)141 2.702459 1.104361 2.447 0.01440 *
## ns(time, df = 18 * 15)142 0.271711 1.190973 0.228 0.81954
## ns(time, df = 18 * 15)143 1.819744 1.157548 1.572 0.11593
## ns(time, df = 18 * 15)144 0.300041 1.164777 0.258 0.79672
## ns(time, df = 18 * 15)145 2.917745 1.055442 2.764 0.00570 **
## ns(time, df = 18 * 15)146 0.432045 1.117295 0.387 0.69899
## ns(time, df = 18 * 15)147 2.545785 1.070554 2.378 0.01741 *
## ns(time, df = 18 * 15)148 0.473831 1.138547 0.416 0.67728
## ns(time, df = 18 * 15)149 2.215072 1.085687 2.040 0.04133 *
## ns(time, df = 18 * 15)150 1.344529 1.145493 1.174 0.24049
## ns(time, df = 18 * 15)151 0.323024 1.268897 0.255 0.79905
## ns(time, df = 18 * 15)152 1.631855 1.244568 1.311 0.18980
## ns(time, df = 18 * 15)153 0.053972 1.374120 0.039 0.96867
## ns(time, df = 18 * 15)154 0.651396 1.311570 0.497 0.61943
## ns(time, df = 18 * 15)155 1.785733 1.243536 1.436 0.15100
## ns(time, df = 18 * 15)156 -0.134223 1.415791 -0.095 0.92447
## ns(time, df = 18 * 15)157 0.590233 1.391899 0.424 0.67153
## ns(time, df = 18 * 15)158 0.563159 1.267320 0.444 0.65678
## ns(time, df = 18 * 15)159 1.868283 1.117806 1.671 0.09465 .
## ns(time, df = 18 * 15)160 2.219001 1.141878 1.943 0.05198 .
## ns(time, df = 18 * 15)161 -1.332277 1.457453 -0.914 0.36066
## ns(time, df = 18 * 15)162 1.935718 1.165954 1.660 0.09687 .
## ns(time, df = 18 * 15)163 1.898842 1.123580 1.690 0.09103 .
## ns(time, df = 18 * 15)164 0.633492 1.252557 0.506 0.61303
## ns(time, df = 18 * 15)165 0.135781 1.290272 0.105 0.91619
## ns(time, df = 18 * 15)166 2.058459 1.122548 1.834 0.06669 .
## ns(time, df = 18 * 15)167 1.301351 1.113407 1.169 0.24248
## ns(time, df = 18 * 15)168 1.336439 1.100510 1.214 0.22460
## ns(time, df = 18 * 15)169 2.005796 1.067501 1.879 0.06025 .
## ns(time, df = 18 * 15)170 1.294840 1.094437 1.183 0.23677
## ns(time, df = 18 * 15)171 1.787309 1.111332 1.608 0.10778
## ns(time, df = 18 * 15)172 0.615992 1.177182 0.523 0.60078
## ns(time, df = 18 * 15)173 1.932602 1.144374 1.689 0.09126 .
## ns(time, df = 18 * 15)174 0.413686 1.211746 0.341 0.73280
## ns(time, df = 18 * 15)175 1.630357 1.141169 1.429 0.15310
## ns(time, df = 18 * 15)176 1.405964 1.124472 1.250 0.21118
## ns(time, df = 18 * 15)177 1.808258 1.167886 1.548 0.12155
## ns(time, df = 18 * 15)178 -0.533317 1.372595 -0.389 0.69761
## ns(time, df = 18 * 15)179 1.565691 1.196162 1.309 0.19056
## ns(time, df = 18 * 15)180 1.601055 1.157179 1.384 0.16649
## ns(time, df = 18 * 15)181 0.880868 1.244966 0.708 0.47923
## ns(time, df = 18 * 15)182 0.351313 1.321436 0.266 0.79035
## ns(time, df = 18 * 15)183 1.372830 1.267297 1.083 0.27869
## ns(time, df = 18 * 15)184 0.105175 1.293817 0.081 0.93521
## ns(time, df = 18 * 15)185 1.708148 1.133242 1.507 0.13173
## ns(time, df = 18 * 15)186 1.916916 1.097512 1.747 0.08071 .
## ns(time, df = 18 * 15)187 0.694941 1.151188 0.604 0.54606
## ns(time, df = 18 * 15)188 2.222669 1.125187 1.975 0.04823 *
## ns(time, df = 18 * 15)189 0.181873 1.253185 0.145 0.88461
## ns(time, df = 18 * 15)190 1.338884 1.228488 1.090 0.27577
## ns(time, df = 18 * 15)191 0.947076 1.251154 0.757 0.44907
## ns(time, df = 18 * 15)192 0.914727 1.320594 0.693 0.48852
## ns(time, df = 18 * 15)193 -0.378655 1.365714 -0.277 0.78158
## ns(time, df = 18 * 15)194 2.508047 1.166297 2.150 0.03152 *
## ns(time, df = 18 * 15)195 0.156448 1.244656 0.126 0.89997
## ns(time, df = 18 * 15)196 1.644725 1.200880 1.370 0.17081
## ns(time, df = 18 * 15)197 0.452379 1.227363 0.369 0.71244
## ns(time, df = 18 * 15)198 1.756412 1.145234 1.534 0.12511
## ns(time, df = 18 * 15)199 1.178906 1.153324 1.022 0.30670
## ns(time, df = 18 * 15)200 1.415343 1.181618 1.198 0.23099
## ns(time, df = 18 * 15)201 0.588177 1.263374 0.466 0.64153
## ns(time, df = 18 * 15)202 1.172345 1.277357 0.918 0.35873
## ns(time, df = 18 * 15)203 0.282350 1.342334 0.210 0.83340
## ns(time, df = 18 * 15)204 0.961901 1.260705 0.763 0.44547
## ns(time, df = 18 * 15)205 1.156500 1.174076 0.985 0.32461
## ns(time, df = 18 * 15)206 1.849923 1.129803 1.637 0.10155
## ns(time, df = 18 * 15)207 0.572815 1.175789 0.487 0.62613
## ns(time, df = 18 * 15)208 2.001952 1.110903 1.802 0.07153 .
## ns(time, df = 18 * 15)209 1.773241 1.189660 1.491 0.13608
## ns(time, df = 18 * 15)210 -1.268602 1.598372 -0.794 0.42738
## ns(time, df = 18 * 15)211 0.750256 1.313899 0.571 0.56799
## ns(time, df = 18 * 15)212 2.070418 1.147188 1.805 0.07111 .
## ns(time, df = 18 * 15)213 0.729590 1.179498 0.619 0.53621
## ns(time, df = 18 * 15)214 1.590320 1.153900 1.378 0.16814
## ns(time, df = 18 * 15)215 0.753970 1.153507 0.654 0.51335
## ns(time, df = 18 * 15)216 2.303925 1.089372 2.115 0.03444 *
## ns(time, df = 18 * 15)217 0.488870 1.134619 0.431 0.66656
## ns(time, df = 18 * 15)218 2.659644 1.076521 2.471 0.01349 *
## ns(time, df = 18 * 15)219 -0.036861 1.153001 -0.032 0.97450
## ns(time, df = 18 * 15)220 3.422383 1.078516 3.173 0.00151 **
## ns(time, df = 18 * 15)221 -1.129237 1.291153 -0.875 0.38179
## ns(time, df = 18 * 15)222 2.626869 1.115133 2.356 0.01849 *
## ns(time, df = 18 * 15)223 0.777743 1.142809 0.681 0.49615
## ns(time, df = 18 * 15)224 1.623302 1.120660 1.449 0.14747
## ns(time, df = 18 * 15)225 1.549332 1.135593 1.364 0.17246
## ns(time, df = 18 * 15)226 0.482848 1.189018 0.406 0.68468
## ns(time, df = 18 * 15)227 2.111974 1.103695 1.914 0.05568 .
## ns(time, df = 18 * 15)228 1.650334 1.169136 1.412 0.15807
## ns(time, df = 18 * 15)229 -1.039361 1.421071 -0.731 0.46454
## ns(time, df = 18 * 15)230 2.326445 1.165776 1.996 0.04598 *
## ns(time, df = 18 * 15)231 0.740002 1.164152 0.636 0.52500
## ns(time, df = 18 * 15)232 1.913229 1.136141 1.684 0.09219 .
## ns(time, df = 18 * 15)233 0.432711 1.189061 0.364 0.71592
## ns(time, df = 18 * 15)234 2.067355 1.117393 1.850 0.06429 .
## ns(time, df = 18 * 15)235 0.848825 1.151019 0.737 0.46085
## ns(time, df = 18 * 15)236 1.517168 1.113688 1.362 0.17311
## ns(time, df = 18 * 15)237 1.697601 1.087210 1.561 0.11842
## ns(time, df = 18 * 15)238 1.494750 1.099792 1.359 0.17411
## ns(time, df = 18 * 15)239 1.617616 1.139442 1.420 0.15571
## ns(time, df = 18 * 15)240 0.383777 1.248274 0.307 0.75850
## ns(time, df = 18 * 15)241 1.182650 1.183801 0.999 0.31778
## ns(time, df = 18 * 15)242 1.717423 1.130467 1.519 0.12871
## ns(time, df = 18 * 15)243 0.751741 1.137716 0.661 0.50878
## ns(time, df = 18 * 15)244 2.480440 1.075313 2.307 0.02107 *
## ns(time, df = 18 * 15)245 0.669890 1.152167 0.581 0.56096
## ns(time, df = 18 * 15)246 1.839453 1.175531 1.565 0.11763
## ns(time, df = 18 * 15)247 -0.119179 1.315950 -0.091 0.92784
## ns(time, df = 18 * 15)248 1.280264 1.182949 1.082 0.27914
## ns(time, df = 18 * 15)249 1.765648 1.096528 1.610 0.10735
## ns(time, df = 18 * 15)250 1.636572 1.091595 1.499 0.13381
## ns(time, df = 18 * 15)251 1.542399 1.138494 1.355 0.17549
## ns(time, df = 18 * 15)252 0.515710 1.250571 0.412 0.68006
## ns(time, df = 18 * 15)253 0.765886 1.202078 0.637 0.52404
## ns(time, df = 18 * 15)254 2.199876 1.101483 1.997 0.04580 *
## ns(time, df = 18 * 15)255 1.404195 1.171831 1.198 0.23080
## ns(time, df = 18 * 15)256 0.031020 1.406989 0.022 0.98241
## ns(time, df = 18 * 15)257 -0.095017 1.385706 -0.069 0.94533
## ns(time, df = 18 * 15)258 2.121853 1.169987 1.814 0.06974 .
## ns(time, df = 18 * 15)259 0.713870 1.180763 0.605 0.54546
## ns(time, df = 18 * 15)260 1.902512 1.165770 1.632 0.10268
## ns(time, df = 18 * 15)261 -0.155097 1.254364 -0.124 0.90160
## ns(time, df = 18 * 15)262 2.244411 1.096554 2.047 0.04068 *
## ns(time, df = 18 * 15)263 1.443077 1.088803 1.325 0.18505
## ns(time, df = 18 * 15)264 1.424847 1.095182 1.301 0.19325
## ns(time, df = 18 * 15)265 1.576026 1.070999 1.472 0.14114
## ns(time, df = 18 * 15)266 2.029655 1.051007 1.931 0.05346 .
## ns(time, df = 18 * 15)267 1.480270 1.087695 1.361 0.17354
## ns(time, df = 18 * 15)268 0.187847 0.887920 0.212 0.83245
## ns(time, df = 18 * 15)269 3.621689 1.976319 1.833 0.06687 .
## ns(time, df = 18 * 15)270 -0.216257 0.630661 -0.343 0.73167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(33805.26) family taken to be 1)
##
## Null deviance: 1179.11 on 938 degrees of freedom
## Residual deviance: 837.74 on 668 degrees of freedom
## AIC: 3529.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 33805
## Std. Err.: 152584
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2985.607## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 16), data = week,
## init.theta = 33776.54728, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.85054 -0.76197 -0.06035 0.51821 2.42694
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.56669 0.89831 -0.631 0.52814
## ns(time, df = 18 * 16)1 0.52346 1.00282 0.522 0.60168
## ns(time, df = 18 * 16)2 2.49790 1.35634 1.842 0.06553 .
## ns(time, df = 18 * 16)3 -0.75167 1.52759 -0.492 0.62267
## ns(time, df = 18 * 16)4 0.05489 1.59402 0.034 0.97253
## ns(time, df = 18 * 16)5 1.72751 1.29295 1.336 0.18151
## ns(time, df = 18 * 16)6 1.38888 1.33753 1.038 0.29909
## ns(time, df = 18 * 16)7 -0.30680 1.50041 -0.204 0.83798
## ns(time, df = 18 * 16)8 1.55719 1.35208 1.152 0.24945
## ns(time, df = 18 * 16)9 0.46749 1.31447 0.356 0.72210
## ns(time, df = 18 * 16)10 1.91961 1.17281 1.637 0.10168
## ns(time, df = 18 * 16)11 1.97881 1.14164 1.733 0.08304 .
## ns(time, df = 18 * 16)12 1.03520 1.17184 0.883 0.37702
## ns(time, df = 18 * 16)13 2.52854 1.13797 2.222 0.02629 *
## ns(time, df = 18 * 16)14 0.47887 1.22951 0.389 0.69692
## ns(time, df = 18 * 16)15 2.31222 1.19699 1.932 0.05340 .
## ns(time, df = 18 * 16)16 0.21531 1.30457 0.165 0.86891
## ns(time, df = 18 * 16)17 1.75699 1.21089 1.451 0.14678
## ns(time, df = 18 * 16)18 2.25715 1.24263 1.816 0.06931 .
## ns(time, df = 18 * 16)19 -1.78530 1.60064 -1.115 0.26469
## ns(time, df = 18 * 16)20 3.09796 1.24605 2.486 0.01291 *
## ns(time, df = 18 * 16)21 0.30160 1.33686 0.226 0.82151
## ns(time, df = 18 * 16)22 1.02728 1.37809 0.745 0.45601
## ns(time, df = 18 * 16)23 1.28310 1.45176 0.884 0.37679
## ns(time, df = 18 * 16)24 -0.73494 1.83604 -0.400 0.68895
## ns(time, df = 18 * 16)25 -0.38358 1.72332 -0.223 0.82386
## ns(time, df = 18 * 16)26 1.55141 1.36053 1.140 0.25416
## ns(time, df = 18 * 16)27 0.97057 1.27381 0.762 0.44610
## ns(time, df = 18 * 16)28 1.92177 1.21660 1.580 0.11419
## ns(time, df = 18 * 16)29 0.71046 1.27113 0.559 0.57621
## ns(time, df = 18 * 16)30 1.34813 1.19635 1.127 0.25980
## ns(time, df = 18 * 16)31 2.57488 1.14870 2.242 0.02499 *
## ns(time, df = 18 * 16)32 0.42921 1.29259 0.332 0.73985
## ns(time, df = 18 * 16)33 0.95950 1.29849 0.739 0.45994
## ns(time, df = 18 * 16)34 1.72113 1.23176 1.397 0.16233
## ns(time, df = 18 * 16)35 0.86812 1.24754 0.696 0.48651
## ns(time, df = 18 * 16)36 2.27948 1.23700 1.843 0.06537 .
## ns(time, df = 18 * 16)37 -0.44015 1.46344 -0.301 0.76360
## ns(time, df = 18 * 16)38 1.20981 1.31865 0.917 0.35890
## ns(time, df = 18 * 16)39 2.09158 1.26983 1.647 0.09953 .
## ns(time, df = 18 * 16)40 -0.72917 1.45816 -0.500 0.61703
## ns(time, df = 18 * 16)41 2.53516 1.26477 2.004 0.04502 *
## ns(time, df = 18 * 16)42 0.02610 1.35974 0.019 0.98468
## ns(time, df = 18 * 16)43 1.67266 1.27509 1.312 0.18959
## ns(time, df = 18 * 16)44 0.99451 1.27828 0.778 0.43656
## ns(time, df = 18 * 16)45 1.35065 1.26959 1.064 0.28740
## ns(time, df = 18 * 16)46 1.23888 1.30825 0.947 0.34365
## ns(time, df = 18 * 16)47 0.04644 1.34689 0.034 0.97249
## ns(time, df = 18 * 16)48 2.56182 1.17461 2.181 0.02918 *
## ns(time, df = 18 * 16)49 1.03196 1.21515 0.849 0.39575
## ns(time, df = 18 * 16)50 1.18646 1.21830 0.974 0.33012
## ns(time, df = 18 * 16)51 1.91873 1.16680 1.644 0.10008
## ns(time, df = 18 * 16)52 1.79366 1.21550 1.476 0.14004
## ns(time, df = 18 * 16)53 -0.02878 1.41692 -0.020 0.98379
## ns(time, df = 18 * 16)54 1.18627 1.33923 0.886 0.37574
## ns(time, df = 18 * 16)55 1.37154 1.30511 1.051 0.29331
## ns(time, df = 18 * 16)56 0.40794 1.33756 0.305 0.76038
## ns(time, df = 18 * 16)57 2.12466 1.25425 1.694 0.09027 .
## ns(time, df = 18 * 16)58 0.14396 1.36010 0.106 0.91571
## ns(time, df = 18 * 16)59 1.61184 1.28262 1.257 0.20887
## ns(time, df = 18 * 16)60 1.02196 1.29101 0.792 0.42860
## ns(time, df = 18 * 16)61 1.87670 1.37886 1.361 0.17350
## ns(time, df = 18 * 16)62 -1.97575 1.90650 -1.036 0.30005
## ns(time, df = 18 * 16)63 1.87726 1.53411 1.224 0.22107
## ns(time, df = 18 * 16)64 -0.17351 1.64687 -0.105 0.91609
## ns(time, df = 18 * 16)65 0.01847 1.61764 0.011 0.99089
## ns(time, df = 18 * 16)66 1.11395 1.37378 0.811 0.41744
## ns(time, df = 18 * 16)67 1.33753 1.26806 1.055 0.29152
## ns(time, df = 18 * 16)68 1.28443 1.22568 1.048 0.29467
## ns(time, df = 18 * 16)69 1.82544 1.20330 1.517 0.12926
## ns(time, df = 18 * 16)70 0.76800 1.26117 0.609 0.54255
## ns(time, df = 18 * 16)71 1.65577 1.22402 1.353 0.17614
## ns(time, df = 18 * 16)72 1.11346 1.22943 0.906 0.36511
## ns(time, df = 18 * 16)73 1.79330 1.21582 1.475 0.14022
## ns(time, df = 18 * 16)74 1.31932 1.33140 0.991 0.32172
## ns(time, df = 18 * 16)75 -0.69713 1.65576 -0.421 0.67373
## ns(time, df = 18 * 16)76 0.99001 1.49225 0.663 0.50705
## ns(time, df = 18 * 16)77 0.67932 1.41966 0.479 0.63229
## ns(time, df = 18 * 16)78 1.00770 1.34111 0.751 0.45242
## ns(time, df = 18 * 16)79 1.51949 1.30727 1.162 0.24510
## ns(time, df = 18 * 16)80 0.20338 1.37757 0.148 0.88263
## ns(time, df = 18 * 16)81 1.69880 1.25476 1.354 0.17577
## ns(time, df = 18 * 16)82 1.12539 1.23226 0.913 0.36110
## ns(time, df = 18 * 16)83 1.54999 1.18535 1.308 0.19100
## ns(time, df = 18 * 16)84 1.91309 1.16408 1.643 0.10029
## ns(time, df = 18 * 16)85 1.38211 1.23366 1.120 0.26257
## ns(time, df = 18 * 16)86 0.20499 1.31864 0.155 0.87646
## ns(time, df = 18 * 16)87 2.28960 1.18154 1.938 0.05265 .
## ns(time, df = 18 * 16)88 1.26127 1.20873 1.043 0.29673
## ns(time, df = 18 * 16)89 1.17732 1.24827 0.943 0.34560
## ns(time, df = 18 * 16)90 1.29128 1.24398 1.038 0.29926
## ns(time, df = 18 * 16)91 1.53527 1.25609 1.222 0.22161
## ns(time, df = 18 * 16)92 0.29601 1.31013 0.226 0.82125
## ns(time, df = 18 * 16)93 2.36358 1.19913 1.971 0.04872 *
## ns(time, df = 18 * 16)94 0.63943 1.26654 0.505 0.61366
## ns(time, df = 18 * 16)95 1.93607 1.28477 1.507 0.13182
## ns(time, df = 18 * 16)96 0.37842 1.55561 0.243 0.80780
## ns(time, df = 18 * 16)97 -1.96576 1.92737 -1.020 0.30777
## ns(time, df = 18 * 16)98 2.57368 1.34183 1.918 0.05511 .
## ns(time, df = 18 * 16)99 0.60907 1.35030 0.451 0.65194
## ns(time, df = 18 * 16)100 0.93386 1.37984 0.677 0.49854
## ns(time, df = 18 * 16)101 0.60285 1.34682 0.448 0.65443
## ns(time, df = 18 * 16)102 1.61906 1.21348 1.334 0.18213
## ns(time, df = 18 * 16)103 2.07260 1.19072 1.741 0.08175 .
## ns(time, df = 18 * 16)104 0.08685 1.29204 0.067 0.94641
## ns(time, df = 18 * 16)105 2.83892 1.21328 2.340 0.01929 *
## ns(time, df = 18 * 16)106 -0.72437 1.41825 -0.511 0.60953
## ns(time, df = 18 * 16)107 2.32507 1.28410 1.811 0.07019 .
## ns(time, df = 18 * 16)108 -0.15598 1.36833 -0.114 0.90924
## ns(time, df = 18 * 16)109 2.21972 1.23667 1.795 0.07267 .
## ns(time, df = 18 * 16)110 0.68721 1.29358 0.531 0.59525
## ns(time, df = 18 * 16)111 1.11060 1.27127 0.874 0.38233
## ns(time, df = 18 * 16)112 1.62678 1.20976 1.345 0.17872
## ns(time, df = 18 * 16)113 1.47152 1.21542 1.211 0.22601
## ns(time, df = 18 * 16)114 1.13506 1.25441 0.905 0.36554
## ns(time, df = 18 * 16)115 1.30833 1.27282 1.028 0.30400
## ns(time, df = 18 * 16)116 0.60557 1.27502 0.475 0.63483
## ns(time, df = 18 * 16)117 2.25017 1.16581 1.930 0.05359 .
## ns(time, df = 18 * 16)118 1.07682 1.17601 0.916 0.35985
## ns(time, df = 18 * 16)119 2.41750 1.15783 2.088 0.03680 *
## ns(time, df = 18 * 16)120 0.38428 1.28330 0.299 0.76460
## ns(time, df = 18 * 16)121 1.53032 1.25529 1.219 0.22281
## ns(time, df = 18 * 16)122 1.06235 1.24461 0.854 0.39335
## ns(time, df = 18 * 16)123 1.75178 1.21273 1.444 0.14860
## ns(time, df = 18 * 16)124 1.21228 1.27130 0.954 0.34030
## ns(time, df = 18 * 16)125 0.44512 1.33868 0.333 0.73951
## ns(time, df = 18 * 16)126 2.14708 1.28907 1.666 0.09579 .
## ns(time, df = 18 * 16)127 -0.57223 1.47714 -0.387 0.69847
## ns(time, df = 18 * 16)128 1.83656 1.29985 1.413 0.15768
## ns(time, df = 18 * 16)129 0.76617 1.28726 0.595 0.55171
## ns(time, df = 18 * 16)130 1.71447 1.23803 1.385 0.16610
## ns(time, df = 18 * 16)131 0.88241 1.26332 0.698 0.48488
## ns(time, df = 18 * 16)132 1.57050 1.22560 1.281 0.20005
## ns(time, df = 18 * 16)133 1.34652 1.23283 1.092 0.27474
## ns(time, df = 18 * 16)134 1.21789 1.26136 0.966 0.33428
## ns(time, df = 18 * 16)135 1.38109 1.31414 1.051 0.29328
## ns(time, df = 18 * 16)136 -0.12885 1.43728 -0.090 0.92857
## ns(time, df = 18 * 16)137 1.93242 1.29327 1.494 0.13512
## ns(time, df = 18 * 16)138 0.58356 1.34596 0.434 0.66460
## ns(time, df = 18 * 16)139 0.87754 1.29611 0.677 0.49837
## ns(time, df = 18 * 16)140 2.25240 1.22837 1.834 0.06671 .
## ns(time, df = 18 * 16)141 -0.23129 1.35250 -0.171 0.86421
## ns(time, df = 18 * 16)142 2.52697 1.22691 2.060 0.03943 *
## ns(time, df = 18 * 16)143 0.36457 1.34574 0.271 0.78646
## ns(time, df = 18 * 16)144 1.38851 1.44138 0.963 0.33539
## ns(time, df = 18 * 16)145 -1.48731 1.63128 -0.912 0.36190
## ns(time, df = 18 * 16)146 2.94253 1.22167 2.409 0.01601 *
## ns(time, df = 18 * 16)147 0.62272 1.22823 0.507 0.61215
## ns(time, df = 18 * 16)148 2.36699 1.20285 1.968 0.04909 *
## ns(time, df = 18 * 16)149 -0.23978 1.32971 -0.180 0.85690
## ns(time, df = 18 * 16)150 2.43767 1.17690 2.071 0.03834 *
## ns(time, df = 18 * 16)151 1.20069 1.19919 1.001 0.31670
## ns(time, df = 18 * 16)152 1.45335 1.22684 1.185 0.23617
## ns(time, df = 18 * 16)153 0.94973 1.24681 0.762 0.44622
## ns(time, df = 18 * 16)154 1.47813 1.16577 1.268 0.20482
## ns(time, df = 18 * 16)155 2.55606 1.11394 2.295 0.02176 *
## ns(time, df = 18 * 16)156 0.71103 1.17506 0.605 0.54511
## ns(time, df = 18 * 16)157 2.62793 1.12930 2.327 0.01996 *
## ns(time, df = 18 * 16)158 0.60327 1.20043 0.503 0.61528
## ns(time, df = 18 * 16)159 2.24811 1.14388 1.965 0.04938 *
## ns(time, df = 18 * 16)160 1.72127 1.19354 1.442 0.14926
## ns(time, df = 18 * 16)161 0.08219 1.35106 0.061 0.95149
## ns(time, df = 18 * 16)162 2.10157 1.28784 1.632 0.10271
## ns(time, df = 18 * 16)163 -0.05560 1.44199 -0.039 0.96924
## ns(time, df = 18 * 16)164 1.10734 1.38226 0.801 0.42307
## ns(time, df = 18 * 16)165 0.89178 1.33017 0.670 0.50258
## ns(time, df = 18 * 16)166 2.01868 1.34551 1.500 0.13353
## ns(time, df = 18 * 16)167 -1.61082 1.70667 -0.944 0.34525
## ns(time, df = 18 * 16)168 2.25136 1.35605 1.660 0.09687 .
## ns(time, df = 18 * 16)169 -0.28818 1.32532 -0.217 0.82786
## ns(time, df = 18 * 16)170 3.40780 1.14808 2.968 0.00299 **
## ns(time, df = 18 * 16)171 0.45733 1.29374 0.353 0.72372
## ns(time, df = 18 * 16)172 0.02364 1.42409 0.017 0.98676
## ns(time, df = 18 * 16)173 1.83555 1.21023 1.517 0.12934
## ns(time, df = 18 * 16)174 2.11933 1.17710 1.800 0.07179 .
## ns(time, df = 18 * 16)175 0.74742 1.32210 0.565 0.57185
## ns(time, df = 18 * 16)176 -0.03741 1.39024 -0.027 0.97853
## ns(time, df = 18 * 16)177 2.36511 1.18497 1.996 0.04594 *
## ns(time, df = 18 * 16)178 1.18273 1.18012 1.002 0.31624
## ns(time, df = 18 * 16)179 1.83925 1.15301 1.595 0.11067
## ns(time, df = 18 * 16)180 1.46316 1.14266 1.280 0.20038
## ns(time, df = 18 * 16)181 2.31547 1.12429 2.060 0.03945 *
## ns(time, df = 18 * 16)182 0.87874 1.18660 0.741 0.45896
## ns(time, df = 18 * 16)183 2.33161 1.17527 1.984 0.04727 *
## ns(time, df = 18 * 16)184 0.06183 1.28173 0.048 0.96152
## ns(time, df = 18 * 16)185 2.68037 1.19624 2.241 0.02505 *
## ns(time, df = 18 * 16)186 -0.23555 1.31504 -0.179 0.85784
## ns(time, df = 18 * 16)187 2.46652 1.17650 2.096 0.03604 *
## ns(time, df = 18 * 16)188 1.02028 1.19936 0.851 0.39495
## ns(time, df = 18 * 16)189 2.28540 1.23198 1.855 0.06359 .
## ns(time, df = 18 * 16)190 -0.99332 1.50013 -0.662 0.50787
## ns(time, df = 18 * 16)191 2.09714 1.24973 1.678 0.09333 .
## ns(time, df = 18 * 16)192 1.30192 1.22247 1.065 0.28688
## ns(time, df = 18 * 16)193 1.73833 1.27415 1.364 0.17247
## ns(time, df = 18 * 16)194 -0.56625 1.46640 -0.386 0.69939
## ns(time, df = 18 * 16)195 2.41868 1.30891 1.848 0.06462 .
## ns(time, df = 18 * 16)196 -0.56891 1.45387 -0.391 0.69557
## ns(time, df = 18 * 16)197 1.74392 1.22949 1.418 0.15607
## ns(time, df = 18 * 16)198 1.93945 1.15763 1.675 0.09386 .
## ns(time, df = 18 * 16)199 1.37537 1.18149 1.164 0.24439
## ns(time, df = 18 * 16)200 1.50610 1.18746 1.268 0.20468
## ns(time, df = 18 * 16)201 1.80984 1.21348 1.491 0.13585
## ns(time, df = 18 * 16)202 0.23664 1.33774 0.177 0.85959
## ns(time, df = 18 * 16)203 1.82541 1.27373 1.433 0.15182
## ns(time, df = 18 * 16)204 0.64405 1.34180 0.480 0.63123
## ns(time, df = 18 * 16)205 1.26623 1.37999 0.918 0.35885
## ns(time, df = 18 * 16)206 -0.40411 1.43559 -0.281 0.77833
## ns(time, df = 18 * 16)207 2.75637 1.22100 2.257 0.02398 *
## ns(time, df = 18 * 16)208 0.20825 1.30728 0.159 0.87343
## ns(time, df = 18 * 16)209 1.80301 1.26062 1.430 0.15264
## ns(time, df = 18 * 16)210 0.57720 1.29589 0.445 0.65602
## ns(time, df = 18 * 16)211 1.68642 1.21647 1.386 0.16565
## ns(time, df = 18 * 16)212 1.47743 1.20207 1.229 0.21904
## ns(time, df = 18 * 16)213 1.44945 1.22455 1.184 0.23655
## ns(time, df = 18 * 16)214 1.17531 1.28423 0.915 0.36009
## ns(time, df = 18 * 16)215 0.71573 1.35075 0.530 0.59620
## ns(time, df = 18 * 16)216 1.27736 1.34817 0.947 0.34340
## ns(time, df = 18 * 16)217 0.38193 1.40602 0.272 0.78590
## ns(time, df = 18 * 16)218 1.09178 1.30044 0.840 0.40116
## ns(time, df = 18 * 16)219 1.63678 1.20610 1.357 0.17475
## ns(time, df = 18 * 16)220 1.67696 1.19706 1.401 0.16124
## ns(time, df = 18 * 16)221 0.81525 1.23178 0.662 0.50807
## ns(time, df = 18 * 16)222 2.11749 1.16650 1.815 0.06948 .
## ns(time, df = 18 * 16)223 2.01008 1.25307 1.604 0.10869
## ns(time, df = 18 * 16)224 -1.59669 1.74891 -0.913 0.36126
## ns(time, df = 18 * 16)225 1.22017 1.38644 0.880 0.37882
## ns(time, df = 18 * 16)226 1.57858 1.22822 1.285 0.19870
## ns(time, df = 18 * 16)227 1.66190 1.20400 1.380 0.16749
## ns(time, df = 18 * 16)228 1.02741 1.24315 0.826 0.40854
## ns(time, df = 18 * 16)229 1.31770 1.20967 1.089 0.27602
## ns(time, df = 18 * 16)230 1.90100 1.16363 1.634 0.10232
## ns(time, df = 18 * 16)231 1.25390 1.17423 1.068 0.28559
## ns(time, df = 18 * 16)232 1.95678 1.14627 1.707 0.08781 .
## ns(time, df = 18 * 16)233 1.46136 1.16505 1.254 0.20972
## ns(time, df = 18 * 16)234 1.41689 1.15739 1.224 0.22087
## ns(time, df = 18 * 16)235 2.60747 1.15241 2.263 0.02366 *
## ns(time, df = 18 * 16)236 -0.51848 1.33253 -0.389 0.69720
## ns(time, df = 18 * 16)237 2.66518 1.16891 2.280 0.02260 *
## ns(time, df = 18 * 16)238 0.93713 1.20038 0.781 0.43499
## ns(time, df = 18 * 16)239 1.69991 1.17999 1.441 0.14969
## ns(time, df = 18 * 16)240 1.73320 1.19067 1.456 0.14549
## ns(time, df = 18 * 16)241 0.64242 1.25476 0.512 0.60866
## ns(time, df = 18 * 16)242 1.88353 1.16947 1.611 0.10727
## ns(time, df = 18 * 16)243 2.31049 1.19195 1.938 0.05257 .
## ns(time, df = 18 * 16)244 -0.83026 1.46766 -0.566 0.57159
## ns(time, df = 18 * 16)245 1.93954 1.26521 1.533 0.12528
## ns(time, df = 18 * 16)246 1.03691 1.22515 0.846 0.39735
## ns(time, df = 18 * 16)247 2.03352 1.18475 1.716 0.08609 .
## ns(time, df = 18 * 16)248 0.86508 1.24191 0.697 0.48607
## ns(time, df = 18 * 16)249 1.42326 1.20870 1.178 0.23899
## ns(time, df = 18 * 16)250 1.84767 1.17910 1.567 0.11711
## ns(time, df = 18 * 16)251 1.00000 1.21198 0.825 0.40932
## ns(time, df = 18 * 16)252 1.74192 1.16255 1.498 0.13404
## ns(time, df = 18 * 16)253 1.83373 1.14344 1.604 0.10878
## ns(time, df = 18 * 16)254 1.49633 1.16145 1.288 0.19763
## ns(time, df = 18 * 16)255 1.99332 1.19371 1.670 0.09495 .
## ns(time, df = 18 * 16)256 0.03272 1.34253 0.024 0.98056
## ns(time, df = 18 * 16)257 1.81298 1.23325 1.470 0.14154
## ns(time, df = 18 * 16)258 1.18274 1.21369 0.975 0.32981
## ns(time, df = 18 * 16)259 1.65544 1.17686 1.407 0.15953
## ns(time, df = 18 * 16)260 1.53351 1.15401 1.329 0.18389
## ns(time, df = 18 * 16)261 2.26178 1.15072 1.966 0.04935 *
## ns(time, df = 18 * 16)262 0.45035 1.26412 0.356 0.72165
## ns(time, df = 18 * 16)263 2.14164 1.26228 1.697 0.08977 .
## ns(time, df = 18 * 16)264 -0.68797 1.42904 -0.481 0.63022
## ns(time, df = 18 * 16)265 2.34642 1.18665 1.977 0.04800 *
## ns(time, df = 18 * 16)266 1.47031 1.15410 1.274 0.20267
## ns(time, df = 18 * 16)267 2.00213 1.14965 1.742 0.08159 .
## ns(time, df = 18 * 16)268 1.33481 1.22043 1.094 0.27408
## ns(time, df = 18 * 16)269 0.72896 1.31698 0.554 0.57992
## ns(time, df = 18 * 16)270 0.89387 1.25722 0.711 0.47709
## ns(time, df = 18 * 16)271 2.35769 1.15451 2.042 0.04113 *
## ns(time, df = 18 * 16)272 1.50017 1.22094 1.229 0.21919
## ns(time, df = 18 * 16)273 0.51514 1.43918 0.358 0.72039
## ns(time, df = 18 * 16)274 -0.52178 1.53576 -0.340 0.73404
## ns(time, df = 18 * 16)275 2.26470 1.24963 1.812 0.06994 .
## ns(time, df = 18 * 16)276 0.85700 1.24074 0.691 0.48974
## ns(time, df = 18 * 16)277 1.98254 1.21142 1.637 0.10173
## ns(time, df = 18 * 16)278 0.67503 1.29503 0.521 0.60220
## ns(time, df = 18 * 16)279 0.87790 1.23835 0.709 0.47837
## ns(time, df = 18 * 16)280 2.60369 1.12888 2.306 0.02109 *
## ns(time, df = 18 * 16)281 1.03171 1.16949 0.882 0.37768
## ns(time, df = 18 * 16)282 1.87085 1.14139 1.639 0.10119
## ns(time, df = 18 * 16)283 1.66261 1.12278 1.481 0.13866
## ns(time, df = 18 * 16)284 2.21250 1.10891 1.995 0.04602 *
## ns(time, df = 18 * 16)285 1.42631 1.15716 1.233 0.21773
## ns(time, df = 18 * 16)286 0.34126 0.92566 0.369 0.71238
## ns(time, df = 18 * 16)287 3.90707 2.09300 1.867 0.06194 .
## ns(time, df = 18 * 16)288 -0.20846 0.64851 -0.321 0.74788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(33776.55) family taken to be 1)
##
## Null deviance: 1179.11 on 938 degrees of freedom
## Residual deviance: 828.59 on 650 degrees of freedom
## AIC: 3556.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 33777
## Std. Err.: 149929
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2976.458## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 18), data = week,
## init.theta = 36901.64025, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.74944 -0.71174 -0.07771 0.50960 2.54735
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.572375 0.955654 -0.599 0.54922
## ns(time, df = 18 * 18)1 1.233831 1.038782 1.188 0.23493
## ns(time, df = 18 * 18)2 1.459796 1.415150 1.032 0.30228
## ns(time, df = 18 * 18)3 1.577896 1.370352 1.151 0.24955
## ns(time, df = 18 * 18)4 -1.404053 1.992487 -0.705 0.48101
## ns(time, df = 18 * 18)5 0.688440 1.547690 0.445 0.65645
## ns(time, df = 18 * 18)6 1.996469 1.366873 1.461 0.14412
## ns(time, df = 18 * 18)7 0.849645 1.440704 0.590 0.55536
## ns(time, df = 18 * 18)8 0.174656 1.581702 0.110 0.91207
## ns(time, df = 18 * 18)9 1.017026 1.460909 0.696 0.48633
## ns(time, df = 18 * 18)10 1.213363 1.392267 0.872 0.38348
## ns(time, df = 18 * 18)11 0.625962 1.321251 0.474 0.63567
## ns(time, df = 18 * 18)12 2.978371 1.196984 2.488 0.01284 *
## ns(time, df = 18 * 18)13 0.470559 1.275968 0.369 0.71229
## ns(time, df = 18 * 18)14 2.297328 1.206779 1.904 0.05695 .
## ns(time, df = 18 * 18)15 1.676171 1.231625 1.361 0.17353
## ns(time, df = 18 * 18)16 0.956468 1.297948 0.737 0.46118
## ns(time, df = 18 * 18)17 1.932379 1.276135 1.514 0.12996
## ns(time, df = 18 * 18)18 0.907298 1.360552 0.667 0.50486
## ns(time, df = 18 * 18)19 0.593816 1.346982 0.441 0.65932
## ns(time, df = 18 * 18)20 3.198260 1.273795 2.511 0.01205 *
## ns(time, df = 18 * 18)21 -1.001117 1.642971 -0.609 0.54230
## ns(time, df = 18 * 18)22 0.300846 1.496623 0.201 0.84069
## ns(time, df = 18 * 18)23 3.021104 1.303458 2.318 0.02046 *
## ns(time, df = 18 * 18)24 -0.564661 1.523216 -0.371 0.71086
## ns(time, df = 18 * 18)25 1.682676 1.441419 1.167 0.24306
## ns(time, df = 18 * 18)26 0.838173 1.555291 0.539 0.58994
## ns(time, df = 18 * 18)27 -0.319455 1.902839 -0.168 0.86668
## ns(time, df = 18 * 18)28 -0.461869 1.934220 -0.239 0.81127
## ns(time, df = 18 * 18)29 0.743950 1.550995 0.480 0.63147
## ns(time, df = 18 * 18)30 1.629875 1.359666 1.199 0.23063
## ns(time, df = 18 * 18)31 0.944374 1.329626 0.710 0.47755
## ns(time, df = 18 * 18)32 2.128258 1.288002 1.652 0.09846 .
## ns(time, df = 18 * 18)33 0.173486 1.391188 0.125 0.90076
## ns(time, df = 18 * 18)34 1.835559 1.249068 1.470 0.14169
## ns(time, df = 18 * 18)35 2.345114 1.217048 1.927 0.05399 .
## ns(time, df = 18 * 18)36 0.668228 1.357145 0.492 0.62245
## ns(time, df = 18 * 18)37 0.850667 1.396046 0.609 0.54230
## ns(time, df = 18 * 18)38 1.504600 1.323897 1.136 0.25575
## ns(time, df = 18 * 18)39 1.253052 1.310907 0.956 0.33914
## ns(time, df = 18 * 18)40 1.513126 1.300110 1.164 0.24449
## ns(time, df = 18 * 18)41 1.582892 1.360857 1.163 0.24477
## ns(time, df = 18 * 18)42 -0.278257 1.568839 -0.177 0.85922
## ns(time, df = 18 * 18)43 1.352642 1.385597 0.976 0.32896
## ns(time, df = 18 * 18)44 1.998198 1.344787 1.486 0.13731
## ns(time, df = 18 * 18)45 -0.417761 1.544074 -0.271 0.78673
## ns(time, df = 18 * 18)46 1.743712 1.367129 1.275 0.20215
## ns(time, df = 18 * 18)47 1.348175 1.370668 0.984 0.32532
## ns(time, df = 18 * 18)48 0.152201 1.452034 0.105 0.91652
## ns(time, df = 18 * 18)49 2.159177 1.323215 1.632 0.10273
## ns(time, df = 18 * 18)50 0.442903 1.389311 0.319 0.74988
## ns(time, df = 18 * 18)51 1.778668 1.339328 1.328 0.18417
## ns(time, df = 18 * 18)52 0.897207 1.417795 0.633 0.52685
## ns(time, df = 18 * 18)53 0.160290 1.443259 0.111 0.91157
## ns(time, df = 18 * 18)54 2.344281 1.250861 1.874 0.06091 .
## ns(time, df = 18 * 18)55 1.481805 1.267518 1.169 0.24238
## ns(time, df = 18 * 18)56 0.808937 1.322015 0.612 0.54061
## ns(time, df = 18 * 18)57 1.886203 1.244704 1.515 0.12968
## ns(time, df = 18 * 18)58 1.730130 1.245602 1.389 0.16484
## ns(time, df = 18 * 18)59 1.538312 1.340071 1.148 0.25100
## ns(time, df = 18 * 18)60 -0.313656 1.563977 -0.201 0.84105
## ns(time, df = 18 * 18)61 1.586126 1.403686 1.130 0.25849
## ns(time, df = 18 * 18)62 1.090864 1.397903 0.780 0.43518
## ns(time, df = 18 * 18)63 0.639698 1.414760 0.452 0.65115
## ns(time, df = 18 * 18)64 1.811404 1.336907 1.355 0.17544
## ns(time, df = 18 * 18)65 0.675030 1.398044 0.483 0.62921
## ns(time, df = 18 * 18)66 1.330190 1.383842 0.961 0.33644
## ns(time, df = 18 * 18)67 0.737317 1.384778 0.532 0.59442
## ns(time, df = 18 * 18)68 2.089424 1.347554 1.551 0.12101
## ns(time, df = 18 * 18)69 0.443124 1.602351 0.277 0.78213
## ns(time, df = 18 * 18)70 -1.398410 1.991020 -0.702 0.48246
## ns(time, df = 18 * 18)71 2.146239 1.616844 1.327 0.18437
## ns(time, df = 18 * 18)72 -0.990340 1.824607 -0.543 0.58729
## ns(time, df = 18 * 18)73 1.441056 1.646414 0.875 0.38143
## ns(time, df = 18 * 18)74 -0.845236 1.678162 -0.504 0.61449
## ns(time, df = 18 * 18)75 2.421710 1.346344 1.799 0.07206 .
## ns(time, df = 18 * 18)76 0.521804 1.358359 0.384 0.70087
## ns(time, df = 18 * 18)77 1.851727 1.276882 1.450 0.14700
## ns(time, df = 18 * 18)78 1.514220 1.287910 1.176 0.23971
## ns(time, df = 18 * 18)79 0.869155 1.345106 0.646 0.51818
## ns(time, df = 18 * 18)80 1.544632 1.303153 1.185 0.23590
## ns(time, df = 18 * 18)81 1.350671 1.296815 1.042 0.29763
## ns(time, df = 18 * 18)82 1.390810 1.294545 1.074 0.28266
## ns(time, df = 18 * 18)83 1.882019 1.336688 1.408 0.15914
## ns(time, df = 18 * 18)84 0.020415 1.636356 0.012 0.99005
## ns(time, df = 18 * 18)85 -0.503235 1.789534 -0.281 0.77855
## ns(time, df = 18 * 18)86 1.650999 1.519858 1.086 0.27735
## ns(time, df = 18 * 18)87 -0.020864 1.544747 -0.014 0.98922
## ns(time, df = 18 * 18)88 1.702213 1.390978 1.224 0.22105
## ns(time, df = 18 * 18)89 0.863501 1.410340 0.612 0.54036
## ns(time, df = 18 * 18)90 1.050835 1.425486 0.737 0.46101
## ns(time, df = 18 * 18)91 0.638380 1.398080 0.457 0.64795
## ns(time, df = 18 * 18)92 2.016183 1.286841 1.567 0.11717
## ns(time, df = 18 * 18)93 0.838826 1.305302 0.643 0.52046
## ns(time, df = 18 * 18)94 1.909313 1.234634 1.546 0.12199
## ns(time, df = 18 * 18)95 1.906103 1.242835 1.534 0.12511
## ns(time, df = 18 * 18)96 0.844864 1.362784 0.620 0.53529
## ns(time, df = 18 * 18)97 0.646439 1.378520 0.469 0.63911
## ns(time, df = 18 * 18)98 2.050080 1.254765 1.634 0.10229
## ns(time, df = 18 * 18)99 1.563870 1.269103 1.232 0.21785
## ns(time, df = 18 * 18)100 0.945672 1.335079 0.708 0.47874
## ns(time, df = 18 * 18)101 1.460277 1.315146 1.110 0.26685
## ns(time, df = 18 * 18)102 1.356946 1.326749 1.023 0.30642
## ns(time, df = 18 * 18)103 0.985941 1.369464 0.720 0.47156
## ns(time, df = 18 * 18)104 0.998147 1.336473 0.747 0.45515
## ns(time, df = 18 * 18)105 2.084770 1.272377 1.638 0.10132
## ns(time, df = 18 * 18)106 0.677996 1.342178 0.505 0.61346
## ns(time, df = 18 * 18)107 2.115303 1.351401 1.565 0.11752
## ns(time, df = 18 * 18)108 -0.065553 1.643095 -0.040 0.96818
## ns(time, df = 18 * 18)109 -0.150534 1.874619 -0.080 0.93600
## ns(time, df = 18 * 18)110 -0.234815 1.606209 -0.146 0.88377
## ns(time, df = 18 * 18)111 3.121197 1.352813 2.307 0.02104 *
## ns(time, df = 18 * 18)112 -1.075500 1.608947 -0.668 0.50385
## ns(time, df = 18 * 18)113 1.963200 1.421716 1.381 0.16732
## ns(time, df = 18 * 18)114 0.185108 1.435950 0.129 0.89743
## ns(time, df = 18 * 18)115 1.834146 1.274957 1.439 0.15027
## ns(time, df = 18 * 18)116 2.154584 1.261867 1.707 0.08774 .
## ns(time, df = 18 * 18)117 -0.271378 1.408956 -0.193 0.84726
## ns(time, df = 18 * 18)118 2.996508 1.277310 2.346 0.01898 *
## ns(time, df = 18 * 18)119 -0.288592 1.461715 -0.197 0.84349
## ns(time, df = 18 * 18)120 1.591179 1.394165 1.141 0.25374
## ns(time, df = 18 * 18)121 0.849391 1.408004 0.603 0.54634
## ns(time, df = 18 * 18)122 0.958516 1.378305 0.695 0.48679
## ns(time, df = 18 * 18)123 1.731016 1.317152 1.314 0.18878
## ns(time, df = 18 * 18)124 0.883459 1.367597 0.646 0.51828
## ns(time, df = 18 * 18)125 1.112265 1.350939 0.823 0.41032
## ns(time, df = 18 * 18)126 1.489603 1.293163 1.152 0.24936
## ns(time, df = 18 * 18)127 1.601377 1.277255 1.254 0.20993
## ns(time, df = 18 * 18)128 1.338558 1.317336 1.016 0.30958
## ns(time, df = 18 * 18)129 0.894741 1.367146 0.654 0.51282
## ns(time, df = 18 * 18)130 1.449943 1.343998 1.079 0.28066
## ns(time, df = 18 * 18)131 0.676434 1.330283 0.508 0.61111
## ns(time, df = 18 * 18)132 2.424270 1.223321 1.982 0.04751 *
## ns(time, df = 18 * 18)133 0.910457 1.252816 0.727 0.46739
## ns(time, df = 18 * 18)134 2.541700 1.224027 2.077 0.03785 *
## ns(time, df = 18 * 18)135 0.422981 1.359465 0.311 0.75570
## ns(time, df = 18 * 18)136 1.387289 1.347645 1.029 0.30328
## ns(time, df = 18 * 18)137 1.187396 1.329159 0.893 0.37167
## ns(time, df = 18 * 18)138 1.379947 1.297045 1.064 0.28737
## ns(time, df = 18 * 18)139 1.846021 1.296185 1.424 0.15439
## ns(time, df = 18 * 18)140 0.434343 1.418658 0.306 0.75948
## ns(time, df = 18 * 18)141 1.287088 1.375737 0.936 0.34950
## ns(time, df = 18 * 18)142 1.573912 1.390208 1.132 0.25757
## ns(time, df = 18 * 18)143 -0.256522 1.564300 -0.164 0.86974
## ns(time, df = 18 * 18)144 1.558857 1.394281 1.118 0.26355
## ns(time, df = 18 * 18)145 1.112998 1.362282 0.817 0.41392
## ns(time, df = 18 * 18)146 1.122786 1.339452 0.838 0.40189
## ns(time, df = 18 * 18)147 1.767332 1.310120 1.349 0.17734
## ns(time, df = 18 * 18)148 0.470037 1.363885 0.345 0.73037
## ns(time, df = 18 * 18)149 2.212222 1.277104 1.732 0.08323 .
## ns(time, df = 18 * 18)150 0.713210 1.344229 0.531 0.59572
## ns(time, df = 18 * 18)151 1.575934 1.329408 1.185 0.23584
## ns(time, df = 18 * 18)152 1.350899 1.405062 0.961 0.33633
## ns(time, df = 18 * 18)153 -0.565186 1.590504 -0.355 0.72233
## ns(time, df = 18 * 18)154 2.305777 1.370254 1.683 0.09243 .
## ns(time, df = 18 * 18)155 0.224815 1.452282 0.155 0.87698
## ns(time, df = 18 * 18)156 1.340614 1.383107 0.969 0.33241
## ns(time, df = 18 * 18)157 1.121879 1.335832 0.840 0.40100
## ns(time, df = 18 * 18)158 1.914973 1.318259 1.453 0.14632
## ns(time, df = 18 * 18)159 -0.067585 1.426930 -0.047 0.96222
## ns(time, df = 18 * 18)160 2.543207 1.295712 1.963 0.04967 *
## ns(time, df = 18 * 18)161 0.413045 1.427457 0.289 0.77231
## ns(time, df = 18 * 18)162 1.152590 1.535727 0.751 0.45294
## ns(time, df = 18 * 18)163 -0.608935 1.709588 -0.356 0.72170
## ns(time, df = 18 * 18)164 1.270579 1.379115 0.921 0.35689
## ns(time, df = 18 * 18)165 2.257614 1.253924 1.800 0.07179 .
## ns(time, df = 18 * 18)166 0.758837 1.306226 0.581 0.56128
## ns(time, df = 18 * 18)167 2.227136 1.293358 1.722 0.08507 .
## ns(time, df = 18 * 18)168 -0.349509 1.427264 -0.245 0.80655
## ns(time, df = 18 * 18)169 2.639297 1.239603 2.129 0.03324 *
## ns(time, df = 18 * 18)170 1.194980 1.274762 0.937 0.34855
## ns(time, df = 18 * 18)171 1.180686 1.315003 0.898 0.36926
## ns(time, df = 18 * 18)172 1.547986 1.307707 1.184 0.23652
## ns(time, df = 18 * 18)173 0.614332 1.303170 0.471 0.63735
## ns(time, df = 18 * 18)174 2.738500 1.182746 2.315 0.02059 *
## ns(time, df = 18 * 18)175 1.300389 1.218824 1.067 0.28601
## ns(time, df = 18 * 18)176 1.632753 1.219925 1.338 0.18076
## ns(time, df = 18 * 18)177 2.067206 1.212012 1.706 0.08808 .
## ns(time, df = 18 * 18)178 0.933203 1.267124 0.736 0.46144
## ns(time, df = 18 * 18)179 2.015135 1.215206 1.658 0.09726 .
## ns(time, df = 18 * 18)180 2.064368 1.248306 1.654 0.09818 .
## ns(time, df = 18 * 18)181 -0.086545 1.443406 -0.060 0.95219
## ns(time, df = 18 * 18)182 1.967644 1.360461 1.446 0.14809
## ns(time, df = 18 * 18)183 0.570310 1.462672 0.390 0.69660
## ns(time, df = 18 * 18)184 0.629963 1.513434 0.416 0.67723
## ns(time, df = 18 * 18)185 0.974681 1.449194 0.673 0.50122
## ns(time, df = 18 * 18)186 1.140757 1.395721 0.817 0.41374
## ns(time, df = 18 * 18)187 1.794176 1.448870 1.238 0.21559
## ns(time, df = 18 * 18)188 -1.546998 1.859399 -0.832 0.40542
## ns(time, df = 18 * 18)189 1.864665 1.469518 1.269 0.20448
## ns(time, df = 18 * 18)190 0.441512 1.409957 0.313 0.75418
## ns(time, df = 18 * 18)191 1.758363 1.249699 1.407 0.15942
## ns(time, df = 18 * 18)192 2.989381 1.249263 2.393 0.01672 *
## ns(time, df = 18 * 18)193 -1.661740 1.696375 -0.980 0.32729
## ns(time, df = 18 * 18)194 1.740682 1.387661 1.254 0.20970
## ns(time, df = 18 * 18)195 1.290932 1.276290 1.011 0.31179
## ns(time, df = 18 * 18)196 2.466196 1.243269 1.984 0.04730 *
## ns(time, df = 18 * 18)197 0.507938 1.437304 0.353 0.72379
## ns(time, df = 18 * 18)198 -0.328913 1.546801 -0.213 0.83161
## ns(time, df = 18 * 18)199 2.423840 1.274729 1.901 0.05724 .
## ns(time, df = 18 * 18)200 1.067806 1.261982 0.846 0.39748
## ns(time, df = 18 * 18)201 2.053829 1.222341 1.680 0.09291 .
## ns(time, df = 18 * 18)202 1.211527 1.236636 0.980 0.32724
## ns(time, df = 18 * 18)203 2.063695 1.193517 1.729 0.08379 .
## ns(time, df = 18 * 18)204 1.928987 1.207590 1.597 0.11018
## ns(time, df = 18 * 18)205 0.925549 1.265240 0.732 0.46446
## ns(time, df = 18 * 18)206 2.539979 1.246851 2.037 0.04164 *
## ns(time, df = 18 * 18)207 -0.399444 1.403423 -0.285 0.77593
## ns(time, df = 18 * 18)208 2.968568 1.263620 2.349 0.01881 *
## ns(time, df = 18 * 18)209 -0.073588 1.407437 -0.052 0.95830
## ns(time, df = 18 * 18)210 1.514432 1.297092 1.168 0.24298
## ns(time, df = 18 * 18)211 2.144024 1.236572 1.734 0.08295 .
## ns(time, df = 18 * 18)212 0.836368 1.298746 0.644 0.51959
## ns(time, df = 18 * 18)213 2.495645 1.331375 1.874 0.06086 .
## ns(time, df = 18 * 18)214 -1.732228 1.718138 -1.008 0.31336
## ns(time, df = 18 * 18)215 2.520601 1.327168 1.899 0.05753 .
## ns(time, df = 18 * 18)216 0.964574 1.302314 0.741 0.45890
## ns(time, df = 18 * 18)217 2.272942 1.320888 1.721 0.08529 .
## ns(time, df = 18 * 18)218 -0.914603 1.596465 -0.573 0.56672
## ns(time, df = 18 * 18)219 2.232910 1.396081 1.599 0.10973
## ns(time, df = 18 * 18)220 0.191433 1.491329 0.128 0.89786
## ns(time, df = 18 * 18)221 0.922500 1.424117 0.648 0.51713
## ns(time, df = 18 * 18)222 1.355678 1.288646 1.052 0.29279
## ns(time, df = 18 * 18)223 2.224751 1.216179 1.829 0.06736 .
## ns(time, df = 18 * 18)224 1.222407 1.260273 0.970 0.33207
## ns(time, df = 18 * 18)225 1.483275 1.263281 1.174 0.24034
## ns(time, df = 18 * 18)226 1.853115 1.268403 1.461 0.14402
## ns(time, df = 18 * 18)227 0.857312 1.381967 0.620 0.53502
## ns(time, df = 18 * 18)228 0.550627 1.418890 0.388 0.69796
## ns(time, df = 18 * 18)229 2.140188 1.342410 1.594 0.11087
## ns(time, df = 18 * 18)230 -0.011672 1.498700 -0.008 0.99379
## ns(time, df = 18 * 18)231 1.555071 1.469420 1.058 0.28992
## ns(time, df = 18 * 18)232 -0.484329 1.523560 -0.318 0.75057
## ns(time, df = 18 * 18)233 2.818418 1.289496 2.186 0.02884 *
## ns(time, df = 18 * 18)234 0.338497 1.379451 0.245 0.80616
## ns(time, df = 18 * 18)235 1.531118 1.351982 1.132 0.25742
## ns(time, df = 18 * 18)236 0.996157 1.363968 0.730 0.46518
## ns(time, df = 18 * 18)237 1.119637 1.332331 0.840 0.40071
## ns(time, df = 18 * 18)238 1.730063 1.271302 1.361 0.17356
## ns(time, df = 18 * 18)239 1.402532 1.281359 1.095 0.27371
## ns(time, df = 18 * 18)240 1.412508 1.308611 1.079 0.28041
## ns(time, df = 18 * 18)241 1.262592 1.372496 0.920 0.35761
## ns(time, df = 18 * 18)242 0.360653 1.460370 0.247 0.80494
## ns(time, df = 18 * 18)243 1.893715 1.412956 1.340 0.18016
## ns(time, df = 18 * 18)244 -0.487104 1.581988 -0.308 0.75815
## ns(time, df = 18 * 18)245 1.803655 1.383345 1.304 0.19229
## ns(time, df = 18 * 18)246 0.678954 1.348085 0.504 0.61451
## ns(time, df = 18 * 18)247 2.144035 1.251293 1.713 0.08663 .
## ns(time, df = 18 * 18)248 1.274348 1.289523 0.988 0.32304
## ns(time, df = 18 * 18)249 0.897287 1.312521 0.684 0.49420
## ns(time, df = 18 * 18)250 2.172850 1.235586 1.759 0.07865 .
## ns(time, df = 18 * 18)251 2.301574 1.352953 1.701 0.08892 .
## ns(time, df = 18 * 18)252 -3.238658 2.203370 -1.470 0.14160
## ns(time, df = 18 * 18)253 2.714385 1.487980 1.824 0.06812 .
## ns(time, df = 18 * 18)254 -0.489835 1.441942 -0.340 0.73408
## ns(time, df = 18 * 18)255 3.416249 1.245640 2.743 0.00610 **
## ns(time, df = 18 * 18)256 -0.464419 1.398674 -0.332 0.73986
## ns(time, df = 18 * 18)257 2.466871 1.275371 1.934 0.05308 .
## ns(time, df = 18 * 18)258 0.472360 1.313830 0.360 0.71920
## ns(time, df = 18 * 18)259 2.345585 1.223109 1.918 0.05515 .
## ns(time, df = 18 * 18)260 1.246055 1.252114 0.995 0.31966
## ns(time, df = 18 * 18)261 1.420450 1.236171 1.149 0.25053
## ns(time, df = 18 * 18)262 2.510582 1.212219 2.071 0.03835 *
## ns(time, df = 18 * 18)263 -0.122891 1.312241 -0.094 0.92539
## ns(time, df = 18 * 18)264 3.685789 1.195265 3.084 0.00204 **
## ns(time, df = 18 * 18)265 -0.486764 1.392788 -0.349 0.72672
## ns(time, df = 18 * 18)266 1.866100 1.292267 1.444 0.14872
## ns(time, df = 18 * 18)267 1.475517 1.255974 1.175 0.24008
## ns(time, df = 18 * 18)268 1.678485 1.250160 1.343 0.17940
## ns(time, df = 18 * 18)269 1.278911 1.263218 1.012 0.31134
## ns(time, df = 18 * 18)270 1.988484 1.255439 1.584 0.11322
## ns(time, df = 18 * 18)271 0.650348 1.332587 0.488 0.62553
## ns(time, df = 18 * 18)272 1.708320 1.262040 1.354 0.17586
## ns(time, df = 18 * 18)273 1.813212 1.236726 1.466 0.14261
## ns(time, df = 18 * 18)274 2.071083 1.334585 1.552 0.12070
## ns(time, df = 18 * 18)275 -2.169006 1.798853 -1.206 0.22791
## ns(time, df = 18 * 18)276 3.133460 1.316757 2.380 0.01733 *
## ns(time, df = 18 * 18)277 0.217420 1.337251 0.163 0.87084
## ns(time, df = 18 * 18)278 2.506907 1.249419 2.006 0.04481 *
## ns(time, df = 18 * 18)279 0.651918 1.330157 0.490 0.62406
## ns(time, df = 18 * 18)280 1.421339 1.296614 1.096 0.27299
## ns(time, df = 18 * 18)281 1.731784 1.255296 1.380 0.16772
## ns(time, df = 18 * 18)282 1.394547 1.272111 1.096 0.27297
## ns(time, df = 18 * 18)283 1.280374 1.271371 1.007 0.31390
## ns(time, df = 18 * 18)284 1.759799 1.222533 1.439 0.15002
## ns(time, df = 18 * 18)285 1.949845 1.211336 1.610 0.10747
## ns(time, df = 18 * 18)286 1.167567 1.245102 0.938 0.34838
## ns(time, df = 18 * 18)287 2.628486 1.262782 2.082 0.03739 *
## ns(time, df = 18 * 18)288 -1.256235 1.548593 -0.811 0.41724
## ns(time, df = 18 * 18)289 3.056307 1.301457 2.348 0.01886 *
## ns(time, df = 18 * 18)290 -0.419843 1.390003 -0.302 0.76262
## ns(time, df = 18 * 18)291 3.052311 1.232013 2.477 0.01323 *
## ns(time, df = 18 * 18)292 0.078252 1.306976 0.060 0.95226
## ns(time, df = 18 * 18)293 2.840204 1.195357 2.376 0.01750 *
## ns(time, df = 18 * 18)294 1.213868 1.262248 0.962 0.33621
## ns(time, df = 18 * 18)295 0.919422 1.337188 0.688 0.49172
## ns(time, df = 18 * 18)296 2.071795 1.348420 1.536 0.12443
## ns(time, df = 18 * 18)297 -0.893911 1.578888 -0.566 0.57128
## ns(time, df = 18 * 18)298 2.213078 1.286115 1.721 0.08530 .
## ns(time, df = 18 * 18)299 1.479675 1.232050 1.201 0.22976
## ns(time, df = 18 * 18)300 1.971262 1.208832 1.631 0.10295
## ns(time, df = 18 * 18)301 1.697172 1.244590 1.364 0.17268
## ns(time, df = 18 * 18)302 1.066669 1.341926 0.795 0.42668
## ns(time, df = 18 * 18)303 0.841172 1.399454 0.601 0.54779
## ns(time, df = 18 * 18)304 0.970388 1.323389 0.733 0.46340
## ns(time, df = 18 * 18)305 2.317833 1.218070 1.903 0.05706 .
## ns(time, df = 18 * 18)306 1.667206 1.273926 1.309 0.19063
## ns(time, df = 18 * 18)307 0.584419 1.487508 0.393 0.69440
## ns(time, df = 18 * 18)308 -0.009096 1.654113 -0.005 0.99561
## ns(time, df = 18 * 18)309 0.680304 1.447576 0.470 0.63838
## ns(time, df = 18 * 18)310 2.254735 1.284460 1.755 0.07919 .
## ns(time, df = 18 * 18)311 0.672395 1.325953 0.507 0.61208
## ns(time, df = 18 * 18)312 2.097009 1.288218 1.628 0.10356
## ns(time, df = 18 * 18)313 0.550730 1.392859 0.395 0.69255
## ns(time, df = 18 * 18)314 0.825465 1.325694 0.623 0.53350
## ns(time, df = 18 * 18)315 2.634957 1.197648 2.200 0.02780 *
## ns(time, df = 18 * 18)316 1.120627 1.236010 0.907 0.36459
## ns(time, df = 18 * 18)317 1.856875 1.219347 1.523 0.12780
## ns(time, df = 18 * 18)318 1.506846 1.209882 1.245 0.21297
## ns(time, df = 18 * 18)319 2.080473 1.176521 1.768 0.07701 .
## ns(time, df = 18 * 18)320 1.995399 1.189009 1.678 0.09331 .
## ns(time, df = 18 * 18)321 1.259673 1.250543 1.007 0.31379
## ns(time, df = 18 * 18)322 0.540276 0.980453 0.551 0.58160
## ns(time, df = 18 * 18)323 3.689444 2.208552 1.671 0.09482 .
## ns(time, df = 18 * 18)324 -0.070705 0.684392 -0.103 0.91772
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(36901.64) family taken to be 1)
##
## Null deviance: 1179.12 on 938 degrees of freedom
## Residual deviance: 786.45 on 614 degrees of freedom
## AIC: 3586.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 36902
## Std. Err.: 159515
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2934.306## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 20), data = week,
## init.theta = 40190.30999, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.69421 -0.67670 -0.03714 0.49498 2.38726
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.57185 1.01053 -0.566 0.5715
## ns(time, df = 18 * 20)1 1.90841 1.08168 1.764 0.0777 .
## ns(time, df = 18 * 20)2 0.42021 1.51835 0.277 0.7820
## ns(time, df = 18 * 20)3 3.03887 1.35424 2.244 0.0248 *
## ns(time, df = 18 * 20)4 -2.24653 2.12598 -1.057 0.2906
## ns(time, df = 18 * 20)5 1.28707 1.74814 0.736 0.4616
## ns(time, df = 18 * 20)6 -0.15937 1.62563 -0.098 0.9219
## ns(time, df = 18 * 20)7 2.81987 1.39718 2.018 0.0436 *
## ns(time, df = 18 * 20)8 -0.13261 1.61603 -0.082 0.9346
## ns(time, df = 18 * 20)9 0.67247 1.64764 0.408 0.6832
## ns(time, df = 18 * 20)10 0.83338 1.56136 0.534 0.5935
## ns(time, df = 18 * 20)11 1.16768 1.48512 0.786 0.4317
## ns(time, df = 18 * 20)12 0.80904 1.42598 0.567 0.5705
## ns(time, df = 18 * 20)13 1.96737 1.29064 1.524 0.1274
## ns(time, df = 18 * 20)14 2.18287 1.27129 1.717 0.0860 .
## ns(time, df = 18 * 20)15 0.63906 1.34524 0.475 0.6347
## ns(time, df = 18 * 20)16 2.50829 1.26466 1.983 0.0473 *
## ns(time, df = 18 * 20)17 1.38098 1.31747 1.048 0.2945
## ns(time, df = 18 * 20)18 1.06111 1.37463 0.772 0.4402
## ns(time, df = 18 * 20)19 1.83247 1.34749 1.360 0.1739
## ns(time, df = 18 * 20)20 1.22038 1.42825 0.854 0.3929
## ns(time, df = 18 * 20)21 -0.05616 1.48199 -0.038 0.9698
## ns(time, df = 18 * 20)22 3.31107 1.31403 2.520 0.0117 *
## ns(time, df = 18 * 20)23 -0.02512 1.53537 -0.016 0.9869
## ns(time, df = 18 * 20)24 0.39160 1.65098 0.237 0.8125
## ns(time, df = 18 * 20)25 0.90932 1.45285 0.626 0.5314
## ns(time, df = 18 * 20)26 2.86446 1.37258 2.087 0.0369 *
## ns(time, df = 18 * 20)27 -1.21121 1.71059 -0.708 0.4789
## ns(time, df = 18 * 20)28 2.21692 1.50928 1.469 0.1419
## ns(time, df = 18 * 20)29 0.35637 1.66376 0.214 0.8304
## ns(time, df = 18 * 20)30 0.29210 1.94941 0.150 0.8809
## ns(time, df = 18 * 20)31 -1.00176 2.21178 -0.453 0.6506
## ns(time, df = 18 * 20)32 0.54559 1.74733 0.312 0.7549
## ns(time, df = 18 * 20)33 1.37034 1.48228 0.924 0.3552
## ns(time, df = 18 * 20)34 1.29156 1.41159 0.915 0.3602
## ns(time, df = 18 * 20)35 1.26090 1.37728 0.915 0.3599
## ns(time, df = 18 * 20)36 2.00046 1.36976 1.460 0.1442
## ns(time, df = 18 * 20)37 -0.13058 1.50140 -0.087 0.9307
## ns(time, df = 18 * 20)38 2.17642 1.30155 1.672 0.0945 .
## ns(time, df = 18 * 20)39 2.21732 1.28211 1.729 0.0837 .
## ns(time, df = 18 * 20)40 0.69179 1.42684 0.485 0.6278
## ns(time, df = 18 * 20)41 1.09826 1.47094 0.747 0.4553
## ns(time, df = 18 * 20)42 0.86530 1.43867 0.601 0.5475
## ns(time, df = 18 * 20)43 1.99721 1.36510 1.463 0.1435
## ns(time, df = 18 * 20)44 0.50760 1.41446 0.359 0.7197
## ns(time, df = 18 * 20)45 2.53903 1.35583 1.873 0.0611 .
## ns(time, df = 18 * 20)46 0.01602 1.57046 0.010 0.9919
## ns(time, df = 18 * 20)47 0.72920 1.58252 0.461 0.6450
## ns(time, df = 18 * 20)48 1.07038 1.45584 0.735 0.4622
## ns(time, df = 18 * 20)49 2.07329 1.40905 1.471 0.1412
## ns(time, df = 18 * 20)50 -0.11496 1.61328 -0.071 0.9432
## ns(time, df = 18 * 20)51 0.97569 1.49525 0.653 0.5141
## ns(time, df = 18 * 20)52 2.08952 1.41076 1.481 0.1386
## ns(time, df = 18 * 20)53 -0.13052 1.56939 -0.083 0.9337
## ns(time, df = 18 * 20)54 1.67931 1.42986 1.174 0.2402
## ns(time, df = 18 * 20)55 1.24207 1.41766 0.876 0.3810
## ns(time, df = 18 * 20)56 0.99755 1.44166 0.692 0.4890
## ns(time, df = 18 * 20)57 1.46888 1.42335 1.032 0.3021
## ns(time, df = 18 * 20)58 1.09012 1.49938 0.727 0.4672
## ns(time, df = 18 * 20)59 -0.13832 1.55954 -0.089 0.9293
## ns(time, df = 18 * 20)60 2.45303 1.32015 1.858 0.0631 .
## ns(time, df = 18 * 20)61 1.42837 1.32990 1.074 0.2828
## ns(time, df = 18 * 20)62 1.19771 1.37870 0.869 0.3850
## ns(time, df = 18 * 20)63 1.23001 1.35497 0.908 0.3640
## ns(time, df = 18 * 20)64 1.99731 1.29640 1.541 0.1234
## ns(time, df = 18 * 20)65 1.66101 1.33531 1.244 0.2135
## ns(time, df = 18 * 20)66 1.09293 1.48905 0.734 0.4630
## ns(time, df = 18 * 20)67 -0.40960 1.67939 -0.244 0.8073
## ns(time, df = 18 * 20)68 2.05236 1.45826 1.407 0.1593
## ns(time, df = 18 * 20)69 0.45756 1.50326 0.304 0.7608
## ns(time, df = 18 * 20)70 1.54678 1.45883 1.060 0.2890
## ns(time, df = 18 * 20)71 0.40456 1.47472 0.274 0.7838
## ns(time, df = 18 * 20)72 2.57973 1.40193 1.840 0.0658 .
## ns(time, df = 18 * 20)73 -1.05975 1.64869 -0.643 0.5204
## ns(time, df = 18 * 20)74 2.80968 1.41313 1.988 0.0468 *
## ns(time, df = 18 * 20)75 -0.39582 1.52556 -0.259 0.7953
## ns(time, df = 18 * 20)76 3.19263 1.45344 2.197 0.0280 *
## ns(time, df = 18 * 20)77 -1.52063 2.03174 -0.748 0.4542
## ns(time, df = 18 * 20)78 -0.71570 2.07127 -0.346 0.7297
## ns(time, df = 18 * 20)79 2.31973 1.71401 1.353 0.1759
## ns(time, df = 18 * 20)80 -1.87100 2.06080 -0.908 0.3639
## ns(time, df = 18 * 20)81 2.66511 1.73816 1.533 0.1252
## ns(time, df = 18 * 20)82 -2.73798 2.16671 -1.264 0.2064
## ns(time, df = 18 * 20)83 2.95054 1.47489 2.001 0.0454 *
## ns(time, df = 18 * 20)84 0.15330 1.47660 0.104 0.9173
## ns(time, df = 18 * 20)85 2.00334 1.36038 1.473 0.1409
## ns(time, df = 18 * 20)86 1.10035 1.36324 0.807 0.4196
## ns(time, df = 18 * 20)87 1.89164 1.35319 1.398 0.1621
## ns(time, df = 18 * 20)88 0.63285 1.43583 0.441 0.6594
## ns(time, df = 18 * 20)89 1.53705 1.37654 1.117 0.2642
## ns(time, df = 18 * 20)90 1.58459 1.36005 1.165 0.2440
## ns(time, df = 18 * 20)91 0.89750 1.38417 0.648 0.5167
## ns(time, df = 18 * 20)92 2.43082 1.35999 1.787 0.0739 .
## ns(time, df = 18 * 20)93 -0.02081 1.62096 -0.013 0.9898
## ns(time, df = 18 * 20)94 0.67479 1.79594 0.376 0.7071
## ns(time, df = 18 * 20)95 -0.76532 1.86358 -0.411 0.6813
## ns(time, df = 18 * 20)96 2.28924 1.56026 1.467 0.1423
## ns(time, df = 18 * 20)97 -0.84749 1.69533 -0.500 0.6171
## ns(time, df = 18 * 20)98 2.48872 1.44152 1.726 0.0843 .
## ns(time, df = 18 * 20)99 -0.01783 1.53321 -0.012 0.9907
## ns(time, df = 18 * 20)100 2.13669 1.47083 1.453 0.1463
## ns(time, df = 18 * 20)101 -0.90585 1.62097 -0.559 0.5763
## ns(time, df = 18 * 20)102 3.12877 1.35486 2.309 0.0209 *
## ns(time, df = 18 * 20)103 -0.24413 1.45611 -0.168 0.8669
## ns(time, df = 18 * 20)104 2.54820 1.30647 1.950 0.0511 .
## ns(time, df = 18 * 20)105 0.97240 1.32398 0.734 0.4627
## ns(time, df = 18 * 20)106 2.55198 1.31732 1.937 0.0527 .
## ns(time, df = 18 * 20)107 -0.16399 1.53450 -0.107 0.9149
## ns(time, df = 18 * 20)108 1.30140 1.42712 0.912 0.3618
## ns(time, df = 18 * 20)109 1.69059 1.32789 1.273 0.2030
## ns(time, df = 18 * 20)110 1.89940 1.32400 1.435 0.1514
## ns(time, df = 18 * 20)111 0.69157 1.41577 0.488 0.6252
## ns(time, df = 18 * 20)112 1.72894 1.38371 1.250 0.2115
## ns(time, df = 18 * 20)113 0.84135 1.41304 0.595 0.5516
## ns(time, df = 18 * 20)114 2.01052 1.39808 1.438 0.1504
## ns(time, df = 18 * 20)115 -0.22353 1.53415 -0.146 0.8842
## ns(time, df = 18 * 20)116 2.28426 1.35136 1.690 0.0910 .
## ns(time, df = 18 * 20)117 1.14481 1.37581 0.832 0.4054
## ns(time, df = 18 * 20)118 1.20700 1.39476 0.865 0.3868
## ns(time, df = 18 * 20)119 1.94708 1.42374 1.368 0.1714
## ns(time, df = 18 * 20)120 -0.23083 1.72339 -0.134 0.8934
## ns(time, df = 18 * 20)121 0.98595 1.88667 0.523 0.6013
## ns(time, df = 18 * 20)122 -2.34277 2.13290 -1.098 0.2720
## ns(time, df = 18 * 20)123 3.36638 1.45552 2.313 0.0207 *
## ns(time, df = 18 * 20)124 0.08127 1.55072 0.052 0.9582
## ns(time, df = 18 * 20)125 0.84298 1.58187 0.533 0.5941
## ns(time, df = 18 * 20)126 0.95839 1.52271 0.629 0.5291
## ns(time, df = 18 * 20)127 0.92606 1.45727 0.635 0.5251
## ns(time, df = 18 * 20)128 1.54377 1.34165 1.151 0.2499
## ns(time, df = 18 * 20)129 2.37427 1.32263 1.795 0.0726 .
## ns(time, df = 18 * 20)130 -0.34157 1.50513 -0.227 0.8205
## ns(time, df = 18 * 20)131 2.52587 1.34664 1.876 0.0607 .
## ns(time, df = 18 * 20)132 1.01870 1.44527 0.705 0.4809
## ns(time, df = 18 * 20)133 -0.11061 1.58835 -0.070 0.9445
## ns(time, df = 18 * 20)134 2.45250 1.43809 1.705 0.0881 .
## ns(time, df = 18 * 20)135 -0.61417 1.60562 -0.383 0.7021
## ns(time, df = 18 * 20)136 2.28569 1.39265 1.641 0.1007
## ns(time, df = 18 * 20)137 0.83834 1.42881 0.587 0.5574
## ns(time, df = 18 * 20)138 1.25144 1.43126 0.874 0.3819
## ns(time, df = 18 * 20)139 1.20179 1.42425 0.844 0.3988
## ns(time, df = 18 * 20)140 0.98528 1.39133 0.708 0.4788
## ns(time, df = 18 * 20)141 2.26319 1.32277 1.711 0.0871 .
## ns(time, df = 18 * 20)142 0.63444 1.41501 0.448 0.6539
## ns(time, df = 18 * 20)143 1.69126 1.39825 1.210 0.2264
## ns(time, df = 18 * 20)144 0.82317 1.45426 0.566 0.5714
## ns(time, df = 18 * 20)145 1.14674 1.42972 0.802 0.4225
## ns(time, df = 18 * 20)146 1.28541 1.35356 0.950 0.3423
## ns(time, df = 18 * 20)147 2.14788 1.29060 1.664 0.0961 .
## ns(time, df = 18 * 20)148 1.03628 1.31837 0.786 0.4319
## ns(time, df = 18 * 20)149 2.46514 1.28917 1.912 0.0559 .
## ns(time, df = 18 * 20)150 0.62496 1.42072 0.440 0.6600
## ns(time, df = 18 * 20)151 1.16123 1.44125 0.806 0.4204
## ns(time, df = 18 * 20)152 1.24096 1.40692 0.882 0.3778
## ns(time, df = 18 * 20)153 1.44135 1.37848 1.046 0.2957
## ns(time, df = 18 * 20)154 1.24482 1.36860 0.910 0.3631
## ns(time, df = 18 * 20)155 2.13185 1.38675 1.537 0.1242
## ns(time, df = 18 * 20)156 -0.70938 1.62698 -0.436 0.6628
## ns(time, df = 18 * 20)157 2.37459 1.41872 1.674 0.0942 .
## ns(time, df = 18 * 20)158 0.70293 1.52499 0.461 0.6448
## ns(time, df = 18 * 20)159 0.07758 1.64855 0.047 0.9625
## ns(time, df = 18 * 20)160 1.50923 1.47837 1.021 0.3073
## ns(time, df = 18 * 20)161 0.98789 1.45051 0.681 0.4958
## ns(time, df = 18 * 20)162 1.29626 1.41203 0.918 0.3586
## ns(time, df = 18 * 20)163 1.39972 1.38869 1.008 0.3135
## ns(time, df = 18 * 20)164 1.33667 1.40599 0.951 0.3418
## ns(time, df = 18 * 20)165 0.72706 1.41661 0.513 0.6078
## ns(time, df = 18 * 20)166 2.36792 1.33984 1.767 0.0772 .
## ns(time, df = 18 * 20)167 0.27492 1.45660 0.189 0.8503
## ns(time, df = 18 * 20)168 1.90672 1.39586 1.366 0.1719
## ns(time, df = 18 * 20)169 1.15648 1.49834 0.772 0.4402
## ns(time, df = 18 * 20)170 -0.58982 1.70652 -0.346 0.7296
## ns(time, df = 18 * 20)171 2.22480 1.45611 1.528 0.1265
## ns(time, df = 18 * 20)172 0.27556 1.51803 0.182 0.8560
## ns(time, df = 18 * 20)173 1.68559 1.46130 1.153 0.2487
## ns(time, df = 18 * 20)174 0.17572 1.49226 0.118 0.9063
## ns(time, df = 18 * 20)175 2.51909 1.36041 1.852 0.0641 .
## ns(time, df = 18 * 20)176 0.32097 1.48403 0.216 0.8288
## ns(time, df = 18 * 20)177 1.14752 1.43546 0.799 0.4241
## ns(time, df = 18 * 20)178 1.86501 1.37395 1.357 0.1746
## ns(time, df = 18 * 20)179 1.04620 1.48089 0.706 0.4799
## ns(time, df = 18 * 20)180 0.28134 1.66283 0.169 0.8656
## ns(time, df = 18 * 20)181 0.95681 1.70478 0.561 0.5746
## ns(time, df = 18 * 20)182 -0.97681 1.70376 -0.573 0.5664
## ns(time, df = 18 * 20)183 3.28449 1.33120 2.467 0.0136 *
## ns(time, df = 18 * 20)184 0.36426 1.39076 0.262 0.7934
## ns(time, df = 18 * 20)185 2.16119 1.34323 1.609 0.1076
## ns(time, df = 18 * 20)186 0.94483 1.42925 0.661 0.5086
## ns(time, df = 18 * 20)187 0.48983 1.45205 0.337 0.7359
## ns(time, df = 18 * 20)188 2.36104 1.30307 1.812 0.0700 .
## ns(time, df = 18 * 20)189 1.45766 1.33877 1.089 0.2762
## ns(time, df = 18 * 20)190 0.77053 1.41060 0.546 0.5849
## ns(time, df = 18 * 20)191 2.03711 1.36597 1.491 0.1359
## ns(time, df = 18 * 20)192 0.24732 1.43959 0.172 0.8636
## ns(time, df = 18 * 20)193 2.08453 1.27811 1.631 0.1029
## ns(time, df = 18 * 20)194 2.41121 1.24625 1.935 0.0530 .
## ns(time, df = 18 * 20)195 0.65794 1.33087 0.494 0.6210
## ns(time, df = 18 * 20)196 2.46622 1.26322 1.952 0.0509 .
## ns(time, df = 18 * 20)197 1.31579 1.30841 1.006 0.3146
## ns(time, df = 18 * 20)198 1.36029 1.32464 1.027 0.3045
## ns(time, df = 18 * 20)199 1.84030 1.28241 1.435 0.1513
## ns(time, df = 18 * 20)200 2.13188 1.30446 1.634 0.1022
## ns(time, df = 18 * 20)201 0.36953 1.48374 0.249 0.8033
## ns(time, df = 18 * 20)202 1.11063 1.47265 0.754 0.4507
## ns(time, df = 18 * 20)203 1.58049 1.46122 1.082 0.2794
## ns(time, df = 18 * 20)204 0.27165 1.61960 0.168 0.8668
## ns(time, df = 18 * 20)205 0.65931 1.59612 0.413 0.6796
## ns(time, df = 18 * 20)206 1.24947 1.49063 0.838 0.4019
## ns(time, df = 18 * 20)207 1.08120 1.46976 0.736 0.4620
## ns(time, df = 18 * 20)208 1.75968 1.55093 1.135 0.2565
## ns(time, df = 18 * 20)209 -1.71623 2.04111 -0.841 0.4004
## ns(time, df = 18 * 20)210 1.71255 1.57816 1.085 0.2779
## ns(time, df = 18 * 20)211 0.78147 1.50063 0.521 0.6025
## ns(time, df = 18 * 20)212 0.92705 1.37507 0.674 0.5002
## ns(time, df = 18 * 20)213 3.12455 1.27274 2.455 0.0141 *
## ns(time, df = 18 * 20)214 0.66789 1.48174 0.451 0.6522
## ns(time, df = 18 * 20)215 -1.19721 1.83136 -0.654 0.5133
## ns(time, df = 18 * 20)216 2.45770 1.39955 1.756 0.0791 .
## ns(time, df = 18 * 20)217 0.83309 1.35193 0.616 0.5377
## ns(time, df = 18 * 20)218 2.87234 1.30845 2.195 0.0281 *
## ns(time, df = 18 * 20)219 0.01547 1.56948 0.010 0.9921
## ns(time, df = 18 * 20)220 -0.12197 1.66827 -0.073 0.9417
## ns(time, df = 18 * 20)221 1.93525 1.37833 1.404 0.1603
## ns(time, df = 18 * 20)222 1.55819 1.31975 1.181 0.2377
## ns(time, df = 18 * 20)223 1.59300 1.30681 1.219 0.2228
## ns(time, df = 18 * 20)224 1.68982 1.29723 1.303 0.1927
## ns(time, df = 18 * 20)225 1.43774 1.28919 1.115 0.2648
## ns(time, df = 18 * 20)226 2.18224 1.25214 1.743 0.0814 .
## ns(time, df = 18 * 20)227 1.68353 1.28875 1.306 0.1914
## ns(time, df = 18 * 20)228 1.00558 1.33609 0.753 0.4517
## ns(time, df = 18 * 20)229 2.57562 1.31556 1.958 0.0503 .
## ns(time, df = 18 * 20)230 -0.48532 1.49622 -0.324 0.7457
## ns(time, df = 18 * 20)231 2.88275 1.33510 2.159 0.0308 *
## ns(time, df = 18 * 20)232 0.21263 1.45003 0.147 0.8834
## ns(time, df = 18 * 20)233 1.61788 1.39044 1.164 0.2446
## ns(time, df = 18 * 20)234 1.12004 1.34519 0.833 0.4051
## ns(time, df = 18 * 20)235 2.46405 1.29389 1.904 0.0569 .
## ns(time, df = 18 * 20)236 0.46256 1.40256 0.330 0.7416
## ns(time, df = 18 * 20)237 2.57116 1.42934 1.799 0.0720 .
## ns(time, df = 18 * 20)238 -1.93461 1.88061 -1.029 0.3036
## ns(time, df = 18 * 20)239 2.34839 1.40883 1.667 0.0955 .
## ns(time, df = 18 * 20)240 1.42730 1.35478 1.054 0.2921
## ns(time, df = 18 * 20)241 1.44115 1.39451 1.033 0.3014
## ns(time, df = 18 * 20)242 1.19771 1.51345 0.791 0.4287
## ns(time, df = 18 * 20)243 -0.71297 1.68398 -0.423 0.6720
## ns(time, df = 18 * 20)244 3.06751 1.46824 2.089 0.0367 *
## ns(time, df = 18 * 20)245 -1.64896 1.79809 -0.917 0.3591
## ns(time, df = 18 * 20)246 2.27752 1.42500 1.598 0.1100
## ns(time, df = 18 * 20)247 0.78857 1.36275 0.579 0.5628
## ns(time, df = 18 * 20)248 2.55832 1.27240 2.011 0.0444 *
## ns(time, df = 18 * 20)249 1.09288 1.33788 0.817 0.4140
## ns(time, df = 18 * 20)250 1.26658 1.34521 0.942 0.3464
## ns(time, df = 18 * 20)251 2.21641 1.31837 1.681 0.0927 .
## ns(time, df = 18 * 20)252 0.63364 1.45349 0.436 0.6629
## ns(time, df = 18 * 20)253 0.94989 1.48296 0.641 0.5218
## ns(time, df = 18 * 20)254 1.33525 1.43651 0.930 0.3526
## ns(time, df = 18 * 20)255 1.29033 1.45983 0.884 0.3768
## ns(time, df = 18 * 20)256 0.62688 1.56248 0.401 0.6883
## ns(time, df = 18 * 20)257 0.90721 1.59640 0.568 0.5698
## ns(time, df = 18 * 20)258 0.06493 1.56214 0.042 0.9668
## ns(time, df = 18 * 20)259 2.48405 1.35599 1.832 0.0670 .
## ns(time, df = 18 * 20)260 0.84815 1.42754 0.594 0.5524
## ns(time, df = 18 * 20)261 0.84405 1.46425 0.576 0.5643
## ns(time, df = 18 * 20)262 1.73347 1.41090 1.229 0.2192
## ns(time, df = 18 * 20)263 0.47685 1.46692 0.325 0.7451
## ns(time, df = 18 * 20)264 1.73881 1.36050 1.278 0.2012
## ns(time, df = 18 * 20)265 1.52989 1.33817 1.143 0.2529
## ns(time, df = 18 * 20)266 1.44467 1.35897 1.063 0.2878
## ns(time, df = 18 * 20)267 1.27755 1.39367 0.917 0.3593
## ns(time, df = 18 * 20)268 1.40697 1.45103 0.970 0.3322
## ns(time, df = 18 * 20)269 0.03208 1.56737 0.020 0.9837
## ns(time, df = 18 * 20)270 2.32687 1.48003 1.572 0.1159
## ns(time, df = 18 * 20)271 -1.10778 1.74076 -0.636 0.5245
## ns(time, df = 18 * 20)272 2.37635 1.47423 1.612 0.1070
## ns(time, df = 18 * 20)273 -0.28607 1.52221 -0.188 0.8509
## ns(time, df = 18 * 20)274 2.66669 1.32685 2.010 0.0445 *
## ns(time, df = 18 * 20)275 0.67309 1.36735 0.492 0.6225
## ns(time, df = 18 * 20)276 2.29995 1.33686 1.720 0.0854 .
## ns(time, df = 18 * 20)277 0.01719 1.43394 0.012 0.9904
## ns(time, df = 18 * 20)278 2.81012 1.29533 2.169 0.0301 *
## ns(time, df = 18 * 20)279 1.94261 1.46526 1.326 0.1849
## ns(time, df = 18 * 20)280 -4.01422 2.65370 -1.513 0.1304
## ns(time, df = 18 * 20)281 3.06712 1.62602 1.886 0.0593 .
## ns(time, df = 18 * 20)282 -0.88518 1.62824 -0.544 0.5867
## ns(time, df = 18 * 20)283 2.73421 1.33116 2.054 0.0400 *
## ns(time, df = 18 * 20)284 1.30933 1.36338 0.960 0.3369
## ns(time, df = 18 * 20)285 0.47412 1.44134 0.329 0.7422
## ns(time, df = 18 * 20)286 2.34937 1.33891 1.755 0.0793 .
## ns(time, df = 18 * 20)287 0.30693 1.39887 0.219 0.8263
## ns(time, df = 18 * 20)288 2.56747 1.28183 2.003 0.0452 *
## ns(time, df = 18 * 20)289 1.14409 1.32910 0.861 0.3893
## ns(time, df = 18 * 20)290 1.26734 1.31636 0.963 0.3357
## ns(time, df = 18 * 20)291 2.63778 1.26963 2.078 0.0377 *
## ns(time, df = 18 * 20)292 0.29037 1.38795 0.209 0.8343
## ns(time, df = 18 * 20)293 2.11124 1.27933 1.650 0.0989 .
## ns(time, df = 18 * 20)294 2.67432 1.30552 2.048 0.0405 *
## ns(time, df = 18 * 20)295 -1.71207 1.67756 -1.021 0.3075
## ns(time, df = 18 * 20)296 3.29987 1.32537 2.490 0.0128 *
## ns(time, df = 18 * 20)297 0.33838 1.37727 0.246 0.8059
## ns(time, df = 18 * 20)298 2.29126 1.30473 1.756 0.0791 .
## ns(time, df = 18 * 20)299 1.07320 1.34138 0.800 0.4237
## ns(time, df = 18 * 20)300 1.75364 1.32795 1.321 0.1866
## ns(time, df = 18 * 20)301 1.52264 1.36919 1.112 0.2661
## ns(time, df = 18 * 20)302 0.26959 1.42323 0.189 0.8498
## ns(time, df = 18 * 20)303 2.84365 1.27660 2.228 0.0259 *
## ns(time, df = 18 * 20)304 1.02362 1.35647 0.755 0.4505
## ns(time, df = 18 * 20)305 1.65093 1.49132 1.107 0.2683
## ns(time, df = 18 * 20)306 -1.64172 1.78886 -0.918 0.3587
## ns(time, df = 18 * 20)307 3.33047 1.36661 2.437 0.0148 *
## ns(time, df = 18 * 20)308 -0.09054 1.42867 -0.063 0.9495
## ns(time, df = 18 * 20)309 2.72718 1.31311 2.077 0.0378 *
## ns(time, df = 18 * 20)310 0.52348 1.40903 0.372 0.7103
## ns(time, df = 18 * 20)311 1.51175 1.37580 1.099 0.2718
## ns(time, df = 18 * 20)312 1.39683 1.33866 1.043 0.2967
## ns(time, df = 18 * 20)313 1.92700 1.32023 1.460 0.1444
## ns(time, df = 18 * 20)314 0.80382 1.37676 0.584 0.5593
## ns(time, df = 18 * 20)315 1.87898 1.30905 1.435 0.1512
## ns(time, df = 18 * 20)316 1.52953 1.28856 1.187 0.2352
## ns(time, df = 18 * 20)317 2.11125 1.27860 1.651 0.0987 .
## ns(time, df = 18 * 20)318 0.89314 1.32680 0.673 0.5009
## ns(time, df = 18 * 20)319 3.08065 1.33607 2.306 0.0211 *
## ns(time, df = 18 * 20)320 -2.16834 1.77196 -1.224 0.2211
## ns(time, df = 18 * 20)321 3.45495 1.38393 2.496 0.0125 *
## ns(time, df = 18 * 20)322 -0.57020 1.50419 -0.379 0.7046
## ns(time, df = 18 * 20)323 2.50371 1.32107 1.895 0.0581 .
## ns(time, df = 18 * 20)324 1.18130 1.33587 0.884 0.3765
## ns(time, df = 18 * 20)325 1.32936 1.31332 1.012 0.3114
## ns(time, df = 18 * 20)326 2.48762 1.26128 1.972 0.0486 *
## ns(time, df = 18 * 20)327 1.08699 1.35689 0.801 0.4231
## ns(time, df = 18 * 20)328 0.96160 1.41463 0.680 0.4967
## ns(time, df = 18 * 20)329 2.14773 1.42611 1.506 0.1321
## ns(time, df = 18 * 20)330 -1.05661 1.71815 -0.615 0.5386
## ns(time, df = 18 * 20)331 2.04674 1.38898 1.474 0.1406
## ns(time, df = 18 * 20)332 1.41312 1.31142 1.078 0.2812
## ns(time, df = 18 * 20)333 2.09195 1.26920 1.648 0.0993 .
## ns(time, df = 18 * 20)334 1.50433 1.29365 1.163 0.2449
## ns(time, df = 18 * 20)335 1.94420 1.33450 1.457 0.1451
## ns(time, df = 18 * 20)336 0.35905 1.49220 0.241 0.8099
## ns(time, df = 18 * 20)337 1.30127 1.45589 0.894 0.3714
## ns(time, df = 18 * 20)338 0.84964 1.39227 0.610 0.5417
## ns(time, df = 18 * 20)339 2.37484 1.27772 1.859 0.0631 .
## ns(time, df = 18 * 20)340 1.71303 1.32720 1.291 0.1968
## ns(time, df = 18 * 20)341 0.74690 1.52335 0.490 0.6239
## ns(time, df = 18 * 20)342 0.30234 1.73145 0.175 0.8614
## ns(time, df = 18 * 20)343 -0.11360 1.68069 -0.068 0.9461
## ns(time, df = 18 * 20)344 2.04014 1.39479 1.463 0.1436
## ns(time, df = 18 * 20)345 1.22356 1.36972 0.893 0.3717
## ns(time, df = 18 * 20)346 1.53773 1.36122 1.130 0.2586
## ns(time, df = 18 * 20)347 1.37774 1.38967 0.991 0.3215
## ns(time, df = 18 * 20)348 1.01051 1.45218 0.696 0.4865
## ns(time, df = 18 * 20)349 0.61312 1.41484 0.433 0.6648
## ns(time, df = 18 * 20)350 2.56740 1.26574 2.028 0.0425 *
## ns(time, df = 18 * 20)351 1.45271 1.28724 1.129 0.2591
## ns(time, df = 18 * 20)352 1.54391 1.30384 1.184 0.2364
## ns(time, df = 18 * 20)353 1.61338 1.28374 1.257 0.2088
## ns(time, df = 18 * 20)354 1.92753 1.25170 1.540 0.1236
## ns(time, df = 18 * 20)355 1.88733 1.24413 1.517 0.1293
## ns(time, df = 18 * 20)356 2.08357 1.26459 1.648 0.0994 .
## ns(time, df = 18 * 20)357 0.88650 1.35179 0.656 0.5120
## ns(time, df = 18 * 20)358 0.87523 1.02628 0.853 0.3938
## ns(time, df = 18 * 20)359 3.45442 2.32143 1.488 0.1367
## ns(time, df = 18 * 20)360 0.04381 0.72752 0.060 0.9520
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(40190.31) family taken to be 1)
##
## Null deviance: 1179.12 on 938 degrees of freedom
## Residual deviance: 740.58 on 578 degrees of freedom
## AIC: 3612.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 40190
## Std. Err.: 167921
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2888.433## model aic theta
## 1 ns1 py 3334.422 29.5223
## 2 ns5 py 3386.524 13518.6673
## 3 ns6 py 3405.698 17043.3342
## 4 ns7 py 3412.745 20836.3592
## 5 ns8 py 3423.554 23590.1831
## 6 ns9 py 3427.643 26527.1497
## 7 ns10 py 3444.692 27991.4874
## 8 ns14 py 3517.555 32829.9959
## 9 ns15 py 3529.607 33805.2556
## 10 ns16 py 3556.458 33776.5473
## 11 ns18 py 3586.306 36901.6402
## 12 ns20 py 3612.433 40190.3100##
## Call:
## glm.nb(formula = ptbBM ~ ns(time, df = 18 * 15), data = week,
## init.theta = 33805.25556, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.70939 -0.75578 -0.03452 0.50500 2.42892
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.443263 0.841886 -0.527 0.59853
## ns(time, df = 18 * 15)1 0.380749 0.962786 0.395 0.69250
## ns(time, df = 18 * 15)2 2.373539 1.323327 1.794 0.07287 .
## ns(time, df = 18 * 15)3 -1.500659 1.585080 -0.947 0.34377
## ns(time, df = 18 * 15)4 0.338441 1.437296 0.235 0.81384
## ns(time, df = 18 * 15)5 2.319432 1.211052 1.915 0.05546 .
## ns(time, df = 18 * 15)6 -0.446395 1.417803 -0.315 0.75288
## ns(time, df = 18 * 15)7 1.000544 1.323849 0.756 0.44978
## ns(time, df = 18 * 15)8 0.783140 1.269764 0.617 0.53739
## ns(time, df = 18 * 15)9 0.922306 1.164524 0.792 0.42836
## ns(time, df = 18 * 15)10 2.486673 1.077291 2.308 0.02098 *
## ns(time, df = 18 * 15)11 0.471835 1.129344 0.418 0.67610
## ns(time, df = 18 * 15)12 2.637101 1.076602 2.449 0.01431 *
## ns(time, df = 18 * 15)13 0.248680 1.169145 0.213 0.83156
## ns(time, df = 18 * 15)14 2.333039 1.137082 2.052 0.04019 *
## ns(time, df = 18 * 15)15 -0.277312 1.256958 -0.221 0.82539
## ns(time, df = 18 * 15)16 2.351778 1.135764 2.071 0.03839 *
## ns(time, df = 18 * 15)17 1.054785 1.230053 0.858 0.39116
## ns(time, df = 18 * 15)18 -0.940585 1.401822 -0.671 0.50224
## ns(time, df = 18 * 15)19 3.211485 1.178502 2.725 0.00643 **
## ns(time, df = 18 * 15)20 -0.983478 1.387732 -0.709 0.47851
## ns(time, df = 18 * 15)21 2.096555 1.311118 1.599 0.10981
## ns(time, df = 18 * 15)22 -0.377742 1.584407 -0.238 0.81156
## ns(time, df = 18 * 15)23 -0.937516 1.777730 -0.527 0.59794
## ns(time, df = 18 * 15)24 0.981917 1.378613 0.712 0.47631
## ns(time, df = 18 * 15)25 1.074378 1.225629 0.877 0.38071
## ns(time, df = 18 * 15)26 1.534906 1.167604 1.315 0.18865
## ns(time, df = 18 * 15)27 0.950278 1.192504 0.797 0.42552
## ns(time, df = 18 * 15)28 0.921354 1.151039 0.800 0.42345
## ns(time, df = 18 * 15)29 2.630514 1.092406 2.408 0.01604 *
## ns(time, df = 18 * 15)30 0.049328 1.244549 0.040 0.96838
## ns(time, df = 18 * 15)31 1.184576 1.218096 0.972 0.33081
## ns(time, df = 18 * 15)32 1.268228 1.177222 1.077 0.28134
## ns(time, df = 18 * 15)33 1.235642 1.172310 1.054 0.29187
## ns(time, df = 18 * 15)34 1.558597 1.216881 1.281 0.20026
## ns(time, df = 18 * 15)35 -0.650901 1.384852 -0.470 0.63834
## ns(time, df = 18 * 15)36 2.314583 1.203023 1.924 0.05436 .
## ns(time, df = 18 * 15)37 0.134256 1.294485 0.104 0.91740
## ns(time, df = 18 * 15)38 1.067707 1.253404 0.852 0.39430
## ns(time, df = 18 * 15)39 1.281563 1.225368 1.046 0.29563
## ns(time, df = 18 * 15)40 0.493412 1.260952 0.391 0.69557
## ns(time, df = 18 * 15)41 1.539379 1.197974 1.285 0.19880
## ns(time, df = 18 * 15)42 0.795178 1.222862 0.650 0.51552
## ns(time, df = 18 * 15)43 1.404816 1.232832 1.140 0.25449
## ns(time, df = 18 * 15)44 -0.186294 1.282555 -0.145 0.88451
## ns(time, df = 18 * 15)45 2.571257 1.115184 2.306 0.02113 *
## ns(time, df = 18 * 15)46 0.643181 1.169364 0.550 0.58230
## ns(time, df = 18 * 15)47 1.312519 1.142797 1.149 0.25076
## ns(time, df = 18 * 15)48 1.873916 1.112148 1.685 0.09200 .
## ns(time, df = 18 * 15)49 1.229987 1.203172 1.022 0.30665
## ns(time, df = 18 * 15)50 -0.319072 1.373058 -0.232 0.81624
## ns(time, df = 18 * 15)51 1.836542 1.236677 1.485 0.13753
## ns(time, df = 18 * 15)52 0.212280 1.282550 0.166 0.86854
## ns(time, df = 18 * 15)53 1.622956 1.210453 1.341 0.17999
## ns(time, df = 18 * 15)54 0.663494 1.249854 0.531 0.59552
## ns(time, df = 18 * 15)55 1.084311 1.241769 0.873 0.38255
## ns(time, df = 18 * 15)56 0.919888 1.233226 0.746 0.45572
## ns(time, df = 18 * 15)57 1.920891 1.290455 1.489 0.13661
## ns(time, df = 18 * 15)58 -1.963720 1.788038 -1.098 0.27209
## ns(time, df = 18 * 15)59 1.537848 1.473939 1.043 0.29678
## ns(time, df = 18 * 15)60 -0.033976 1.557807 -0.022 0.98260
## ns(time, df = 18 * 15)61 -0.461669 1.558587 -0.296 0.76707
## ns(time, df = 18 * 15)62 1.529720 1.268804 1.206 0.22796
## ns(time, df = 18 * 15)63 0.851597 1.209149 0.704 0.48125
## ns(time, df = 18 * 15)64 1.573823 1.150704 1.368 0.17140
## ns(time, df = 18 * 15)65 1.284246 1.164009 1.103 0.26990
## ns(time, df = 18 * 15)66 0.837712 1.194458 0.701 0.48310
## ns(time, df = 18 * 15)67 1.555516 1.158077 1.343 0.17921
## ns(time, df = 18 * 15)68 0.979607 1.171997 0.836 0.40324
## ns(time, df = 18 * 15)69 2.028685 1.203319 1.686 0.09181 .
## ns(time, df = 18 * 15)70 -0.821114 1.529658 -0.537 0.59141
## ns(time, df = 18 * 15)71 0.524888 1.464285 0.358 0.72000
## ns(time, df = 18 * 15)72 0.788882 1.353592 0.583 0.56002
## ns(time, df = 18 * 15)73 0.672296 1.293658 0.520 0.60328
## ns(time, df = 18 * 15)74 1.542694 1.243669 1.240 0.21481
## ns(time, df = 18 * 15)75 -0.065322 1.319444 -0.050 0.96052
## ns(time, df = 18 * 15)76 1.770421 1.182977 1.497 0.13450
## ns(time, df = 18 * 15)77 0.877298 1.173281 0.748 0.45462
## ns(time, df = 18 * 15)78 1.534938 1.118534 1.372 0.16998
## ns(time, df = 18 * 15)79 1.971322 1.115327 1.767 0.07715 .
## ns(time, df = 18 * 15)80 0.281049 1.240969 0.226 0.82083
## ns(time, df = 18 * 15)81 1.157136 1.176362 0.984 0.32528
## ns(time, df = 18 * 15)82 1.987852 1.119690 1.775 0.07584 .
## ns(time, df = 18 * 15)83 0.719912 1.190156 0.605 0.54525
## ns(time, df = 18 * 15)84 1.236507 1.182807 1.045 0.29584
## ns(time, df = 18 * 15)85 1.463331 1.188845 1.231 0.21837
## ns(time, df = 18 * 15)86 0.179826 1.252135 0.144 0.88580
## ns(time, df = 18 * 15)87 2.111767 1.147237 1.841 0.06566 .
## ns(time, df = 18 * 15)88 0.655858 1.200242 0.546 0.58476
## ns(time, df = 18 * 15)89 1.735886 1.228425 1.413 0.15763
## ns(time, df = 18 * 15)90 0.213647 1.497077 0.143 0.88652
## ns(time, df = 18 * 15)91 -2.074724 1.779645 -1.166 0.24369
## ns(time, df = 18 * 15)92 3.027062 1.260172 2.402 0.01630 *
## ns(time, df = 18 * 15)93 -0.427889 1.358526 -0.315 0.75279
## ns(time, df = 18 * 15)94 1.401835 1.285631 1.090 0.27554
## ns(time, df = 18 * 15)95 0.114077 1.253590 0.091 0.92749
## ns(time, df = 18 * 15)96 2.646049 1.121103 2.360 0.01826 *
## ns(time, df = 18 * 15)97 0.001017 1.212596 0.001 0.99933
## ns(time, df = 18 * 15)98 2.341534 1.149331 2.037 0.04162 *
## ns(time, df = 18 * 15)99 0.031059 1.270187 0.024 0.98049
## ns(time, df = 18 * 15)100 1.580893 1.236447 1.279 0.20105
## ns(time, df = 18 * 15)101 0.174929 1.284535 0.136 0.89168
## ns(time, df = 18 * 15)102 1.750152 1.186528 1.475 0.14021
## ns(time, df = 18 * 15)103 0.846595 1.219660 0.694 0.48760
## ns(time, df = 18 * 15)104 0.809474 1.215552 0.666 0.50546
## ns(time, df = 18 * 15)105 1.663670 1.147171 1.450 0.14699
## ns(time, df = 18 * 15)106 1.174252 1.165364 1.008 0.31363
## ns(time, df = 18 * 15)107 1.145596 1.196676 0.957 0.33841
## ns(time, df = 18 * 15)108 0.860390 1.219091 0.706 0.48034
## ns(time, df = 18 * 15)109 1.090623 1.169079 0.933 0.35088
## ns(time, df = 18 * 15)110 1.754586 1.104078 1.589 0.11202
## ns(time, df = 18 * 15)111 1.475671 1.105449 1.335 0.18191
## ns(time, df = 18 * 15)112 1.578768 1.140788 1.384 0.16638
## ns(time, df = 18 * 15)113 0.472107 1.231297 0.383 0.70141
## ns(time, df = 18 * 15)114 1.405717 1.180504 1.191 0.23374
## ns(time, df = 18 * 15)115 1.205083 1.164602 1.035 0.30078
## ns(time, df = 18 * 15)116 1.581514 1.185714 1.334 0.18227
## ns(time, df = 18 * 15)117 -0.025002 1.293234 -0.019 0.98458
## ns(time, df = 18 * 15)118 2.194557 1.223633 1.793 0.07290 .
## ns(time, df = 18 * 15)119 -0.747027 1.407074 -0.531 0.59548
## ns(time, df = 18 * 15)120 1.791655 1.234196 1.452 0.14659
## ns(time, df = 18 * 15)121 0.543704 1.227520 0.443 0.65782
## ns(time, df = 18 * 15)122 1.793066 1.173644 1.528 0.12657
## ns(time, df = 18 * 15)123 0.367048 1.218293 0.301 0.76320
## ns(time, df = 18 * 15)124 2.067149 1.149514 1.798 0.07213 .
## ns(time, df = 18 * 15)125 0.374158 1.215955 0.308 0.75831
## ns(time, df = 18 * 15)126 2.068835 1.197844 1.727 0.08414 .
## ns(time, df = 18 * 15)127 -0.511222 1.382180 -0.370 0.71148
## ns(time, df = 18 * 15)128 1.537469 1.253985 1.226 0.22017
## ns(time, df = 18 * 15)129 0.844508 1.265190 0.667 0.50446
## ns(time, df = 18 * 15)130 0.425505 1.260173 0.338 0.73562
## ns(time, df = 18 * 15)131 2.186886 1.169946 1.869 0.06159 .
## ns(time, df = 18 * 15)132 -0.163753 1.274430 -0.128 0.89776
## ns(time, df = 18 * 15)133 2.159213 1.173649 1.840 0.06581 .
## ns(time, df = 18 * 15)134 0.532820 1.272111 0.419 0.67533
## ns(time, df = 18 * 15)135 0.840939 1.390545 0.605 0.54534
## ns(time, df = 18 * 15)136 -0.947342 1.456470 -0.650 0.51541
## ns(time, df = 18 * 15)137 2.701804 1.146450 2.357 0.01844 *
## ns(time, df = 18 * 15)138 0.606084 1.170121 0.518 0.60448
## ns(time, df = 18 * 15)139 1.868304 1.161622 1.608 0.10776
## ns(time, df = 18 * 15)140 -0.038578 1.225774 -0.031 0.97489
## ns(time, df = 18 * 15)141 2.702459 1.104361 2.447 0.01440 *
## ns(time, df = 18 * 15)142 0.271711 1.190973 0.228 0.81954
## ns(time, df = 18 * 15)143 1.819744 1.157548 1.572 0.11593
## ns(time, df = 18 * 15)144 0.300041 1.164777 0.258 0.79672
## ns(time, df = 18 * 15)145 2.917745 1.055442 2.764 0.00570 **
## ns(time, df = 18 * 15)146 0.432045 1.117295 0.387 0.69899
## ns(time, df = 18 * 15)147 2.545785 1.070554 2.378 0.01741 *
## ns(time, df = 18 * 15)148 0.473831 1.138547 0.416 0.67728
## ns(time, df = 18 * 15)149 2.215072 1.085687 2.040 0.04133 *
## ns(time, df = 18 * 15)150 1.344529 1.145493 1.174 0.24049
## ns(time, df = 18 * 15)151 0.323024 1.268897 0.255 0.79905
## ns(time, df = 18 * 15)152 1.631855 1.244568 1.311 0.18980
## ns(time, df = 18 * 15)153 0.053972 1.374120 0.039 0.96867
## ns(time, df = 18 * 15)154 0.651396 1.311570 0.497 0.61943
## ns(time, df = 18 * 15)155 1.785733 1.243536 1.436 0.15100
## ns(time, df = 18 * 15)156 -0.134223 1.415791 -0.095 0.92447
## ns(time, df = 18 * 15)157 0.590233 1.391899 0.424 0.67153
## ns(time, df = 18 * 15)158 0.563159 1.267320 0.444 0.65678
## ns(time, df = 18 * 15)159 1.868283 1.117806 1.671 0.09465 .
## ns(time, df = 18 * 15)160 2.219001 1.141878 1.943 0.05198 .
## ns(time, df = 18 * 15)161 -1.332277 1.457453 -0.914 0.36066
## ns(time, df = 18 * 15)162 1.935718 1.165954 1.660 0.09687 .
## ns(time, df = 18 * 15)163 1.898842 1.123580 1.690 0.09103 .
## ns(time, df = 18 * 15)164 0.633492 1.252557 0.506 0.61303
## ns(time, df = 18 * 15)165 0.135781 1.290272 0.105 0.91619
## ns(time, df = 18 * 15)166 2.058459 1.122548 1.834 0.06669 .
## ns(time, df = 18 * 15)167 1.301351 1.113407 1.169 0.24248
## ns(time, df = 18 * 15)168 1.336439 1.100510 1.214 0.22460
## ns(time, df = 18 * 15)169 2.005796 1.067501 1.879 0.06025 .
## ns(time, df = 18 * 15)170 1.294840 1.094437 1.183 0.23677
## ns(time, df = 18 * 15)171 1.787309 1.111332 1.608 0.10778
## ns(time, df = 18 * 15)172 0.615992 1.177182 0.523 0.60078
## ns(time, df = 18 * 15)173 1.932602 1.144374 1.689 0.09126 .
## ns(time, df = 18 * 15)174 0.413686 1.211746 0.341 0.73280
## ns(time, df = 18 * 15)175 1.630357 1.141169 1.429 0.15310
## ns(time, df = 18 * 15)176 1.405964 1.124472 1.250 0.21118
## ns(time, df = 18 * 15)177 1.808258 1.167886 1.548 0.12155
## ns(time, df = 18 * 15)178 -0.533317 1.372595 -0.389 0.69761
## ns(time, df = 18 * 15)179 1.565691 1.196162 1.309 0.19056
## ns(time, df = 18 * 15)180 1.601055 1.157179 1.384 0.16649
## ns(time, df = 18 * 15)181 0.880868 1.244966 0.708 0.47923
## ns(time, df = 18 * 15)182 0.351313 1.321436 0.266 0.79035
## ns(time, df = 18 * 15)183 1.372830 1.267297 1.083 0.27869
## ns(time, df = 18 * 15)184 0.105175 1.293817 0.081 0.93521
## ns(time, df = 18 * 15)185 1.708148 1.133242 1.507 0.13173
## ns(time, df = 18 * 15)186 1.916916 1.097512 1.747 0.08071 .
## ns(time, df = 18 * 15)187 0.694941 1.151188 0.604 0.54606
## ns(time, df = 18 * 15)188 2.222669 1.125187 1.975 0.04823 *
## ns(time, df = 18 * 15)189 0.181873 1.253185 0.145 0.88461
## ns(time, df = 18 * 15)190 1.338884 1.228488 1.090 0.27577
## ns(time, df = 18 * 15)191 0.947076 1.251154 0.757 0.44907
## ns(time, df = 18 * 15)192 0.914727 1.320594 0.693 0.48852
## ns(time, df = 18 * 15)193 -0.378655 1.365714 -0.277 0.78158
## ns(time, df = 18 * 15)194 2.508047 1.166297 2.150 0.03152 *
## ns(time, df = 18 * 15)195 0.156448 1.244656 0.126 0.89997
## ns(time, df = 18 * 15)196 1.644725 1.200880 1.370 0.17081
## ns(time, df = 18 * 15)197 0.452379 1.227363 0.369 0.71244
## ns(time, df = 18 * 15)198 1.756412 1.145234 1.534 0.12511
## ns(time, df = 18 * 15)199 1.178906 1.153324 1.022 0.30670
## ns(time, df = 18 * 15)200 1.415343 1.181618 1.198 0.23099
## ns(time, df = 18 * 15)201 0.588177 1.263374 0.466 0.64153
## ns(time, df = 18 * 15)202 1.172345 1.277357 0.918 0.35873
## ns(time, df = 18 * 15)203 0.282350 1.342334 0.210 0.83340
## ns(time, df = 18 * 15)204 0.961901 1.260705 0.763 0.44547
## ns(time, df = 18 * 15)205 1.156500 1.174076 0.985 0.32461
## ns(time, df = 18 * 15)206 1.849923 1.129803 1.637 0.10155
## ns(time, df = 18 * 15)207 0.572815 1.175789 0.487 0.62613
## ns(time, df = 18 * 15)208 2.001952 1.110903 1.802 0.07153 .
## ns(time, df = 18 * 15)209 1.773241 1.189660 1.491 0.13608
## ns(time, df = 18 * 15)210 -1.268602 1.598372 -0.794 0.42738
## ns(time, df = 18 * 15)211 0.750256 1.313899 0.571 0.56799
## ns(time, df = 18 * 15)212 2.070418 1.147188 1.805 0.07111 .
## ns(time, df = 18 * 15)213 0.729590 1.179498 0.619 0.53621
## ns(time, df = 18 * 15)214 1.590320 1.153900 1.378 0.16814
## ns(time, df = 18 * 15)215 0.753970 1.153507 0.654 0.51335
## ns(time, df = 18 * 15)216 2.303925 1.089372 2.115 0.03444 *
## ns(time, df = 18 * 15)217 0.488870 1.134619 0.431 0.66656
## ns(time, df = 18 * 15)218 2.659644 1.076521 2.471 0.01349 *
## ns(time, df = 18 * 15)219 -0.036861 1.153001 -0.032 0.97450
## ns(time, df = 18 * 15)220 3.422383 1.078516 3.173 0.00151 **
## ns(time, df = 18 * 15)221 -1.129237 1.291153 -0.875 0.38179
## ns(time, df = 18 * 15)222 2.626869 1.115133 2.356 0.01849 *
## ns(time, df = 18 * 15)223 0.777743 1.142809 0.681 0.49615
## ns(time, df = 18 * 15)224 1.623302 1.120660 1.449 0.14747
## ns(time, df = 18 * 15)225 1.549332 1.135593 1.364 0.17246
## ns(time, df = 18 * 15)226 0.482848 1.189018 0.406 0.68468
## ns(time, df = 18 * 15)227 2.111974 1.103695 1.914 0.05568 .
## ns(time, df = 18 * 15)228 1.650334 1.169136 1.412 0.15807
## ns(time, df = 18 * 15)229 -1.039361 1.421071 -0.731 0.46454
## ns(time, df = 18 * 15)230 2.326445 1.165776 1.996 0.04598 *
## ns(time, df = 18 * 15)231 0.740002 1.164152 0.636 0.52500
## ns(time, df = 18 * 15)232 1.913229 1.136141 1.684 0.09219 .
## ns(time, df = 18 * 15)233 0.432711 1.189061 0.364 0.71592
## ns(time, df = 18 * 15)234 2.067355 1.117393 1.850 0.06429 .
## ns(time, df = 18 * 15)235 0.848825 1.151019 0.737 0.46085
## ns(time, df = 18 * 15)236 1.517168 1.113688 1.362 0.17311
## ns(time, df = 18 * 15)237 1.697601 1.087210 1.561 0.11842
## ns(time, df = 18 * 15)238 1.494750 1.099792 1.359 0.17411
## ns(time, df = 18 * 15)239 1.617616 1.139442 1.420 0.15571
## ns(time, df = 18 * 15)240 0.383777 1.248274 0.307 0.75850
## ns(time, df = 18 * 15)241 1.182650 1.183801 0.999 0.31778
## ns(time, df = 18 * 15)242 1.717423 1.130467 1.519 0.12871
## ns(time, df = 18 * 15)243 0.751741 1.137716 0.661 0.50878
## ns(time, df = 18 * 15)244 2.480440 1.075313 2.307 0.02107 *
## ns(time, df = 18 * 15)245 0.669890 1.152167 0.581 0.56096
## ns(time, df = 18 * 15)246 1.839453 1.175531 1.565 0.11763
## ns(time, df = 18 * 15)247 -0.119179 1.315950 -0.091 0.92784
## ns(time, df = 18 * 15)248 1.280264 1.182949 1.082 0.27914
## ns(time, df = 18 * 15)249 1.765648 1.096528 1.610 0.10735
## ns(time, df = 18 * 15)250 1.636572 1.091595 1.499 0.13381
## ns(time, df = 18 * 15)251 1.542399 1.138494 1.355 0.17549
## ns(time, df = 18 * 15)252 0.515710 1.250571 0.412 0.68006
## ns(time, df = 18 * 15)253 0.765886 1.202078 0.637 0.52404
## ns(time, df = 18 * 15)254 2.199876 1.101483 1.997 0.04580 *
## ns(time, df = 18 * 15)255 1.404195 1.171831 1.198 0.23080
## ns(time, df = 18 * 15)256 0.031020 1.406989 0.022 0.98241
## ns(time, df = 18 * 15)257 -0.095017 1.385706 -0.069 0.94533
## ns(time, df = 18 * 15)258 2.121853 1.169987 1.814 0.06974 .
## ns(time, df = 18 * 15)259 0.713870 1.180763 0.605 0.54546
## ns(time, df = 18 * 15)260 1.902512 1.165770 1.632 0.10268
## ns(time, df = 18 * 15)261 -0.155097 1.254364 -0.124 0.90160
## ns(time, df = 18 * 15)262 2.244411 1.096554 2.047 0.04068 *
## ns(time, df = 18 * 15)263 1.443077 1.088803 1.325 0.18505
## ns(time, df = 18 * 15)264 1.424847 1.095182 1.301 0.19325
## ns(time, df = 18 * 15)265 1.576026 1.070999 1.472 0.14114
## ns(time, df = 18 * 15)266 2.029655 1.051007 1.931 0.05346 .
## ns(time, df = 18 * 15)267 1.480270 1.087695 1.361 0.17354
## ns(time, df = 18 * 15)268 0.187847 0.887920 0.212 0.83245
## ns(time, df = 18 * 15)269 3.621689 1.976319 1.833 0.06687 .
## ns(time, df = 18 * 15)270 -0.216257 0.630661 -0.343 0.73167
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(33805.26) family taken to be 1)
##
## Null deviance: 1179.11 on 938 degrees of freedom
## Residual deviance: 837.74 on 668 degrees of freedom
## AIC: 3529.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 33805
## Std. Err.: 152584
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2985.607##
## Call:
## glm.nb(formula = ptbBM ~ poly(time, degree = 3), data = week,
## init.theta = 24.42315882, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.18075 -0.76899 -0.03902 0.62369 2.93967
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.73518 0.02359 31.171 < 2e-16 ***
## poly(time, degree = 3)1 2.60782 0.71408 3.652 0.00026 ***
## poly(time, degree = 3)2 0.95835 0.71620 1.338 0.18086
## poly(time, degree = 3)3 -0.41157 0.71535 -0.575 0.56506
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(24.4232) family taken to be 1)
##
## Null deviance: 1098.4 on 938 degrees of freedom
## Residual deviance: 1082.7 on 935 degrees of freedom
## AIC: 3316.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 24.4
## Std. Err.: 14.7
##
## 2 x log-likelihood: -3306.795
## [1] model aic theta
## <0 rows> (or 0-length row.names)##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 1), data = week,
## init.theta = 58.23420512, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.9295 -0.6842 -0.3054 0.5374 3.3332
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.4337 0.2990 -1.451 0.14683
## ns(time, df = 18 * 1)1 0.5296 0.3309 1.600 0.10955
## ns(time, df = 18 * 1)2 1.1221 0.4231 2.652 0.00800 **
## ns(time, df = 18 * 1)3 0.5287 0.3780 1.399 0.16195
## ns(time, df = 18 * 1)4 1.1183 0.3958 2.826 0.00472 **
## ns(time, df = 18 * 1)5 0.7862 0.3807 2.065 0.03890 *
## ns(time, df = 18 * 1)6 1.2486 0.3839 3.253 0.00114 **
## ns(time, df = 18 * 1)7 0.6162 0.3874 1.590 0.11174
## ns(time, df = 18 * 1)8 1.1660 0.3832 3.042 0.00235 **
## ns(time, df = 18 * 1)9 0.9940 0.3868 2.570 0.01018 *
## ns(time, df = 18 * 1)10 0.5744 0.4002 1.435 0.15124
## ns(time, df = 18 * 1)11 0.8929 0.4019 2.222 0.02631 *
## ns(time, df = 18 * 1)12 0.3594 0.4092 0.878 0.37977
## ns(time, df = 18 * 1)13 0.9298 0.3938 2.361 0.01821 *
## ns(time, df = 18 * 1)14 1.0534 0.3859 2.730 0.00633 **
## ns(time, df = 18 * 1)15 0.9277 0.3906 2.375 0.01756 *
## ns(time, df = 18 * 1)16 0.1379 0.3179 0.434 0.66437
## ns(time, df = 18 * 1)17 1.8052 0.7156 2.523 0.01165 *
## ns(time, df = 18 * 1)18 0.3119 0.2570 1.214 0.22487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(58.2342) family taken to be 1)
##
## Null deviance: 1124.9 on 938 degrees of freedom
## Residual deviance: 1092.2 on 920 degrees of freedom
## AIC: 2937.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 58
## Std. Err.: 108
##
## 2 x log-likelihood: -2897.858## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 2), data = week,
## init.theta = 6921.417412, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1153 -0.8301 -0.1799 0.6110 4.0007
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.3857 0.4374 -0.882 0.37783
## ns(time, df = 18 * 2)1 1.5335 0.4715 3.252 0.00114 **
## ns(time, df = 18 * 2)2 -0.1305 0.6334 -0.206 0.83671
## ns(time, df = 18 * 2)3 1.3753 0.5358 2.567 0.01027 *
## ns(time, df = 18 * 2)4 0.4561 0.5835 0.782 0.43437
## ns(time, df = 18 * 2)5 0.8117 0.5566 1.458 0.14473
## ns(time, df = 18 * 2)6 1.1433 0.5631 2.031 0.04231 *
## ns(time, df = 18 * 2)7 0.1532 0.5776 0.265 0.79078
## ns(time, df = 18 * 2)8 1.3032 0.5599 2.328 0.01993 *
## ns(time, df = 18 * 2)9 0.2699 0.5650 0.478 0.63287
## ns(time, df = 18 * 2)10 1.5792 0.5437 2.904 0.00368 **
## ns(time, df = 18 * 2)11 0.3638 0.5596 0.650 0.51570
## ns(time, df = 18 * 2)12 1.2315 0.5478 2.248 0.02456 *
## ns(time, df = 18 * 2)13 0.7191 0.5491 1.310 0.19034
## ns(time, df = 18 * 2)14 1.3806 0.5457 2.530 0.01141 *
## ns(time, df = 18 * 2)15 0.2220 0.5696 0.390 0.69673
## ns(time, df = 18 * 2)16 1.2132 0.5525 2.196 0.02811 *
## ns(time, df = 18 * 2)17 0.7024 0.5531 1.270 0.20407
## ns(time, df = 18 * 2)18 1.0726 0.5425 1.977 0.04801 *
## ns(time, df = 18 * 2)19 1.4892 0.5479 2.718 0.00657 **
## ns(time, df = 18 * 2)20 -0.4326 0.6041 -0.716 0.47390
## ns(time, df = 18 * 2)21 1.2345 0.5673 2.176 0.02955 *
## ns(time, df = 18 * 2)22 0.7952 0.5703 1.394 0.16323
## ns(time, df = 18 * 2)23 0.3502 0.5906 0.593 0.55325
## ns(time, df = 18 * 2)24 0.7369 0.5850 1.260 0.20780
## ns(time, df = 18 * 2)25 0.5630 0.5878 0.958 0.33815
## ns(time, df = 18 * 2)26 0.5140 0.5873 0.875 0.38152
## ns(time, df = 18 * 2)27 0.6128 0.5700 1.075 0.28231
## ns(time, df = 18 * 2)28 1.2095 0.5463 2.214 0.02682 *
## ns(time, df = 18 * 2)29 1.0188 0.5489 1.856 0.06344 .
## ns(time, df = 18 * 2)30 0.5980 0.5626 1.063 0.28782
## ns(time, df = 18 * 2)31 0.9868 0.5581 1.768 0.07702 .
## ns(time, df = 18 * 2)32 0.8979 0.5680 1.581 0.11392
## ns(time, df = 18 * 2)33 0.1362 0.5910 0.230 0.81775
## ns(time, df = 18 * 2)34 0.8567 0.4451 1.925 0.05424 .
## ns(time, df = 18 * 2)35 1.0664 1.0444 1.021 0.30724
## ns(time, df = 18 * 2)36 0.6927 0.3698 1.873 0.06101 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(6921.417) family taken to be 1)
##
## Null deviance: 1149.6 on 938 degrees of freedom
## Residual deviance: 1080.7 on 902 degrees of freedom
## AIC: 2938.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 6921
## Std. Err.: 77227
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2862.753## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 5), data = week,
## init.theta = 13015.44671, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1183 -0.8442 -0.1538 0.5311 3.4656
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.21961 0.64835 -0.339 0.7348
## ns(time, df = 18 * 5)1 0.15243 0.81836 0.186 0.8522
## ns(time, df = 18 * 5)2 0.62271 0.94600 0.658 0.5104
## ns(time, df = 18 * 5)3 0.58638 0.82664 0.709 0.4781
## ns(time, df = 18 * 5)4 0.89049 0.85639 1.040 0.2984
## ns(time, df = 18 * 5)5 0.77466 0.84066 0.921 0.3568
## ns(time, df = 18 * 5)6 0.59089 0.90407 0.654 0.5134
## ns(time, df = 18 * 5)7 -0.73883 0.96429 -0.766 0.4436
## ns(time, df = 18 * 5)8 1.25421 0.86010 1.458 0.1448
## ns(time, df = 18 * 5)9 0.49274 0.84820 0.581 0.5613
## ns(time, df = 18 * 5)10 0.95117 0.84207 1.130 0.2587
## ns(time, df = 18 * 5)11 0.52282 0.86437 0.605 0.5453
## ns(time, df = 18 * 5)12 0.37953 0.88110 0.431 0.6667
## ns(time, df = 18 * 5)13 0.66044 0.87069 0.759 0.4481
## ns(time, df = 18 * 5)14 0.34849 0.86191 0.404 0.6860
## ns(time, df = 18 * 5)15 1.24025 0.83105 1.492 0.1356
## ns(time, df = 18 * 5)16 0.37267 0.86022 0.433 0.6648
## ns(time, df = 18 * 5)17 0.49700 0.85770 0.579 0.5623
## ns(time, df = 18 * 5)18 1.44072 0.85582 1.683 0.0923 .
## ns(time, df = 18 * 5)19 -1.08010 0.97996 -1.102 0.2704
## ns(time, df = 18 * 5)20 1.01256 0.87839 1.153 0.2490
## ns(time, df = 18 * 5)21 0.55838 0.84630 0.660 0.5094
## ns(time, df = 18 * 5)22 1.36007 0.84018 1.619 0.1055
## ns(time, df = 18 * 5)23 -0.51718 0.91884 -0.563 0.5735
## ns(time, df = 18 * 5)24 0.96755 0.85655 1.130 0.2587
## ns(time, df = 18 * 5)25 0.72175 0.83265 0.867 0.3860
## ns(time, df = 18 * 5)26 1.01001 0.82102 1.230 0.2186
## ns(time, df = 18 * 5)27 0.66487 0.82744 0.804 0.4217
## ns(time, df = 18 * 5)28 1.06528 0.82524 1.291 0.1967
## ns(time, df = 18 * 5)29 0.55329 0.85650 0.646 0.5183
## ns(time, df = 18 * 5)30 0.25463 0.87597 0.291 0.7713
## ns(time, df = 18 * 5)31 0.81233 0.83600 0.972 0.3312
## ns(time, df = 18 * 5)32 1.24804 0.81887 1.524 0.1275
## ns(time, df = 18 * 5)33 0.13707 0.84721 0.162 0.8715
## ns(time, df = 18 * 5)34 1.48400 0.82028 1.809 0.0704 .
## ns(time, df = 18 * 5)35 -0.09590 0.84861 -0.113 0.9100
## ns(time, df = 18 * 5)36 1.79854 0.80293 2.240 0.0251 *
## ns(time, df = 18 * 5)37 0.31129 0.84397 0.369 0.7122
## ns(time, df = 18 * 5)38 0.77458 0.86909 0.891 0.3728
## ns(time, df = 18 * 5)39 -0.01948 0.89876 -0.022 0.9827
## ns(time, df = 18 * 5)40 0.87211 0.85226 1.023 0.3062
## ns(time, df = 18 * 5)41 0.88731 0.84120 1.055 0.2915
## ns(time, df = 18 * 5)42 0.30391 0.85416 0.356 0.7220
## ns(time, df = 18 * 5)43 1.23190 0.82859 1.487 0.1371
## ns(time, df = 18 * 5)44 0.32228 0.85066 0.379 0.7048
## ns(time, df = 18 * 5)45 0.87241 0.83126 1.049 0.2940
## ns(time, df = 18 * 5)46 0.97077 0.82043 1.183 0.2367
## ns(time, df = 18 * 5)47 0.54682 0.82059 0.666 0.5052
## ns(time, df = 18 * 5)48 1.44297 0.79244 1.821 0.0686 .
## ns(time, df = 18 * 5)49 1.10430 0.81721 1.351 0.1766
## ns(time, df = 18 * 5)50 0.17423 0.91120 0.191 0.8484
## ns(time, df = 18 * 5)51 -0.38983 0.99305 -0.393 0.6946
## ns(time, df = 18 * 5)52 0.26245 0.93020 0.282 0.7778
## ns(time, df = 18 * 5)53 0.82821 0.87475 0.947 0.3437
## ns(time, df = 18 * 5)54 0.35423 0.87188 0.406 0.6845
## ns(time, df = 18 * 5)55 0.83207 0.84845 0.981 0.3267
## ns(time, df = 18 * 5)56 0.71399 0.84499 0.845 0.3981
## ns(time, df = 18 * 5)57 0.90920 0.86331 1.053 0.2923
## ns(time, df = 18 * 5)58 -0.31866 0.92683 -0.344 0.7310
## ns(time, df = 18 * 5)59 1.02871 0.89295 1.152 0.2493
## ns(time, df = 18 * 5)60 -0.20885 0.93546 -0.223 0.8233
## ns(time, df = 18 * 5)61 0.47419 0.89619 0.529 0.5967
## ns(time, df = 18 * 5)62 1.00786 0.87369 1.154 0.2487
## ns(time, df = 18 * 5)63 -0.18731 0.93143 -0.201 0.8406
## ns(time, df = 18 * 5)64 0.44041 0.90419 0.487 0.6262
## ns(time, df = 18 * 5)65 0.79291 0.88164 0.899 0.3685
## ns(time, df = 18 * 5)66 0.31589 0.92495 0.342 0.7327
## ns(time, df = 18 * 5)67 -0.56301 0.95349 -0.590 0.5549
## ns(time, df = 18 * 5)68 1.58592 0.86078 1.842 0.0654 .
## ns(time, df = 18 * 5)69 -0.28813 0.90546 -0.318 0.7503
## ns(time, df = 18 * 5)70 0.71985 0.85454 0.842 0.3996
## ns(time, df = 18 * 5)71 1.14383 0.81530 1.403 0.1606
## ns(time, df = 18 * 5)72 0.81332 0.82036 0.991 0.3215
## ns(time, df = 18 * 5)73 0.69956 0.82288 0.850 0.3953
## ns(time, df = 18 * 5)74 1.24764 0.81465 1.532 0.1256
## ns(time, df = 18 * 5)75 0.52602 0.85648 0.614 0.5391
## ns(time, df = 18 * 5)76 0.12421 0.89029 0.140 0.8890
## ns(time, df = 18 * 5)77 0.80979 0.85112 0.951 0.3414
## ns(time, df = 18 * 5)78 0.88291 0.83573 1.056 0.2908
## ns(time, df = 18 * 5)79 0.69490 0.84931 0.818 0.4132
## ns(time, df = 18 * 5)80 0.36483 0.86167 0.423 0.6720
## ns(time, df = 18 * 5)81 1.08453 0.84414 1.285 0.1989
## ns(time, df = 18 * 5)82 0.34968 0.87969 0.398 0.6910
## ns(time, df = 18 * 5)83 0.39082 0.90475 0.432 0.6658
## ns(time, df = 18 * 5)84 0.33304 0.92009 0.362 0.7174
## ns(time, df = 18 * 5)85 0.11204 0.92613 0.121 0.9037
## ns(time, df = 18 * 5)86 0.51091 0.88813 0.575 0.5651
## ns(time, df = 18 * 5)87 0.61758 0.85264 0.724 0.4689
## ns(time, df = 18 * 5)88 1.12156 0.65183 1.721 0.0853 .
## ns(time, df = 18 * 5)89 0.30596 1.63929 0.187 0.8519
## ns(time, df = 18 * 5)90 0.53120 0.62688 0.847 0.3968
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(13015.45) family taken to be 1)
##
## Null deviance: 1149.7 on 938 degrees of freedom
## Residual deviance: 1036.6 on 848 degrees of freedom
## AIC: 3002.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 13015
## Std. Err.: 92150
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2818.534## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 7), data = week,
## init.theta = 16102.46868, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1547 -0.8797 -0.1749 0.5475 3.3908
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.75023 0.85548 -0.877 0.38050
## ns(time, df = 18 * 7)1 -1.02642 1.15633 -0.888 0.37473
## ns(time, df = 18 * 7)2 1.82906 1.23720 1.478 0.13930
## ns(time, df = 18 * 7)3 0.27142 1.10183 0.246 0.80542
## ns(time, df = 18 * 7)4 1.62011 1.10382 1.468 0.14218
## ns(time, df = 18 * 7)5 1.01767 1.06594 0.955 0.33972
## ns(time, df = 18 * 7)6 1.25643 1.06780 1.177 0.23934
## ns(time, df = 18 * 7)7 1.72628 1.04975 1.644 0.10008
## ns(time, df = 18 * 7)8 0.60874 1.10857 0.549 0.58292
## ns(time, df = 18 * 7)9 1.84867 1.13571 1.628 0.10358
## ns(time, df = 18 * 7)10 -1.53340 1.34950 -1.136 0.25584
## ns(time, df = 18 * 7)11 2.22843 1.10561 2.016 0.04384 *
## ns(time, df = 18 * 7)12 0.59107 1.09496 0.540 0.58933
## ns(time, df = 18 * 7)13 1.72043 1.05545 1.630 0.10309
## ns(time, df = 18 * 7)14 1.07969 1.06691 1.012 0.31155
## ns(time, df = 18 * 7)15 1.35341 1.07140 1.263 0.20651
## ns(time, df = 18 * 7)16 1.07445 1.09352 0.983 0.32583
## ns(time, df = 18 * 7)17 0.94546 1.11275 0.850 0.39551
## ns(time, df = 18 * 7)18 1.00915 1.10891 0.910 0.36280
## ns(time, df = 18 * 7)19 1.15468 1.09691 1.053 0.29250
## ns(time, df = 18 * 7)20 0.97964 1.08850 0.900 0.36813
## ns(time, df = 18 * 7)21 1.40629 1.05536 1.333 0.18269
## ns(time, df = 18 * 7)22 1.67565 1.05362 1.590 0.11175
## ns(time, df = 18 * 7)23 0.48497 1.10686 0.438 0.66128
## ns(time, df = 18 * 7)24 1.58197 1.07092 1.477 0.13962
## ns(time, df = 18 * 7)25 1.03340 1.07589 0.961 0.33680
## ns(time, df = 18 * 7)26 1.75188 1.09619 1.598 0.11001
## ns(time, df = 18 * 7)27 -0.30132 1.25314 -0.240 0.80998
## ns(time, df = 18 * 7)28 0.43840 1.18055 0.371 0.71037
## ns(time, df = 18 * 7)29 1.92382 1.06634 1.804 0.07121 .
## ns(time, df = 18 * 7)30 0.82810 1.07032 0.774 0.43911
## ns(time, df = 18 * 7)31 1.87404 1.05141 1.782 0.07468 .
## ns(time, df = 18 * 7)32 0.85448 1.11430 0.767 0.44318
## ns(time, df = 18 * 7)33 0.28367 1.16587 0.243 0.80777
## ns(time, df = 18 * 7)34 1.46036 1.08397 1.347 0.17791
## ns(time, df = 18 * 7)35 1.24856 1.05690 1.181 0.23747
## ns(time, df = 18 * 7)36 1.55519 1.04408 1.490 0.13635
## ns(time, df = 18 * 7)37 1.04260 1.04870 0.994 0.32013
## ns(time, df = 18 * 7)38 2.02693 1.03252 1.963 0.04964 *
## ns(time, df = 18 * 7)39 0.38694 1.07783 0.359 0.71960
## ns(time, df = 18 * 7)40 2.31252 1.03493 2.234 0.02545 *
## ns(time, df = 18 * 7)41 0.59303 1.10173 0.538 0.59039
## ns(time, df = 18 * 7)42 0.86982 1.12195 0.775 0.43818
## ns(time, df = 18 * 7)43 1.16035 1.08642 1.068 0.28550
## ns(time, df = 18 * 7)44 1.27855 1.05221 1.215 0.22432
## ns(time, df = 18 * 7)45 1.82390 1.03238 1.767 0.07728 .
## ns(time, df = 18 * 7)46 0.93400 1.06521 0.877 0.38058
## ns(time, df = 18 * 7)47 1.37574 1.05882 1.299 0.19384
## ns(time, df = 18 * 7)48 1.24930 1.05088 1.189 0.23451
## ns(time, df = 18 * 7)49 1.71851 1.04925 1.638 0.10145
## ns(time, df = 18 * 7)50 0.27255 1.07789 0.253 0.80038
## ns(time, df = 18 * 7)51 2.79068 1.00924 2.765 0.00569 **
## ns(time, df = 18 * 7)52 0.47918 1.07372 0.446 0.65540
## ns(time, df = 18 * 7)53 1.49591 1.08287 1.381 0.16714
## ns(time, df = 18 * 7)54 0.84092 1.11962 0.751 0.45260
## ns(time, df = 18 * 7)55 0.76061 1.13487 0.670 0.50272
## ns(time, df = 18 * 7)56 1.07568 1.09533 0.982 0.32607
## ns(time, df = 18 * 7)57 1.48502 1.06019 1.401 0.16130
## ns(time, df = 18 * 7)58 1.26770 1.06627 1.189 0.23447
## ns(time, df = 18 * 7)59 1.03050 1.07974 0.954 0.33988
## ns(time, df = 18 * 7)60 1.27371 1.06216 1.199 0.23046
## ns(time, df = 18 * 7)61 1.54346 1.04998 1.470 0.14156
## ns(time, df = 18 * 7)62 1.11755 1.07353 1.041 0.29787
## ns(time, df = 18 * 7)63 0.94093 1.07788 0.873 0.38269
## ns(time, df = 18 * 7)64 1.62153 1.03903 1.561 0.11861
## ns(time, df = 18 * 7)65 1.58924 1.04209 1.525 0.12725
## ns(time, df = 18 * 7)66 0.54260 1.06529 0.509 0.61051
## ns(time, df = 18 * 7)67 2.40323 1.00376 2.394 0.01666 *
## ns(time, df = 18 * 7)68 0.99356 1.01980 0.974 0.32992
## ns(time, df = 18 * 7)69 2.52377 1.01962 2.475 0.01332 *
## ns(time, df = 18 * 7)70 -0.16126 1.15287 -0.140 0.88875
## ns(time, df = 18 * 7)71 1.54565 1.17372 1.317 0.18788
## ns(time, df = 18 * 7)72 -0.57452 1.30862 -0.439 0.66065
## ns(time, df = 18 * 7)73 0.94323 1.18730 0.794 0.42695
## ns(time, df = 18 * 7)74 1.11380 1.11620 0.998 0.31835
## ns(time, df = 18 * 7)75 1.17398 1.09385 1.073 0.28315
## ns(time, df = 18 * 7)76 1.17063 1.09418 1.070 0.28468
## ns(time, df = 18 * 7)77 0.78082 1.09315 0.714 0.47505
## ns(time, df = 18 * 7)78 1.82088 1.05194 1.731 0.08346 .
## ns(time, df = 18 * 7)79 0.84108 1.07610 0.782 0.43445
## ns(time, df = 18 * 7)80 1.67694 1.07462 1.561 0.11864
## ns(time, df = 18 * 7)81 0.53291 1.14352 0.466 0.64119
## ns(time, df = 18 * 7)82 0.80444 1.15642 0.696 0.48666
## ns(time, df = 18 * 7)83 0.92868 1.13176 0.821 0.41189
## ns(time, df = 18 * 7)84 1.46226 1.13611 1.287 0.19807
## ns(time, df = 18 * 7)85 -0.55265 1.23079 -0.449 0.65342
## ns(time, df = 18 * 7)86 2.10641 1.09585 1.922 0.05459 .
## ns(time, df = 18 * 7)87 0.36975 1.11960 0.330 0.74121
## ns(time, df = 18 * 7)88 2.00756 1.10824 1.811 0.07007 .
## ns(time, df = 18 * 7)89 -0.76242 1.24779 -0.611 0.54119
## ns(time, df = 18 * 7)90 1.81504 1.11887 1.622 0.10476
## ns(time, df = 18 * 7)91 0.55858 1.12007 0.499 0.61799
## ns(time, df = 18 * 7)92 1.77309 1.10503 1.605 0.10859
## ns(time, df = 18 * 7)93 -0.01019 1.21066 -0.008 0.99329
## ns(time, df = 18 * 7)94 0.82930 1.18549 0.700 0.48421
## ns(time, df = 18 * 7)95 0.69143 1.12772 0.613 0.53980
## ns(time, df = 18 * 7)96 1.97574 1.07406 1.839 0.06584 .
## ns(time, df = 18 * 7)97 0.34116 1.14793 0.297 0.76632
## ns(time, df = 18 * 7)98 0.73875 1.12357 0.658 0.51086
## ns(time, df = 18 * 7)99 1.66410 1.04636 1.590 0.11175
## ns(time, df = 18 * 7)100 1.50144 1.03143 1.456 0.14548
## ns(time, df = 18 * 7)101 1.42205 1.03806 1.370 0.17072
## ns(time, df = 18 * 7)102 1.27965 1.04629 1.223 0.22132
## ns(time, df = 18 * 7)103 1.35417 1.03790 1.305 0.19199
## ns(time, df = 18 * 7)104 1.74914 1.02943 1.699 0.08929 .
## ns(time, df = 18 * 7)105 1.17080 1.06643 1.098 0.27226
## ns(time, df = 18 * 7)106 0.98909 1.11081 0.890 0.37324
## ns(time, df = 18 * 7)107 0.66169 1.12949 0.586 0.55799
## ns(time, df = 18 * 7)108 1.29168 1.08125 1.195 0.23224
## ns(time, df = 18 * 7)109 1.41442 1.05940 1.335 0.18184
## ns(time, df = 18 * 7)110 1.11219 1.05969 1.050 0.29393
## ns(time, df = 18 * 7)111 1.93111 1.06332 1.816 0.06935 .
## ns(time, df = 18 * 7)112 -0.36815 1.15874 -0.318 0.75070
## ns(time, df = 18 * 7)113 2.27359 1.05166 2.162 0.03063 *
## ns(time, df = 18 * 7)114 1.03321 1.08525 0.952 0.34107
## ns(time, df = 18 * 7)115 0.36201 1.13026 0.320 0.74875
## ns(time, df = 18 * 7)116 2.27659 1.09331 2.082 0.03732 *
## ns(time, df = 18 * 7)117 -0.88943 1.24304 -0.716 0.47428
## ns(time, df = 18 * 7)118 2.11795 1.13169 1.871 0.06128 .
## ns(time, df = 18 * 7)119 -0.23797 1.20060 -0.198 0.84288
## ns(time, df = 18 * 7)120 1.62279 1.13452 1.430 0.15261
## ns(time, df = 18 * 7)121 0.03464 1.14644 0.030 0.97589
## ns(time, df = 18 * 7)122 2.31028 1.06026 2.179 0.02933 *
## ns(time, df = 18 * 7)123 0.07349 1.10273 0.067 0.94686
## ns(time, df = 18 * 7)124 2.04063 0.78462 2.601 0.00930 **
## ns(time, df = 18 * 7)125 2.09203 2.11783 0.988 0.32324
## ns(time, df = 18 * 7)126 -0.53996 0.80683 -0.669 0.50334
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(16102.47) family taken to be 1)
##
## Null deviance: 1149.71 on 938 degrees of freedom
## Residual deviance: 993.98 on 812 degrees of freedom
## AIC: 3031.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 16102
## Std. Err.: 95313
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2775.901## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 9), data = week,
## init.theta = 19631.96389, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3571 -0.9046 -0.1515 0.5473 2.5685
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.46108 0.87852 -0.525 0.59970
## ns(time, df = 18 * 9)1 0.50835 1.18543 0.429 0.66804
## ns(time, df = 18 * 9)2 -1.38563 1.55946 -0.889 0.37425
## ns(time, df = 18 * 9)3 2.26354 1.17453 1.927 0.05396 .
## ns(time, df = 18 * 9)4 -1.14405 1.33504 -0.857 0.39148
## ns(time, df = 18 * 9)5 2.26562 1.13251 2.001 0.04544 *
## ns(time, df = 18 * 9)6 -0.30179 1.20584 -0.250 0.80238
## ns(time, df = 18 * 9)7 1.85672 1.11879 1.660 0.09700 .
## ns(time, df = 18 * 9)8 0.14807 1.15340 0.128 0.89785
## ns(time, df = 18 * 9)9 2.01154 1.10053 1.828 0.06758 .
## ns(time, df = 18 * 9)10 0.11372 1.17778 0.097 0.92308
## ns(time, df = 18 * 9)11 1.32748 1.16847 1.136 0.25592
## ns(time, df = 18 * 9)12 0.62474 1.26482 0.494 0.62135
## ns(time, df = 18 * 9)13 -0.89218 1.47006 -0.607 0.54392
## ns(time, df = 18 * 9)14 0.41011 1.26660 0.324 0.74610
## ns(time, df = 18 * 9)15 1.70862 1.14735 1.489 0.13644
## ns(time, df = 18 * 9)16 -0.07847 1.18085 -0.066 0.94702
## ns(time, df = 18 * 9)17 2.12560 1.10773 1.919 0.05500 .
## ns(time, df = 18 * 9)18 -0.23160 1.17347 -0.197 0.84354
## ns(time, df = 18 * 9)19 2.19846 1.11043 1.980 0.04772 *
## ns(time, df = 18 * 9)20 -0.29132 1.20297 -0.242 0.80865
## ns(time, df = 18 * 9)21 1.62867 1.15198 1.414 0.15742
## ns(time, df = 18 * 9)22 0.24009 1.21011 0.198 0.84273
## ns(time, df = 18 * 9)23 0.72570 1.20026 0.605 0.54543
## ns(time, df = 18 * 9)24 0.90740 1.17400 0.773 0.43957
## ns(time, df = 18 * 9)25 0.80889 1.17972 0.686 0.49293
## ns(time, df = 18 * 9)26 0.45587 1.17574 0.388 0.69821
## ns(time, df = 18 * 9)27 1.48697 1.11486 1.334 0.18228
## ns(time, df = 18 * 9)28 0.77422 1.12336 0.689 0.49070
## ns(time, df = 18 * 9)29 1.53721 1.12820 1.363 0.17303
## ns(time, df = 18 * 9)30 -0.21423 1.21296 -0.177 0.85981
## ns(time, df = 18 * 9)31 1.68763 1.13523 1.487 0.13712
## ns(time, df = 18 * 9)32 0.28894 1.15937 0.249 0.80319
## ns(time, df = 18 * 9)33 1.73704 1.12597 1.543 0.12290
## ns(time, df = 18 * 9)34 0.37594 1.22438 0.307 0.75881
## ns(time, df = 18 * 9)35 0.14409 1.34924 0.107 0.91495
## ns(time, df = 18 * 9)36 -0.39412 1.35381 -0.291 0.77096
## ns(time, df = 18 * 9)37 1.26041 1.17061 1.077 0.28161
## ns(time, df = 18 * 9)38 1.09079 1.12550 0.969 0.33246
## ns(time, df = 18 * 9)39 1.06695 1.12720 0.947 0.34387
## ns(time, df = 18 * 9)40 0.73080 1.13127 0.646 0.51828
## ns(time, df = 18 * 9)41 1.99492 1.13770 1.753 0.07952 .
## ns(time, df = 18 * 9)42 -1.26231 1.37164 -0.920 0.35742
## ns(time, df = 18 * 9)43 1.22996 1.21626 1.011 0.31189
## ns(time, df = 18 * 9)44 0.60752 1.17078 0.519 0.60383
## ns(time, df = 18 * 9)45 1.21414 1.12445 1.080 0.28025
## ns(time, df = 18 * 9)46 1.01414 1.11294 0.911 0.36218
## ns(time, df = 18 * 9)47 1.26218 1.10919 1.138 0.25515
## ns(time, df = 18 * 9)48 0.62213 1.11902 0.556 0.57824
## ns(time, df = 18 * 9)49 1.93133 1.08646 1.778 0.07546 .
## ns(time, df = 18 * 9)50 0.18150 1.15554 0.157 0.87519
## ns(time, df = 18 * 9)51 1.32026 1.11427 1.185 0.23607
## ns(time, df = 18 * 9)52 1.22620 1.10596 1.109 0.26755
## ns(time, df = 18 * 9)53 1.34212 1.16596 1.151 0.24970
## ns(time, df = 18 * 9)54 -1.35709 1.34647 -1.008 0.31351
## ns(time, df = 18 * 9)55 2.54873 1.14834 2.219 0.02645 *
## ns(time, df = 18 * 9)56 -0.76790 1.22905 -0.625 0.53211
## ns(time, df = 18 * 9)57 2.01457 1.09678 1.837 0.06624 .
## ns(time, df = 18 * 9)58 0.93352 1.10126 0.848 0.39661
## ns(time, df = 18 * 9)59 1.18156 1.11794 1.057 0.29055
## ns(time, df = 18 * 9)60 0.67465 1.14177 0.591 0.55460
## ns(time, df = 18 * 9)61 1.25840 1.12405 1.120 0.26292
## ns(time, df = 18 * 9)62 0.62535 1.12755 0.555 0.57916
## ns(time, df = 18 * 9)63 1.86872 1.10105 1.697 0.08966 .
## ns(time, df = 18 * 9)64 -0.04099 1.18580 -0.035 0.97243
## ns(time, df = 18 * 9)65 1.10809 1.10535 1.002 0.31611
## ns(time, df = 18 * 9)66 2.13312 1.06074 2.011 0.04433 *
## ns(time, df = 18 * 9)67 0.34640 1.13782 0.304 0.76079
## ns(time, df = 18 * 9)68 1.24098 1.14982 1.079 0.28046
## ns(time, df = 18 * 9)69 0.40522 1.19363 0.339 0.73424
## ns(time, df = 18 * 9)70 1.07135 1.19269 0.898 0.36905
## ns(time, df = 18 * 9)71 0.15460 1.24346 0.124 0.90106
## ns(time, df = 18 * 9)72 0.75620 1.18994 0.635 0.52510
## ns(time, df = 18 * 9)73 1.10560 1.13661 0.973 0.33069
## ns(time, df = 18 * 9)74 1.05005 1.12560 0.933 0.35088
## ns(time, df = 18 * 9)75 1.05189 1.13618 0.926 0.35454
## ns(time, df = 18 * 9)76 0.65307 1.16098 0.563 0.57377
## ns(time, df = 18 * 9)77 0.96772 1.13851 0.850 0.39533
## ns(time, df = 18 * 9)78 1.18195 1.11959 1.056 0.29111
## ns(time, df = 18 * 9)79 0.84588 1.12459 0.752 0.45195
## ns(time, df = 18 * 9)80 1.61147 1.13376 1.421 0.15521
## ns(time, df = 18 * 9)81 -0.76500 1.23810 -0.618 0.53665
## ns(time, df = 18 * 9)82 2.40743 1.09461 2.199 0.02785 *
## ns(time, df = 18 * 9)83 0.17412 1.13417 0.154 0.87798
## ns(time, df = 18 * 9)84 1.78280 1.09829 1.623 0.10454
## ns(time, df = 18 * 9)85 0.48400 1.13963 0.425 0.67105
## ns(time, df = 18 * 9)86 0.92934 1.09479 0.849 0.39595
## ns(time, df = 18 * 9)87 2.28398 1.04557 2.184 0.02893 *
## ns(time, df = 18 * 9)88 0.24707 1.09622 0.225 0.82168
## ns(time, df = 18 * 9)89 2.52851 1.06893 2.365 0.01801 *
## ns(time, df = 18 * 9)90 -0.22175 1.21662 -0.182 0.85537
## ns(time, df = 18 * 9)91 0.56836 1.27674 0.445 0.65620
## ns(time, df = 18 * 9)92 0.50928 1.31115 0.388 0.69771
## ns(time, df = 18 * 9)93 0.11088 1.43886 0.077 0.93857
## ns(time, df = 18 * 9)94 -1.60130 1.46590 -1.092 0.27467
## ns(time, df = 18 * 9)95 3.09522 1.18599 2.610 0.00906 **
## ns(time, df = 18 * 9)96 -2.02409 1.35995 -1.488 0.13666
## ns(time, df = 18 * 9)97 2.91553 1.13632 2.566 0.01029 *
## ns(time, df = 18 * 9)98 -0.66925 1.23927 -0.540 0.58917
## ns(time, df = 18 * 9)99 1.34911 1.16530 1.158 0.24697
## ns(time, df = 18 * 9)100 0.60458 1.14335 0.529 0.59696
## ns(time, df = 18 * 9)101 1.67937 1.11091 1.512 0.13061
## ns(time, df = 18 * 9)102 0.18538 1.16366 0.159 0.87343
## ns(time, df = 18 * 9)103 1.74245 1.13220 1.539 0.12381
## ns(time, df = 18 * 9)104 0.04050 1.22194 0.033 0.97356
## ns(time, df = 18 * 9)105 0.96393 1.22799 0.785 0.43247
## ns(time, df = 18 * 9)106 0.04127 1.27191 0.032 0.97412
## ns(time, df = 18 * 9)107 0.83793 1.20876 0.693 0.48817
## ns(time, df = 18 * 9)108 1.12708 1.20241 0.937 0.34858
## ns(time, df = 18 * 9)109 -0.09186 1.32229 -0.069 0.94462
## ns(time, df = 18 * 9)110 -0.06136 1.28650 -0.048 0.96196
## ns(time, df = 18 * 9)111 1.72759 1.15831 1.491 0.13584
## ns(time, df = 18 * 9)112 0.12189 1.20168 0.101 0.91921
## ns(time, df = 18 * 9)113 1.32217 1.17150 1.129 0.25906
## ns(time, df = 18 * 9)114 0.61535 1.24906 0.493 0.62226
## ns(time, df = 18 * 9)115 -0.70483 1.37593 -0.512 0.60847
## ns(time, df = 18 * 9)116 1.23954 1.20260 1.031 0.30267
## ns(time, df = 18 * 9)117 0.90272 1.18197 0.764 0.44502
## ns(time, df = 18 * 9)118 0.27810 1.20445 0.231 0.81740
## ns(time, df = 18 * 9)119 1.81668 1.18838 1.529 0.12634
## ns(time, df = 18 * 9)120 -1.23217 1.41275 -0.872 0.38311
## ns(time, df = 18 * 9)121 1.15754 1.27828 0.906 0.36518
## ns(time, df = 18 * 9)122 -0.11051 1.26176 -0.088 0.93021
## ns(time, df = 18 * 9)123 1.42177 1.15315 1.233 0.21759
## ns(time, df = 18 * 9)124 0.83922 1.15601 0.726 0.46786
## ns(time, df = 18 * 9)125 1.16710 1.21607 0.960 0.33719
## ns(time, df = 18 * 9)126 -1.47506 1.37522 -1.073 0.28345
## ns(time, df = 18 * 9)127 2.47153 1.12089 2.205 0.02746 *
## ns(time, df = 18 * 9)128 0.12123 1.13372 0.107 0.91484
## ns(time, df = 18 * 9)129 2.03423 1.07744 1.888 0.05902 .
## ns(time, df = 18 * 9)130 0.53597 1.11486 0.481 0.63070
## ns(time, df = 18 * 9)131 1.50574 1.10229 1.366 0.17194
## ns(time, df = 18 * 9)132 0.58763 1.12571 0.522 0.60167
## ns(time, df = 18 * 9)133 1.44725 1.08868 1.329 0.18373
## ns(time, df = 18 * 9)134 1.26211 1.09074 1.157 0.24723
## ns(time, df = 18 * 9)135 1.00298 1.12267 0.893 0.37165
## ns(time, df = 18 * 9)136 0.94975 1.16615 0.814 0.41540
## ns(time, df = 18 * 9)137 0.22668 1.22773 0.185 0.85352
## ns(time, df = 18 * 9)138 0.98850 1.20019 0.824 0.41015
## ns(time, df = 18 * 9)139 0.16918 1.18915 0.142 0.88687
## ns(time, df = 18 * 9)140 1.96417 1.11042 1.769 0.07692 .
## ns(time, df = 18 * 9)141 0.07020 1.16562 0.060 0.95198
## ns(time, df = 18 * 9)142 1.55931 1.10845 1.407 0.15950
## ns(time, df = 18 * 9)143 1.32826 1.14054 1.165 0.24418
## ns(time, df = 18 * 9)144 -0.62576 1.28521 -0.487 0.62634
## ns(time, df = 18 * 9)145 1.39186 1.14566 1.215 0.22441
## ns(time, df = 18 * 9)146 1.22905 1.11572 1.102 0.27065
## ns(time, df = 18 * 9)147 1.00047 1.15737 0.864 0.38735
## ns(time, df = 18 * 9)148 0.05387 1.22510 0.044 0.96493
## ns(time, df = 18 * 9)149 1.33053 1.16293 1.144 0.25257
## ns(time, df = 18 * 9)150 0.92641 1.21463 0.763 0.44564
## ns(time, df = 18 * 9)151 -0.75061 1.37288 -0.547 0.58455
## ns(time, df = 18 * 9)152 1.16280 1.22703 0.948 0.34330
## ns(time, df = 18 * 9)153 0.91069 1.24102 0.734 0.46306
## ns(time, df = 18 * 9)154 -0.89236 1.35219 -0.660 0.50929
## ns(time, df = 18 * 9)155 2.08839 1.19787 1.743 0.08126 .
## ns(time, df = 18 * 9)156 -1.13392 1.28677 -0.881 0.37820
## ns(time, df = 18 * 9)157 2.61125 1.12145 2.328 0.01989 *
## ns(time, df = 18 * 9)158 -0.45513 1.20609 -0.377 0.70591
## ns(time, df = 18 * 9)159 1.51720 1.11886 1.356 0.17509
## ns(time, df = 18 * 9)160 1.17830 0.87074 1.353 0.17598
## ns(time, df = 18 * 9)161 0.78064 2.20573 0.354 0.72340
## ns(time, df = 18 * 9)162 0.06558 0.88240 0.074 0.94076
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(19631.96) family taken to be 1)
##
## Null deviance: 1149.7 on 938 degrees of freedom
## Residual deviance: 936.9 on 776 degrees of freedom
## AIC: 3046.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 19632
## Std. Err.: 101551
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2718.814## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 10), data = week,
## init.theta = 20157.87656, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2730 -0.8592 -0.1481 0.5279 2.2001
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.14084 0.83490 -0.169 0.86604
## ns(time, df = 18 * 10)1 1.73260 1.18476 1.462 0.14363
## ns(time, df = 18 * 10)2 -4.08005 2.03095 -2.009 0.04454 *
## ns(time, df = 18 * 10)3 2.43982 1.22649 1.989 0.04667 *
## ns(time, df = 18 * 10)4 -1.01121 1.35389 -0.747 0.45513
## ns(time, df = 18 * 10)5 0.48527 1.19631 0.406 0.68501
## ns(time, df = 18 * 10)6 1.14861 1.14612 1.002 0.31626
## ns(time, df = 18 * 10)7 -0.30342 1.18465 -0.256 0.79785
## ns(time, df = 18 * 10)8 1.45450 1.11241 1.308 0.19103
## ns(time, df = 18 * 10)9 -0.12331 1.14342 -0.108 0.91412
## ns(time, df = 18 * 10)10 1.59125 1.08834 1.462 0.14372
## ns(time, df = 18 * 10)11 0.01069 1.15237 0.009 0.99260
## ns(time, df = 18 * 10)12 0.98338 1.15456 0.852 0.39436
## ns(time, df = 18 * 10)13 0.03027 1.23197 0.025 0.98040
## ns(time, df = 18 * 10)14 0.70830 1.33511 0.531 0.59575
## ns(time, df = 18 * 10)15 -2.55458 1.66231 -1.537 0.12435
## ns(time, df = 18 * 10)16 1.55667 1.19981 1.297 0.19448
## ns(time, df = 18 * 10)17 0.51303 1.16051 0.442 0.65844
## ns(time, df = 18 * 10)18 0.00747 1.16473 0.006 0.99488
## ns(time, df = 18 * 10)19 1.69644 1.09526 1.549 0.12141
## ns(time, df = 18 * 10)20 -0.51233 1.17474 -0.436 0.66275
## ns(time, df = 18 * 10)21 1.65228 1.09807 1.505 0.13240
## ns(time, df = 18 * 10)22 0.04105 1.16525 0.035 0.97190
## ns(time, df = 18 * 10)23 0.48883 1.17099 0.417 0.67635
## ns(time, df = 18 * 10)24 0.77932 1.16247 0.670 0.50260
## ns(time, df = 18 * 10)25 0.05070 1.21831 0.042 0.96681
## ns(time, df = 18 * 10)26 0.35249 1.19885 0.294 0.76874
## ns(time, df = 18 * 10)27 0.69784 1.16303 0.600 0.54849
## ns(time, df = 18 * 10)28 0.47024 1.18332 0.397 0.69108
## ns(time, df = 18 * 10)29 -0.10887 1.18660 -0.092 0.92690
## ns(time, df = 18 * 10)30 1.54679 1.09708 1.410 0.15856
## ns(time, df = 18 * 10)31 -0.08007 1.13404 -0.071 0.94371
## ns(time, df = 18 * 10)32 1.72377 1.09604 1.573 0.11578
## ns(time, df = 18 * 10)33 -0.50925 1.21771 -0.418 0.67580
## ns(time, df = 18 * 10)34 0.62777 1.16295 0.540 0.58933
## ns(time, df = 18 * 10)35 1.01345 1.12600 0.900 0.36810
## ns(time, df = 18 * 10)36 -0.06083 1.15719 -0.053 0.95808
## ns(time, df = 18 * 10)37 1.71270 1.11328 1.538 0.12394
## ns(time, df = 18 * 10)38 -0.50550 1.27219 -0.397 0.69111
## ns(time, df = 18 * 10)39 0.18728 1.35686 0.138 0.89022
## ns(time, df = 18 * 10)40 -0.88807 1.40347 -0.633 0.52689
## ns(time, df = 18 * 10)41 0.73560 1.19450 0.616 0.53801
## ns(time, df = 18 * 10)42 0.81466 1.11877 0.728 0.46650
## ns(time, df = 18 * 10)43 0.84244 1.11077 0.758 0.44819
## ns(time, df = 18 * 10)44 0.39406 1.12699 0.350 0.72660
## ns(time, df = 18 * 10)45 1.09433 1.10346 0.992 0.32133
## ns(time, df = 18 * 10)46 0.84655 1.17625 0.720 0.47171
## ns(time, df = 18 * 10)47 -1.33768 1.40303 -0.953 0.34038
## ns(time, df = 18 * 10)48 0.93202 1.21105 0.770 0.44154
## ns(time, df = 18 * 10)49 0.32707 1.16524 0.281 0.77895
## ns(time, df = 18 * 10)50 0.80604 1.12079 0.719 0.47203
## ns(time, df = 18 * 10)51 0.71989 1.10081 0.654 0.51313
## ns(time, df = 18 * 10)52 1.02903 1.09500 0.940 0.34734
## ns(time, df = 18 * 10)53 0.19612 1.12199 0.175 0.86124
## ns(time, df = 18 * 10)54 1.35439 1.07214 1.263 0.20650
## ns(time, df = 18 * 10)55 0.68205 1.10309 0.618 0.53637
## ns(time, df = 18 * 10)56 0.31453 1.13797 0.276 0.78225
## ns(time, df = 18 * 10)57 0.93877 1.09907 0.854 0.39302
## ns(time, df = 18 * 10)58 0.88580 1.09447 0.809 0.41832
## ns(time, df = 18 * 10)59 1.17691 1.16487 1.010 0.31234
## ns(time, df = 18 * 10)60 -2.14745 1.43785 -1.494 0.13530
## ns(time, df = 18 * 10)61 2.24996 1.15128 1.954 0.05066 .
## ns(time, df = 18 * 10)62 -0.70932 1.22392 -0.580 0.56222
## ns(time, df = 18 * 10)63 0.91252 1.12312 0.812 0.41651
## ns(time, df = 18 * 10)64 1.11398 1.07323 1.038 0.29929
## ns(time, df = 18 * 10)65 0.81613 1.09110 0.748 0.45446
## ns(time, df = 18 * 10)66 0.57401 1.12499 0.510 0.60989
## ns(time, df = 18 * 10)67 0.50337 1.13100 0.445 0.65627
## ns(time, df = 18 * 10)68 0.96072 1.11476 0.862 0.38879
## ns(time, df = 18 * 10)69 0.11959 1.12628 0.106 0.91544
## ns(time, df = 18 * 10)70 1.81334 1.08092 1.678 0.09343 .
## ns(time, df = 18 * 10)71 -0.57087 1.19566 -0.477 0.63304
## ns(time, df = 18 * 10)72 0.95831 1.11201 0.862 0.38881
## ns(time, df = 18 * 10)73 0.90298 1.05721 0.854 0.39304
## ns(time, df = 18 * 10)74 1.88468 1.05581 1.785 0.07425 .
## ns(time, df = 18 * 10)75 -1.08976 1.22110 -0.892 0.37216
## ns(time, df = 18 * 10)76 1.96602 1.12956 1.741 0.08177 .
## ns(time, df = 18 * 10)77 -0.97400 1.25690 -0.775 0.43838
## ns(time, df = 18 * 10)78 1.44014 1.17855 1.222 0.22173
## ns(time, df = 18 * 10)79 -0.59616 1.27788 -0.467 0.64084
## ns(time, df = 18 * 10)80 0.47535 1.19922 0.396 0.69182
## ns(time, df = 18 * 10)81 0.82262 1.13213 0.727 0.46747
## ns(time, df = 18 * 10)82 0.56906 1.12153 0.507 0.61187
## ns(time, df = 18 * 10)83 0.99268 1.11367 0.891 0.37274
## ns(time, df = 18 * 10)84 0.20536 1.15728 0.177 0.85916
## ns(time, df = 18 * 10)85 0.77871 1.13908 0.684 0.49421
## ns(time, df = 18 * 10)86 0.38694 1.13390 0.341 0.73292
## ns(time, df = 18 * 10)87 1.10495 1.10134 1.003 0.31573
## ns(time, df = 18 * 10)88 0.31224 1.12114 0.279 0.78063
## ns(time, df = 18 * 10)89 1.52297 1.12303 1.356 0.17506
## ns(time, df = 18 * 10)90 -1.33977 1.27966 -1.047 0.29511
## ns(time, df = 18 * 10)91 1.85989 1.09112 1.705 0.08828 .
## ns(time, df = 18 * 10)92 0.42648 1.10540 0.386 0.69963
## ns(time, df = 18 * 10)93 0.67662 1.10488 0.612 0.54028
## ns(time, df = 18 * 10)94 1.20332 1.09551 1.098 0.27203
## ns(time, df = 18 * 10)95 -0.21824 1.15010 -0.190 0.84950
## ns(time, df = 18 * 10)96 1.38459 1.04779 1.321 0.18635
## ns(time, df = 18 * 10)97 1.48716 1.03218 1.441 0.14964
## ns(time, df = 18 * 10)98 0.09743 1.08091 0.090 0.92818
## ns(time, df = 18 * 10)99 2.17818 1.04814 2.078 0.03770 *
## ns(time, df = 18 * 10)100 -0.45219 1.20379 -0.376 0.70719
## ns(time, df = 18 * 10)101 0.33053 1.27553 0.259 0.79553
## ns(time, df = 18 * 10)102 -0.10582 1.32074 -0.080 0.93614
## ns(time, df = 18 * 10)103 0.40449 1.37315 0.295 0.76832
## ns(time, df = 18 * 10)104 -1.39265 1.59466 -0.873 0.38249
## ns(time, df = 18 * 10)105 -0.31412 1.31281 -0.239 0.81089
## ns(time, df = 18 * 10)106 2.31712 1.17203 1.977 0.04804 *
## ns(time, df = 18 * 10)107 -2.77345 1.44064 -1.925 0.05421 .
## ns(time, df = 18 * 10)108 3.04404 1.12973 2.694 0.00705 **
## ns(time, df = 18 * 10)109 -1.37848 1.28095 -1.076 0.28187
## ns(time, df = 18 * 10)110 0.93768 1.17542 0.798 0.42502
## ns(time, df = 18 * 10)111 0.58373 1.13375 0.515 0.60665
## ns(time, df = 18 * 10)112 0.70416 1.11322 0.633 0.52703
## ns(time, df = 18 * 10)113 1.08326 1.11378 0.973 0.33075
## ns(time, df = 18 * 10)114 -0.29013 1.17405 -0.247 0.80482
## ns(time, df = 18 * 10)115 1.90095 1.12749 1.686 0.09180 .
## ns(time, df = 18 * 10)116 -1.35063 1.32051 -1.023 0.30640
## ns(time, df = 18 * 10)117 1.41780 1.21650 1.165 0.24382
## ns(time, df = 18 * 10)118 -0.85746 1.31738 -0.651 0.51512
## ns(time, df = 18 * 10)119 0.78552 1.20420 0.652 0.51420
## ns(time, df = 18 * 10)120 0.63891 1.19603 0.534 0.59321
## ns(time, df = 18 * 10)121 0.12765 1.31289 0.097 0.92254
## ns(time, df = 18 * 10)122 -1.38940 1.41212 -0.984 0.32516
## ns(time, df = 18 * 10)123 1.84178 1.16414 1.582 0.11363
## ns(time, df = 18 * 10)124 -0.51374 1.21500 -0.423 0.67242
## ns(time, df = 18 * 10)125 1.25506 1.14929 1.092 0.27482
## ns(time, df = 18 * 10)126 0.02939 1.20549 0.024 0.98055
## ns(time, df = 18 * 10)127 0.78068 1.26073 0.619 0.53577
## ns(time, df = 18 * 10)128 -1.52639 1.44020 -1.060 0.28921
## ns(time, df = 18 * 10)129 1.27970 1.19376 1.072 0.28373
## ns(time, df = 18 * 10)130 0.28385 1.18610 0.239 0.81087
## ns(time, df = 18 * 10)131 0.33100 1.18937 0.278 0.78078
## ns(time, df = 18 * 10)132 0.83859 1.17676 0.713 0.47608
## ns(time, df = 18 * 10)133 0.17182 1.28165 0.134 0.89336
## ns(time, df = 18 * 10)134 -0.94267 1.42063 -0.664 0.50698
## ns(time, df = 18 * 10)135 0.70913 1.26778 0.559 0.57592
## ns(time, df = 18 * 10)136 -0.20090 1.23208 -0.163 0.87048
## ns(time, df = 18 * 10)137 1.20331 1.13449 1.061 0.28884
## ns(time, df = 18 * 10)138 0.29888 1.15703 0.258 0.79616
## ns(time, df = 18 * 10)139 1.25674 1.21888 1.031 0.30251
## ns(time, df = 18 * 10)140 -2.69701 1.53820 -1.753 0.07954 .
## ns(time, df = 18 * 10)141 2.34050 1.13010 2.071 0.03835 *
## ns(time, df = 18 * 10)142 -0.16389 1.13477 -0.144 0.88517
## ns(time, df = 18 * 10)143 1.33162 1.07177 1.242 0.21407
## ns(time, df = 18 * 10)144 0.91905 1.07747 0.853 0.39367
## ns(time, df = 18 * 10)145 0.41812 1.10804 0.377 0.70591
## ns(time, df = 18 * 10)146 1.21920 1.08946 1.119 0.26310
## ns(time, df = 18 * 10)147 0.10075 1.12452 0.090 0.92861
## ns(time, df = 18 * 10)148 1.32603 1.06825 1.241 0.21449
## ns(time, df = 18 * 10)149 0.79488 1.07895 0.737 0.46129
## ns(time, df = 18 * 10)150 0.81042 1.10485 0.734 0.46324
## ns(time, df = 18 * 10)151 0.62470 1.15381 0.541 0.58822
## ns(time, df = 18 * 10)152 -0.01602 1.22382 -0.013 0.98956
## ns(time, df = 18 * 10)153 0.58093 1.20744 0.481 0.63042
## ns(time, df = 18 * 10)154 0.02444 1.21456 0.020 0.98394
## ns(time, df = 18 * 10)155 0.64048 1.13731 0.563 0.57333
## ns(time, df = 18 * 10)156 1.28265 1.10256 1.163 0.24469
## ns(time, df = 18 * 10)157 -0.25006 1.16519 -0.215 0.83007
## ns(time, df = 18 * 10)158 1.41199 1.08959 1.296 0.19501
## ns(time, df = 18 * 10)159 0.82497 1.13608 0.726 0.46775
## ns(time, df = 18 * 10)160 -0.61443 1.28658 -0.478 0.63296
## ns(time, df = 18 * 10)161 0.47015 1.17502 0.400 0.68907
## ns(time, df = 18 * 10)162 1.26703 1.09434 1.158 0.24694
## ns(time, df = 18 * 10)163 0.58629 1.13145 0.518 0.60433
## ns(time, df = 18 * 10)164 0.30990 1.19435 0.259 0.79527
## ns(time, df = 18 * 10)165 0.07368 1.20265 0.061 0.95115
## ns(time, df = 18 * 10)166 1.13333 1.15290 0.983 0.32560
## ns(time, df = 18 * 10)167 0.16311 1.25239 0.130 0.89638
## ns(time, df = 18 * 10)168 -0.72316 1.38575 -0.522 0.60177
## ns(time, df = 18 * 10)169 0.38789 1.24365 0.312 0.75512
## ns(time, df = 18 * 10)170 1.29432 1.22298 1.058 0.28991
## ns(time, df = 18 * 10)171 -2.06785 1.48543 -1.392 0.16390
## ns(time, df = 18 * 10)172 1.74938 1.20944 1.446 0.14805
## ns(time, df = 18 * 10)173 -0.53191 1.27107 -0.418 0.67560
## ns(time, df = 18 * 10)174 0.26516 1.18126 0.224 0.82239
## ns(time, df = 18 * 10)175 1.80614 1.10988 1.627 0.10367
## ns(time, df = 18 * 10)176 -1.15633 1.24454 -0.929 0.35283
## ns(time, df = 18 * 10)177 1.77791 1.08953 1.632 0.10272
## ns(time, df = 18 * 10)178 0.76317 0.91270 0.836 0.40306
## ns(time, df = 18 * 10)179 -0.69084 2.16626 -0.319 0.74980
## ns(time, df = 18 * 10)180 0.64417 0.93293 0.690 0.48989
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(20157.88) family taken to be 1)
##
## Null deviance: 1149.73 on 938 degrees of freedom
## Residual deviance: 912.95 on 758 degrees of freedom
## AIC: 3058.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 20158
## Std. Err.: 98126
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2694.863## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 14), data = week,
## init.theta = 20479.9809, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.6587 -0.8307 -0.1010 0.5224 2.2124
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.7607151 1.1179569 -0.680 0.49622
## ns(time, df = 18 * 14)1 -1.1584761 1.6329522 -0.709 0.47805
## ns(time, df = 18 * 14)2 3.4775806 1.9928994 1.745 0.08099 .
## ns(time, df = 18 * 14)3 -6.0047750 3.5839754 -1.675 0.09385 .
## ns(time, df = 18 * 14)4 1.4915804 1.8547128 0.804 0.42128
## ns(time, df = 18 * 14)5 1.7112833 1.5297101 1.119 0.26327
## ns(time, df = 18 * 14)6 -0.0280834 1.6708738 -0.017 0.98659
## ns(time, df = 18 * 14)7 1.0639155 1.5595804 0.682 0.49512
## ns(time, df = 18 * 14)8 0.8123412 1.4850467 0.547 0.58437
## ns(time, df = 18 * 14)9 2.3281154 1.4245285 1.634 0.10219
## ns(time, df = 18 * 14)10 -0.6114241 1.5870365 -0.385 0.70004
## ns(time, df = 18 * 14)11 2.4568481 1.4153427 1.736 0.08259 .
## ns(time, df = 18 * 14)12 0.4266640 1.4540158 0.293 0.76919
## ns(time, df = 18 * 14)13 2.2208800 1.4081089 1.577 0.11475
## ns(time, df = 18 * 14)14 -0.1502065 1.4782725 -0.102 0.91907
## ns(time, df = 18 * 14)15 3.2879970 1.3708910 2.398 0.01647 *
## ns(time, df = 18 * 14)16 -0.6280094 1.5791451 -0.398 0.69086
## ns(time, df = 18 * 14)17 1.4437958 1.4795500 0.976 0.32915
## ns(time, df = 18 * 14)18 1.8490520 1.4659001 1.261 0.20717
## ns(time, df = 18 * 14)19 -0.4278341 1.6690544 -0.256 0.79769
## ns(time, df = 18 * 14)20 2.2651980 1.6719174 1.355 0.17547
## ns(time, df = 18 * 14)21 -2.3933572 2.3495922 -1.019 0.30838
## ns(time, df = 18 * 14)22 0.3228619 1.7855357 0.181 0.85651
## ns(time, df = 18 * 14)23 1.7875172 1.4728989 1.214 0.22490
## ns(time, df = 18 * 14)24 1.3278819 1.4481109 0.917 0.35916
## ns(time, df = 18 * 14)25 0.8447929 1.4901448 0.567 0.57077
## ns(time, df = 18 * 14)26 1.0402229 1.4423004 0.721 0.47077
## ns(time, df = 18 * 14)27 2.1260310 1.3864854 1.533 0.12518
## ns(time, df = 18 * 14)28 0.6271667 1.4720074 0.426 0.67006
## ns(time, df = 18 * 14)29 1.0977945 1.4469997 0.759 0.44805
## ns(time, df = 18 * 14)30 1.8756520 1.3932294 1.346 0.17822
## ns(time, df = 18 * 14)31 1.1494186 1.4528213 0.791 0.42885
## ns(time, df = 18 * 14)32 0.5901095 1.5288890 0.386 0.69952
## ns(time, df = 18 * 14)33 1.2247915 1.4662291 0.835 0.40353
## ns(time, df = 18 * 14)34 1.8115963 1.4586969 1.242 0.21426
## ns(time, df = 18 * 14)35 -0.3145724 1.6122169 -0.195 0.84530
## ns(time, df = 18 * 14)36 2.0155986 1.4895055 1.353 0.17599
## ns(time, df = 18 * 14)37 0.0912163 1.5591226 0.059 0.95335
## ns(time, df = 18 * 14)38 1.6759688 1.4623509 1.146 0.25176
## ns(time, df = 18 * 14)39 1.0374602 1.4759108 0.703 0.48210
## ns(time, df = 18 * 14)40 1.1898331 1.5242401 0.781 0.43503
## ns(time, df = 18 * 14)41 -0.1719529 1.5623497 -0.110 0.91236
## ns(time, df = 18 * 14)42 2.7419179 1.3861635 1.978 0.04792 *
## ns(time, df = 18 * 14)43 0.3879895 1.4621078 0.265 0.79073
## ns(time, df = 18 * 14)44 1.4633557 1.4108520 1.037 0.29964
## ns(time, df = 18 * 14)45 1.9439637 1.3856557 1.403 0.16064
## ns(time, df = 18 * 14)46 0.7707589 1.4864726 0.519 0.60410
## ns(time, df = 18 * 14)47 0.7444193 1.5302881 0.486 0.62664
## ns(time, df = 18 * 14)48 1.2213485 1.4731619 0.829 0.40707
## ns(time, df = 18 * 14)49 1.2598586 1.4389300 0.876 0.38127
## ns(time, df = 18 * 14)50 1.4393214 1.4284049 1.008 0.31363
## ns(time, df = 18 * 14)51 1.0079005 1.4519043 0.694 0.48756
## ns(time, df = 18 * 14)52 1.3010462 1.4226392 0.915 0.36044
## ns(time, df = 18 * 14)53 2.2754280 1.4543008 1.565 0.11767
## ns(time, df = 18 * 14)54 -1.3144887 1.8696310 -0.703 0.48201
## ns(time, df = 18 * 14)55 1.1122086 1.7115539 0.650 0.51581
## ns(time, df = 18 * 14)56 0.6683905 1.7320710 0.386 0.69958
## ns(time, df = 18 * 14)57 -0.8167683 1.8032962 -0.453 0.65060
## ns(time, df = 18 * 14)58 2.1135913 1.4661068 1.442 0.14941
## ns(time, df = 18 * 14)59 0.9429708 1.4319662 0.659 0.51021
## ns(time, df = 18 * 14)60 1.8039480 1.3978251 1.291 0.19686
## ns(time, df = 18 * 14)61 0.9334636 1.4299108 0.653 0.51388
## ns(time, df = 18 * 14)62 1.6579978 1.4132369 1.173 0.24072
## ns(time, df = 18 * 14)63 0.7816605 1.4296058 0.547 0.58454
## ns(time, df = 18 * 14)64 2.3637391 1.3911403 1.699 0.08929 .
## ns(time, df = 18 * 14)65 0.5614459 1.5703409 0.358 0.72069
## ns(time, df = 18 * 14)66 -0.5616808 1.8177605 -0.309 0.75732
## ns(time, df = 18 * 14)67 1.3162301 1.5950060 0.825 0.40925
## ns(time, df = 18 * 14)68 0.5647349 1.5276806 0.370 0.71163
## ns(time, df = 18 * 14)69 1.9555777 1.4430856 1.355 0.17537
## ns(time, df = 18 * 14)70 0.1747635 1.4933428 0.117 0.90684
## ns(time, df = 18 * 14)71 2.3020709 1.3816717 1.666 0.09568 .
## ns(time, df = 18 * 14)72 0.8864431 1.4201166 0.624 0.53249
## ns(time, df = 18 * 14)73 1.4500913 1.4022830 1.034 0.30109
## ns(time, df = 18 * 14)74 1.7101181 1.3969714 1.224 0.22089
## ns(time, df = 18 * 14)75 0.6182759 1.4479974 0.427 0.66939
## ns(time, df = 18 * 14)76 1.8577513 1.3674950 1.359 0.17430
## ns(time, df = 18 * 14)77 1.9689668 1.3737162 1.433 0.15177
## ns(time, df = 18 * 14)78 0.1814270 1.4925228 0.122 0.90325
## ns(time, df = 18 * 14)79 2.0136675 1.4167059 1.421 0.15521
## ns(time, df = 18 * 14)80 0.5256877 1.4394819 0.365 0.71497
## ns(time, df = 18 * 14)81 2.3250494 1.3616913 1.707 0.08773 .
## ns(time, df = 18 * 14)82 0.9835676 1.4135151 0.696 0.48653
## ns(time, df = 18 * 14)83 1.7480618 1.4701685 1.189 0.23443
## ns(time, df = 18 * 14)84 0.0131366 1.7301559 0.008 0.99394
## ns(time, df = 18 * 14)85 -0.9092103 1.7458293 -0.521 0.60251
## ns(time, df = 18 * 14)86 3.2857109 1.4377624 2.285 0.02230 *
## ns(time, df = 18 * 14)87 -0.5569303 1.5963932 -0.349 0.72719
## ns(time, df = 18 * 14)88 1.4623053 1.4853036 0.985 0.32486
## ns(time, df = 18 * 14)89 1.0924066 1.4096390 0.775 0.43837
## ns(time, df = 18 * 14)90 2.2599346 1.3515881 1.672 0.09451 .
## ns(time, df = 18 * 14)91 0.8220970 1.4038167 0.586 0.55813
## ns(time, df = 18 * 14)92 1.9865022 1.3888002 1.430 0.15261
## ns(time, df = 18 * 14)93 0.6849490 1.4601203 0.469 0.63899
## ns(time, df = 18 * 14)94 1.3951196 1.4367985 0.971 0.33155
## ns(time, df = 18 * 14)95 1.2996415 1.4237456 0.913 0.36133
## ns(time, df = 18 * 14)96 1.3830809 1.4278603 0.969 0.33273
## ns(time, df = 18 * 14)97 0.8652274 1.4328449 0.604 0.54594
## ns(time, df = 18 * 14)98 2.0513504 1.3703990 1.497 0.13442
## ns(time, df = 18 * 14)99 1.3467568 1.4221411 0.947 0.34364
## ns(time, df = 18 * 14)100 0.5174127 1.5268291 0.339 0.73470
## ns(time, df = 18 * 14)101 1.0072562 1.4544191 0.693 0.48859
## ns(time, df = 18 * 14)102 1.9546961 1.3589011 1.438 0.15031
## ns(time, df = 18 * 14)103 1.3847808 1.3418582 1.032 0.30208
## ns(time, df = 18 * 14)104 2.6308754 1.3388438 1.965 0.04941 *
## ns(time, df = 18 * 14)105 -0.0543329 1.5279799 -0.036 0.97163
## ns(time, df = 18 * 14)106 1.1085020 1.4902338 0.744 0.45697
## ns(time, df = 18 * 14)107 1.7298345 1.4384339 1.203 0.22914
## ns(time, df = 18 * 14)108 0.8425473 1.5345740 0.549 0.58298
## ns(time, df = 18 * 14)109 -0.0172532 1.5931232 -0.011 0.99136
## ns(time, df = 18 * 14)110 2.6464135 1.4828049 1.785 0.07430 .
## ns(time, df = 18 * 14)111 -1.2996344 1.7492539 -0.743 0.45750
## ns(time, df = 18 * 14)112 2.1631208 1.5138072 1.429 0.15303
## ns(time, df = 18 * 14)113 0.0152711 1.5275129 0.010 0.99202
## ns(time, df = 18 * 14)114 2.3896177 1.4051835 1.701 0.08902 .
## ns(time, df = 18 * 14)115 0.3010189 1.4615809 0.206 0.83683
## ns(time, df = 18 * 14)116 2.2405926 1.3918540 1.610 0.10744
## ns(time, df = 18 * 14)117 0.6008519 1.4544157 0.413 0.67952
## ns(time, df = 18 * 14)118 1.7823571 1.4426581 1.235 0.21666
## ns(time, df = 18 * 14)119 0.2417519 1.5066328 0.160 0.87252
## ns(time, df = 18 * 14)120 2.1287501 1.4190935 1.500 0.13359
## ns(time, df = 18 * 14)121 0.3440393 1.4671323 0.234 0.81460
## ns(time, df = 18 * 14)122 2.2260559 1.3889645 1.603 0.10901
## ns(time, df = 18 * 14)123 0.6355986 1.4396358 0.441 0.65885
## ns(time, df = 18 * 14)124 1.8053596 1.3972770 1.292 0.19634
## ns(time, df = 18 * 14)125 1.4471417 1.4393513 1.005 0.31470
## ns(time, df = 18 * 14)126 0.3862605 1.5815515 0.244 0.80705
## ns(time, df = 18 * 14)127 0.4597052 1.5247180 0.302 0.76303
## ns(time, df = 18 * 14)128 2.2640308 1.3717599 1.650 0.09885 .
## ns(time, df = 18 * 14)129 1.3421057 1.3943518 0.963 0.33578
## ns(time, df = 18 * 14)130 0.9026636 1.4326463 0.630 0.52865
## ns(time, df = 18 * 14)131 1.8330375 1.3852440 1.323 0.18575
## ns(time, df = 18 * 14)132 1.2358099 1.4102169 0.876 0.38085
## ns(time, df = 18 * 14)133 1.3591155 1.4423114 0.942 0.34603
## ns(time, df = 18 * 14)134 0.4276405 1.4521229 0.294 0.76838
## ns(time, df = 18 * 14)135 2.4838154 1.3211096 1.880 0.06009 .
## ns(time, df = 18 * 14)136 1.6463478 1.3239043 1.244 0.21366
## ns(time, df = 18 * 14)137 1.7033105 1.3515972 1.260 0.20759
## ns(time, df = 18 * 14)138 0.9983808 1.3741742 0.727 0.46751
## ns(time, df = 18 * 14)139 2.5006680 1.3312811 1.878 0.06033 .
## ns(time, df = 18 * 14)140 1.2462343 1.4399275 0.865 0.38677
## ns(time, df = 18 * 14)141 -0.3410688 1.6818252 -0.203 0.83929
## ns(time, df = 18 * 14)142 1.5949390 1.5857400 1.006 0.31451
## ns(time, df = 18 * 14)143 -0.0007113 1.6858314 0.000 0.99966
## ns(time, df = 18 * 14)144 1.1206759 1.6505805 0.679 0.49716
## ns(time, df = 18 * 14)145 0.8326147 1.8304299 0.455 0.64920
## ns(time, df = 18 * 14)146 -2.7015538 2.4102302 -1.121 0.26234
## ns(time, df = 18 * 14)147 2.3641644 1.6704025 1.415 0.15697
## ns(time, df = 18 * 14)148 -0.7027851 1.6142026 -0.435 0.66329
## ns(time, df = 18 * 14)149 4.2399860 1.5229048 2.784 0.00537 **
## ns(time, df = 18 * 14)150 -4.8671426 2.4777609 -1.964 0.04949 *
## ns(time, df = 18 * 14)151 2.8808882 1.5024294 1.917 0.05518 .
## ns(time, df = 18 * 14)152 1.5219539 1.4431007 1.055 0.29159
## ns(time, df = 18 * 14)153 0.1583604 1.5923359 0.099 0.92078
## ns(time, df = 18 * 14)154 1.1573133 1.5283937 0.757 0.44892
## ns(time, df = 18 * 14)155 0.8894921 1.4726556 0.604 0.54584
## ns(time, df = 18 * 14)156 1.9068139 1.4121727 1.350 0.17693
## ns(time, df = 18 * 14)157 0.4914303 1.4487819 0.339 0.73446
## ns(time, df = 18 * 14)158 2.3995495 1.3822303 1.736 0.08256 .
## ns(time, df = 18 * 14)159 0.3940602 1.4806934 0.266 0.79014
## ns(time, df = 18 * 14)160 1.4211404 1.4511086 0.979 0.32741
## ns(time, df = 18 * 14)161 1.2074472 1.4348438 0.842 0.40006
## ns(time, df = 18 * 14)162 1.9090608 1.4739076 1.295 0.19524
## ns(time, df = 18 * 14)163 -1.0365246 1.7483432 -0.593 0.55327
## ns(time, df = 18 * 14)164 2.0726496 1.5304424 1.354 0.17565
## ns(time, df = 18 * 14)165 0.3227759 1.6065731 0.201 0.84077
## ns(time, df = 18 * 14)166 0.7065757 1.6323284 0.433 0.66511
## ns(time, df = 18 * 14)167 0.4088168 1.5516887 0.263 0.79219
## ns(time, df = 18 * 14)168 2.5037823 1.4498974 1.727 0.08419 .
## ns(time, df = 18 * 14)169 -0.5012680 1.6637759 -0.301 0.76320
## ns(time, df = 18 * 14)170 1.4034362 1.6267913 0.863 0.38830
## ns(time, df = 18 * 14)171 0.1072761 1.7593073 0.061 0.95138
## ns(time, df = 18 * 14)172 -0.4611873 1.6744388 -0.275 0.78299
## ns(time, df = 18 * 14)173 3.1826144 1.4350365 2.218 0.02657 *
## ns(time, df = 18 * 14)174 -0.8167034 1.6213250 -0.504 0.61445
## ns(time, df = 18 * 14)175 1.8578603 1.4643574 1.269 0.20454
## ns(time, df = 18 * 14)176 1.2294892 1.4639316 0.840 0.40099
## ns(time, df = 18 * 14)177 0.7271639 1.5295662 0.475 0.63450
## ns(time, df = 18 * 14)178 1.4342615 1.5629887 0.918 0.35881
## ns(time, df = 18 * 14)179 -0.1180036 1.8083415 -0.065 0.94797
## ns(time, df = 18 * 14)180 -0.6294394 1.7843450 -0.353 0.72427
## ns(time, df = 18 * 14)181 2.5405864 1.4816002 1.715 0.08639 .
## ns(time, df = 18 * 14)182 -0.1403399 1.5499555 -0.091 0.92785
## ns(time, df = 18 * 14)183 2.1759174 1.4516450 1.499 0.13389
## ns(time, df = 18 * 14)184 0.1822596 1.5407181 0.118 0.90583
## ns(time, df = 18 * 14)185 1.5012880 1.4777635 1.016 0.30967
## ns(time, df = 18 * 14)186 1.3364007 1.5141548 0.883 0.37745
## ns(time, df = 18 * 14)187 0.4056699 1.7534099 0.231 0.81703
## ns(time, df = 18 * 14)188 -1.4888393 1.9809620 -0.752 0.45231
## ns(time, df = 18 * 14)189 2.6901996 1.5870365 1.695 0.09005 .
## ns(time, df = 18 * 14)190 -1.1007431 1.7521312 -0.628 0.52985
## ns(time, df = 18 * 14)191 1.8804887 1.4922801 1.260 0.20762
## ns(time, df = 18 * 14)192 0.9660152 1.4534537 0.665 0.50628
## ns(time, df = 18 * 14)193 1.6470192 1.4313404 1.151 0.24986
## ns(time, df = 18 * 14)194 0.7876442 1.4773157 0.533 0.59392
## ns(time, df = 18 * 14)195 2.3856942 1.5684410 1.521 0.12824
## ns(time, df = 18 * 14)196 -3.2125552 2.4327670 -1.321 0.18666
## ns(time, df = 18 * 14)197 1.2787498 1.6130017 0.793 0.42791
## ns(time, df = 18 * 14)198 1.9907644 1.4132419 1.409 0.15894
## ns(time, df = 18 * 14)199 0.9730773 1.4281799 0.681 0.49566
## ns(time, df = 18 * 14)200 1.3968366 1.3921744 1.003 0.31569
## ns(time, df = 18 * 14)201 1.8862938 1.3580566 1.389 0.16484
## ns(time, df = 18 * 14)202 1.1870860 1.3822867 0.859 0.39046
## ns(time, df = 18 * 14)203 1.9519144 1.3874308 1.407 0.15947
## ns(time, df = 18 * 14)204 0.1660387 1.4573863 0.114 0.90929
## ns(time, df = 18 * 14)205 2.9380085 1.3745057 2.138 0.03256 *
## ns(time, df = 18 * 14)206 -0.6318886 1.5301437 -0.413 0.67964
## ns(time, df = 18 * 14)207 2.5345247 1.3671093 1.854 0.06375 .
## ns(time, df = 18 * 14)208 1.1342028 1.3811989 0.821 0.41155
## ns(time, df = 18 * 14)209 1.5065730 1.3753506 1.095 0.27334
## ns(time, df = 18 * 14)210 1.9065773 1.3772409 1.384 0.16625
## ns(time, df = 18 * 14)211 0.5986723 1.4670241 0.408 0.68321
## ns(time, df = 18 * 14)212 1.6523314 1.4559708 1.135 0.25643
## ns(time, df = 18 * 14)213 0.6873997 1.5397220 0.446 0.65528
## ns(time, df = 18 * 14)214 0.6328050 1.5680726 0.404 0.68654
## ns(time, df = 18 * 14)215 1.3470581 1.5169942 0.888 0.37455
## ns(time, df = 18 * 14)216 0.5423109 1.5480527 0.350 0.72610
## ns(time, df = 18 * 14)217 1.1618345 1.4723662 0.789 0.43006
## ns(time, df = 18 * 14)218 1.4708049 1.4045254 1.047 0.29501
## ns(time, df = 18 * 14)219 1.8644539 1.4036733 1.328 0.18409
## ns(time, df = 18 * 14)220 0.1828629 1.5092146 0.121 0.90356
## ns(time, df = 18 * 14)221 1.7697930 1.4032337 1.261 0.20723
## ns(time, df = 18 * 14)222 1.5834420 1.3829761 1.145 0.25223
## ns(time, df = 18 * 14)223 1.5158407 1.4328017 1.058 0.29008
## ns(time, df = 18 * 14)224 0.6143011 1.5892224 0.387 0.69910
## ns(time, df = 18 * 14)225 -0.1681759 1.6745412 -0.100 0.92000
## ns(time, df = 18 * 14)226 1.6394172 1.4513871 1.130 0.25867
## ns(time, df = 18 * 14)227 1.5288425 1.3912443 1.099 0.27181
## ns(time, df = 18 * 14)228 1.6042685 1.3979384 1.148 0.25114
## ns(time, df = 18 * 14)229 0.9876617 1.4568014 0.678 0.49779
## ns(time, df = 18 * 14)230 1.3735524 1.5028806 0.914 0.36074
## ns(time, df = 18 * 14)231 -0.1205391 1.6008291 -0.075 0.93998
## ns(time, df = 18 * 14)232 1.9698222 1.4483709 1.360 0.17382
## ns(time, df = 18 * 14)233 0.8961776 1.4726464 0.609 0.54282
## ns(time, df = 18 * 14)234 1.5191487 1.5262938 0.995 0.31958
## ns(time, df = 18 * 14)235 -0.3660315 1.7492611 -0.209 0.83425
## ns(time, df = 18 * 14)236 0.9785231 1.6875924 0.580 0.56203
## ns(time, df = 18 * 14)237 -0.1779380 1.6252707 -0.109 0.91282
## ns(time, df = 18 * 14)238 3.0627832 1.4904968 2.055 0.03989 *
## ns(time, df = 18 * 14)239 -1.4132881 1.9061069 -0.741 0.45842
## ns(time, df = 18 * 14)240 0.0215156 1.7829441 0.012 0.99037
## ns(time, df = 18 * 14)241 1.8699557 1.5217681 1.229 0.21915
## ns(time, df = 18 * 14)242 0.5316348 1.5609712 0.341 0.73342
## ns(time, df = 18 * 14)243 1.0167356 1.5663802 0.649 0.51627
## ns(time, df = 18 * 14)244 0.2506600 1.5268834 0.164 0.86960
## ns(time, df = 18 * 14)245 2.5808008 1.3883613 1.859 0.06304 .
## ns(time, df = 18 * 14)246 0.6965160 1.4943486 0.466 0.64114
## ns(time, df = 18 * 14)247 0.0945839 1.5664128 0.060 0.95185
## ns(time, df = 18 * 14)248 2.1111443 1.3947002 1.514 0.13010
## ns(time, df = 18 * 14)249 1.3371960 1.3936054 0.960 0.33730
## ns(time, df = 18 * 14)250 1.0035833 1.1153518 0.900 0.36823
## ns(time, df = 18 * 14)251 1.8574769 2.7989203 0.664 0.50692
## ns(time, df = 18 * 14)252 -0.3135889 1.0777250 -0.291 0.77107
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(20479.98) family taken to be 1)
##
## Null deviance: 1149.73 on 938 degrees of freedom
## Residual deviance: 843.44 on 686 degrees of freedom
## AIC: 3133.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 20480
## Std. Err.: 84363
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2625.347## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 15), data = week,
## init.theta = 21819.86914, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5398 -0.8189 -0.1214 0.5090 2.2049
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.09154 1.26513 -0.863 0.3883
## ns(time, df = 18 * 15)1 -2.10234 1.91810 -1.096 0.2731
## ns(time, df = 18 * 15)2 4.62206 2.17796 2.122 0.0338 *
## ns(time, df = 18 * 15)3 -5.31501 3.58915 -1.481 0.1386
## ns(time, df = 18 * 15)4 0.06596 2.29180 0.029 0.9770
## ns(time, df = 18 * 15)5 2.49150 1.68355 1.480 0.1389
## ns(time, df = 18 * 15)6 0.80090 1.74701 0.458 0.6466
## ns(time, df = 18 * 15)7 0.86386 1.75354 0.493 0.6223
## ns(time, df = 18 * 15)8 1.22863 1.66663 0.737 0.4610
## ns(time, df = 18 * 15)9 1.79068 1.57462 1.137 0.2554
## ns(time, df = 18 * 15)10 1.93344 1.58610 1.219 0.2228
## ns(time, df = 18 * 15)11 0.26995 1.68146 0.161 0.8725
## ns(time, df = 18 * 15)12 2.57728 1.54883 1.664 0.0961 .
## ns(time, df = 18 * 15)13 0.93239 1.58224 0.589 0.5557
## ns(time, df = 18 * 15)14 2.33497 1.54738 1.509 0.1313
## ns(time, df = 18 * 15)15 0.59284 1.59968 0.371 0.7109
## ns(time, df = 18 * 15)16 2.82578 1.50727 1.875 0.0608 .
## ns(time, df = 18 * 15)17 1.45666 1.60809 0.906 0.3650
## ns(time, df = 18 * 15)18 -0.54551 1.77742 -0.307 0.7589
## ns(time, df = 18 * 15)19 3.86620 1.57688 2.452 0.0142 *
## ns(time, df = 18 * 15)20 -1.21687 1.86609 -0.652 0.5143
## ns(time, df = 18 * 15)21 2.79801 1.73072 1.617 0.1059
## ns(time, df = 18 * 15)22 -0.34816 2.07274 -0.168 0.8666
## ns(time, df = 18 * 15)23 -0.77515 2.31267 -0.335 0.7375
## ns(time, df = 18 * 15)24 1.21125 1.76675 0.686 0.4930
## ns(time, df = 18 * 15)25 2.11826 1.57787 1.342 0.1794
## ns(time, df = 18 * 15)26 1.55474 1.58862 0.979 0.3277
## ns(time, df = 18 * 15)27 1.09506 1.63143 0.671 0.5021
## ns(time, df = 18 * 15)28 1.52927 1.56859 0.975 0.3296
## ns(time, df = 18 * 15)29 2.33340 1.52253 1.533 0.1254
## ns(time, df = 18 * 15)30 1.18283 1.59886 0.740 0.4594
## ns(time, df = 18 * 15)31 1.08285 1.60387 0.675 0.4996
## ns(time, df = 18 * 15)32 2.42948 1.52455 1.594 0.1110
## ns(time, df = 18 * 15)33 1.27724 1.57686 0.810 0.4179
## ns(time, df = 18 * 15)34 1.70561 1.62260 1.051 0.2932
## ns(time, df = 18 * 15)35 0.32597 1.68095 0.194 0.8462
## ns(time, df = 18 * 15)36 2.94135 1.56376 1.881 0.0600 .
## ns(time, df = 18 * 15)37 0.08199 1.71726 0.048 0.9619
## ns(time, df = 18 * 15)38 1.82533 1.65027 1.106 0.2687
## ns(time, df = 18 * 15)39 1.09213 1.66300 0.657 0.5114
## ns(time, df = 18 * 15)40 1.31972 1.64305 0.803 0.4219
## ns(time, df = 18 * 15)41 1.56489 1.60334 0.976 0.3291
## ns(time, df = 18 * 15)42 1.65670 1.60393 1.033 0.3016
## ns(time, df = 18 * 15)43 1.29399 1.67786 0.771 0.4406
## ns(time, df = 18 * 15)44 0.13684 1.70766 0.080 0.9361
## ns(time, df = 18 * 15)45 3.10055 1.52180 2.037 0.0416 *
## ns(time, df = 18 * 15)46 0.77430 1.59248 0.486 0.6268
## ns(time, df = 18 * 15)47 1.76333 1.55448 1.134 0.2566
## ns(time, df = 18 * 15)48 2.00150 1.52155 1.315 0.1884
## ns(time, df = 18 * 15)49 1.88274 1.56665 1.202 0.2295
## ns(time, df = 18 * 15)50 0.49875 1.69787 0.294 0.7689
## ns(time, df = 18 * 15)51 1.76442 1.62422 1.086 0.2773
## ns(time, df = 18 * 15)52 1.17611 1.60206 0.734 0.4629
## ns(time, df = 18 * 15)53 2.14741 1.55298 1.383 0.1667
## ns(time, df = 18 * 15)54 1.08525 1.59537 0.680 0.4963
## ns(time, df = 18 * 15)55 1.85227 1.56274 1.185 0.2359
## ns(time, df = 18 * 15)56 1.50716 1.55821 0.967 0.3334
## ns(time, df = 18 * 15)57 2.59962 1.60692 1.618 0.1057
## ns(time, df = 18 * 15)58 -1.28490 2.07751 -0.618 0.5363
## ns(time, df = 18 * 15)59 1.54463 1.84830 0.836 0.4033
## ns(time, df = 18 * 15)60 1.05716 1.86742 0.566 0.5713
## ns(time, df = 18 * 15)61 -0.63270 2.00036 -0.316 0.7518
## ns(time, df = 18 * 15)62 2.15365 1.63040 1.321 0.1865
## ns(time, df = 18 * 15)63 1.57508 1.56594 1.006 0.3145
## ns(time, df = 18 * 15)64 1.69895 1.54498 1.100 0.2715
## ns(time, df = 18 * 15)65 1.95382 1.53933 1.269 0.2043
## ns(time, df = 18 * 15)66 1.17784 1.57692 0.747 0.4551
## ns(time, df = 18 * 15)67 2.05650 1.54297 1.333 0.1826
## ns(time, df = 18 * 15)68 1.22129 1.55368 0.786 0.4318
## ns(time, df = 18 * 15)69 2.85629 1.54613 1.847 0.0647 .
## ns(time, df = 18 * 15)70 -0.12643 1.83958 -0.069 0.9452
## ns(time, df = 18 * 15)71 0.38638 1.90807 0.202 0.8395
## ns(time, df = 18 * 15)72 1.48341 1.72055 0.862 0.3886
## ns(time, df = 18 * 15)73 1.02162 1.65162 0.619 0.5362
## ns(time, df = 18 * 15)74 2.22853 1.57850 1.412 0.1580
## ns(time, df = 18 * 15)75 0.56274 1.63047 0.345 0.7300
## ns(time, df = 18 * 15)76 2.45360 1.52379 1.610 0.1074
## ns(time, df = 18 * 15)77 1.49842 1.54405 0.970 0.3318
## ns(time, df = 18 * 15)78 1.51440 1.54865 0.978 0.3281
## ns(time, df = 18 * 15)79 2.18819 1.52493 1.435 0.1513
## ns(time, df = 18 * 15)80 1.14485 1.57805 0.725 0.4682
## ns(time, df = 18 * 15)81 1.68663 1.53592 1.098 0.2721
## ns(time, df = 18 * 15)82 2.27915 1.49328 1.526 0.1269
## ns(time, df = 18 * 15)83 1.88891 1.54316 1.224 0.2209
## ns(time, df = 18 * 15)84 0.39608 1.64754 0.240 0.8100
## ns(time, df = 18 * 15)85 2.71731 1.53929 1.765 0.0775 .
## ns(time, df = 18 * 15)86 0.45455 1.59044 0.286 0.7750
## ns(time, df = 18 * 15)87 3.04095 1.49110 2.039 0.0414 *
## ns(time, df = 18 * 15)88 0.97599 1.56538 0.623 0.5330
## ns(time, df = 18 * 15)89 2.20497 1.60383 1.375 0.1692
## ns(time, df = 18 * 15)90 0.56385 1.86032 0.303 0.7618
## ns(time, df = 18 * 15)91 -1.39849 2.03781 -0.686 0.4925
## ns(time, df = 18 * 15)92 3.72405 1.58532 2.349 0.0188 *
## ns(time, df = 18 * 15)93 0.11009 1.68480 0.065 0.9479
## ns(time, df = 18 * 15)94 1.85501 1.63754 1.133 0.2573
## ns(time, df = 18 * 15)95 0.69467 1.60696 0.432 0.6655
## ns(time, df = 18 * 15)96 2.98449 1.48801 2.006 0.0449 *
## ns(time, df = 18 * 15)97 0.96273 1.53637 0.627 0.5309
## ns(time, df = 18 * 15)98 2.61024 1.50583 1.733 0.0830 .
## ns(time, df = 18 * 15)99 0.88271 1.58339 0.557 0.5772
## ns(time, df = 18 * 15)100 2.09998 1.56183 1.345 0.1788
## ns(time, df = 18 * 15)101 0.95686 1.59889 0.598 0.5495
## ns(time, df = 18 * 15)102 2.18664 1.54492 1.415 0.1570
## ns(time, df = 18 * 15)103 1.27781 1.57865 0.809 0.4183
## ns(time, df = 18 * 15)104 1.55186 1.55933 0.995 0.3196
## ns(time, df = 18 * 15)105 1.98347 1.51380 1.310 0.1901
## ns(time, df = 18 * 15)106 2.24417 1.53595 1.461 0.1440
## ns(time, df = 18 * 15)107 0.28463 1.69227 0.168 0.8664
## ns(time, df = 18 * 15)108 1.87389 1.59220 1.177 0.2392
## ns(time, df = 18 * 15)109 1.37998 1.53952 0.896 0.3701
## ns(time, df = 18 * 15)110 2.46343 1.46821 1.678 0.0934 .
## ns(time, df = 18 * 15)111 1.98704 1.47193 1.350 0.1770
## ns(time, df = 18 * 15)112 2.37728 1.51988 1.564 0.1178
## ns(time, df = 18 * 15)113 0.01313 1.72218 0.008 0.9939
## ns(time, df = 18 * 15)114 1.88840 1.59681 1.183 0.2370
## ns(time, df = 18 * 15)115 2.00398 1.58112 1.267 0.2050
## ns(time, df = 18 * 15)116 0.69099 1.72110 0.401 0.6881
## ns(time, df = 18 * 15)117 0.76572 1.69459 0.452 0.6514
## ns(time, df = 18 * 15)118 2.86350 1.62269 1.765 0.0776 .
## ns(time, df = 18 * 15)119 -1.27487 1.92719 -0.662 0.5083
## ns(time, df = 18 * 15)120 2.91845 1.64783 1.771 0.0765 .
## ns(time, df = 18 * 15)121 -0.31559 1.71438 -0.184 0.8539
## ns(time, df = 18 * 15)122 3.24115 1.53977 2.105 0.0353 *
## ns(time, df = 18 * 15)123 0.04992 1.63006 0.031 0.9756
## ns(time, df = 18 * 15)124 3.03717 1.52173 1.996 0.0459 *
## ns(time, df = 18 * 15)125 0.42059 1.60596 0.262 0.7934
## ns(time, df = 18 * 15)126 2.74719 1.55210 1.770 0.0767 .
## ns(time, df = 18 * 15)127 0.14674 1.68074 0.087 0.9304
## ns(time, df = 18 * 15)128 2.18985 1.57196 1.393 0.1636
## ns(time, df = 18 * 15)129 1.47102 1.58135 0.930 0.3523
## ns(time, df = 18 * 15)130 1.14645 1.58219 0.725 0.4687
## ns(time, df = 18 * 15)131 2.56096 1.52189 1.683 0.0924 .
## ns(time, df = 18 * 15)132 0.68430 1.59239 0.430 0.6674
## ns(time, df = 18 * 15)133 2.47424 1.52552 1.622 0.1048
## ns(time, df = 18 * 15)134 1.38147 1.58467 0.872 0.3833
## ns(time, df = 18 * 15)135 1.38534 1.69045 0.820 0.4125
## ns(time, df = 18 * 15)136 -0.28147 1.75646 -0.160 0.8727
## ns(time, df = 18 * 15)137 3.25223 1.51095 2.152 0.0314 *
## ns(time, df = 18 * 15)138 0.94832 1.54697 0.613 0.5399
## ns(time, df = 18 * 15)139 2.43769 1.52839 1.595 0.1107
## ns(time, df = 18 * 15)140 0.57083 1.58728 0.360 0.7191
## ns(time, df = 18 * 15)141 3.03395 1.50598 2.015 0.0439 *
## ns(time, df = 18 * 15)142 0.46268 1.61299 0.287 0.7742
## ns(time, df = 18 * 15)143 2.23224 1.56566 1.426 0.1539
## ns(time, df = 18 * 15)144 0.58474 1.56639 0.373 0.7089
## ns(time, df = 18 * 15)145 3.36101 1.44268 2.330 0.0198 *
## ns(time, df = 18 * 15)146 1.36467 1.48216 0.921 0.3572
## ns(time, df = 18 * 15)147 2.38162 1.48469 1.604 0.1087
## ns(time, df = 18 * 15)148 1.13125 1.51654 0.746 0.4557
## ns(time, df = 18 * 15)149 2.92135 1.46575 1.993 0.0463 *
## ns(time, df = 18 * 15)150 1.63323 1.56544 1.043 0.2968
## ns(time, df = 18 * 15)151 0.03945 1.81540 0.022 0.9827
## ns(time, df = 18 * 15)152 1.61050 1.72898 0.931 0.3516
## ns(time, df = 18 * 15)153 1.03035 1.78093 0.579 0.5629
## ns(time, df = 18 * 15)154 0.21899 1.84878 0.118 0.9057
## ns(time, df = 18 * 15)155 2.90008 1.86105 1.558 0.1192
## ns(time, df = 18 * 15)156 -3.84551 2.96347 -1.298 0.1944
## ns(time, df = 18 * 15)157 2.54981 1.93766 1.316 0.1882
## ns(time, df = 18 * 15)158 -0.39396 1.85833 -0.212 0.8321
## ns(time, df = 18 * 15)159 2.59848 1.59457 1.630 0.1032
## ns(time, df = 18 * 15)160 1.98273 1.68454 1.177 0.2392
## ns(time, df = 18 * 15)161 -2.62010 2.30672 -1.136 0.2560
## ns(time, df = 18 * 15)162 3.18642 1.60002 1.991 0.0464 *
## ns(time, df = 18 * 15)163 1.73402 1.57838 1.099 0.2719
## ns(time, df = 18 * 15)164 0.55352 1.72830 0.320 0.7488
## ns(time, df = 18 * 15)165 1.35691 1.67582 0.810 0.4181
## ns(time, df = 18 * 15)166 1.30891 1.61046 0.813 0.4164
## ns(time, df = 18 * 15)167 2.07373 1.55426 1.334 0.1821
## ns(time, df = 18 * 15)168 1.09389 1.57660 0.694 0.4878
## ns(time, df = 18 * 15)169 2.25997 1.52504 1.482 0.1384
## ns(time, df = 18 * 15)170 1.46931 1.56345 0.940 0.3473
## ns(time, df = 18 * 15)171 1.49445 1.60433 0.932 0.3516
## ns(time, df = 18 * 15)172 0.99940 1.60208 0.624 0.5328
## ns(time, df = 18 * 15)173 2.83463 1.54918 1.830 0.0673 .
## ns(time, df = 18 * 15)174 0.09657 1.75815 0.055 0.9562
## ns(time, df = 18 * 15)175 1.07059 1.74720 0.613 0.5400
## ns(time, df = 18 * 15)176 1.52871 1.67506 0.913 0.3614
## ns(time, df = 18 * 15)177 1.24903 1.71856 0.727 0.4674
## ns(time, df = 18 * 15)178 0.46726 1.80349 0.259 0.7956
## ns(time, df = 18 * 15)179 1.22692 1.66575 0.737 0.4614
## ns(time, df = 18 * 15)180 2.28687 1.58384 1.444 0.1488
## ns(time, df = 18 * 15)181 0.90171 1.71327 0.526 0.5987
## ns(time, df = 18 * 15)182 0.43737 1.83055 0.239 0.8112
## ns(time, df = 18 * 15)183 1.78258 1.79056 0.996 0.3195
## ns(time, df = 18 * 15)184 -0.79232 1.96539 -0.403 0.6868
## ns(time, df = 18 * 15)185 2.24813 1.60793 1.398 0.1621
## ns(time, df = 18 * 15)186 2.29335 1.59350 1.439 0.1501
## ns(time, df = 18 * 15)187 -0.92192 1.83258 -0.503 0.6149
## ns(time, df = 18 * 15)188 3.28238 1.56892 2.092 0.0364 *
## ns(time, df = 18 * 15)189 0.35799 1.66748 0.215 0.8300
## ns(time, df = 18 * 15)190 1.86208 1.64813 1.130 0.2586
## ns(time, df = 18 * 15)191 1.12002 1.74067 0.643 0.5199
## ns(time, df = 18 * 15)192 0.47955 1.95225 0.246 0.8060
## ns(time, df = 18 * 15)193 -0.48153 1.92609 -0.250 0.8026
## ns(time, df = 18 * 15)194 3.08344 1.61146 1.913 0.0557 .
## ns(time, df = 18 * 15)195 -0.03336 1.69993 -0.020 0.9843
## ns(time, df = 18 * 15)196 2.61621 1.58366 1.652 0.0985 .
## ns(time, df = 18 * 15)197 0.54519 1.67107 0.326 0.7442
## ns(time, df = 18 * 15)198 1.70233 1.62088 1.050 0.2936
## ns(time, df = 18 * 15)199 1.57113 1.62220 0.969 0.3328
## ns(time, df = 18 * 15)200 1.68523 1.75699 0.959 0.3375
## ns(time, df = 18 * 15)201 -1.62601 2.26741 -0.717 0.4733
## ns(time, df = 18 * 15)202 1.97666 1.78935 1.105 0.2693
## ns(time, df = 18 * 15)203 1.01946 1.75768 0.580 0.5619
## ns(time, df = 18 * 15)204 0.48739 1.76519 0.276 0.7825
## ns(time, df = 18 * 15)205 1.96264 1.60329 1.224 0.2209
## ns(time, df = 18 * 15)206 1.43321 1.57832 0.908 0.3638
## ns(time, df = 18 * 15)207 1.89429 1.56880 1.207 0.2272
## ns(time, df = 18 * 15)208 1.15342 1.61270 0.715 0.4745
## ns(time, df = 18 * 15)209 2.74938 1.71039 1.607 0.1080
## ns(time, df = 18 * 15)210 -2.97450 2.67134 -1.113 0.2655
## ns(time, df = 18 * 15)211 1.19387 1.81703 0.657 0.5112
## ns(time, df = 18 * 15)212 2.36196 1.55939 1.515 0.1299
## ns(time, df = 18 * 15)213 1.45198 1.55435 0.934 0.3502
## ns(time, df = 18 * 15)214 1.73049 1.53980 1.124 0.2611
## ns(time, df = 18 * 15)215 1.72086 1.51170 1.138 0.2550
## ns(time, df = 18 * 15)216 2.39314 1.49075 1.605 0.1084
## ns(time, df = 18 * 15)217 1.13495 1.54389 0.735 0.4623
## ns(time, df = 18 * 15)218 2.44871 1.52622 1.604 0.1086
## ns(time, df = 18 * 15)219 0.42103 1.59271 0.264 0.7915
## ns(time, df = 18 * 15)220 3.44173 1.51478 2.272 0.0231 *
## ns(time, df = 18 * 15)221 -0.79409 1.70634 -0.465 0.6417
## ns(time, df = 18 * 15)222 3.29328 1.49683 2.200 0.0278 *
## ns(time, df = 18 * 15)223 1.13894 1.52881 0.745 0.4563
## ns(time, df = 18 * 15)224 2.04258 1.50904 1.354 0.1759
## ns(time, df = 18 * 15)225 2.01765 1.51416 1.333 0.1827
## ns(time, df = 18 * 15)226 1.43945 1.58220 0.910 0.3629
## ns(time, df = 18 * 15)227 1.08044 1.61914 0.667 0.5046
## ns(time, df = 18 * 15)228 2.47329 1.61662 1.530 0.1260
## ns(time, df = 18 * 15)229 -0.95930 1.87244 -0.512 0.6084
## ns(time, df = 18 * 15)230 3.03623 1.62990 1.863 0.0625 .
## ns(time, df = 18 * 15)231 -0.20019 1.76243 -0.114 0.9096
## ns(time, df = 18 * 15)232 2.05442 1.62376 1.265 0.2058
## ns(time, df = 18 * 15)233 1.01812 1.59674 0.638 0.5237
## ns(time, df = 18 * 15)234 2.36172 1.52140 1.552 0.1206
## ns(time, df = 18 * 15)235 1.61358 1.56764 1.029 0.3033
## ns(time, df = 18 * 15)236 0.71373 1.63800 0.436 0.6630
## ns(time, df = 18 * 15)237 2.19842 1.52961 1.437 0.1506
## ns(time, df = 18 * 15)238 1.82258 1.51994 1.199 0.2305
## ns(time, df = 18 * 15)239 1.96497 1.56445 1.256 0.2091
## ns(time, df = 18 * 15)240 0.79867 1.73191 0.461 0.6447
## ns(time, df = 18 * 15)241 0.33760 1.80611 0.187 0.8517
## ns(time, df = 18 * 15)242 1.74847 1.61701 1.081 0.2796
## ns(time, df = 18 * 15)243 1.62059 1.53927 1.053 0.2924
## ns(time, df = 18 * 15)244 2.50060 1.51203 1.654 0.0982 .
## ns(time, df = 18 * 15)245 0.63674 1.60705 0.396 0.6919
## ns(time, df = 18 * 15)246 2.64535 1.58481 1.669 0.0951 .
## ns(time, df = 18 * 15)247 -0.31797 1.79715 -0.177 0.8596
## ns(time, df = 18 * 15)248 1.82904 1.63332 1.120 0.2628
## ns(time, df = 18 * 15)249 1.65054 1.58544 1.041 0.2978
## ns(time, df = 18 * 15)250 1.54524 1.60853 0.961 0.3367
## ns(time, df = 18 * 15)251 1.47288 1.69275 0.870 0.3842
## ns(time, df = 18 * 15)252 0.13050 1.89128 0.069 0.9450
## ns(time, df = 18 * 15)253 1.13429 1.83136 0.619 0.5357
## ns(time, df = 18 * 15)254 0.41102 1.74723 0.235 0.8140
## ns(time, df = 18 * 15)255 2.92189 1.61513 1.809 0.0704 .
## ns(time, df = 18 * 15)256 0.34036 1.91255 0.178 0.8588
## ns(time, df = 18 * 15)257 -2.21664 2.36531 -0.937 0.3487
## ns(time, df = 18 * 15)258 3.55680 1.68858 2.106 0.0352 *
## ns(time, df = 18 * 15)259 -0.35537 1.77150 -0.201 0.8410
## ns(time, df = 18 * 15)260 2.57515 1.65780 1.553 0.1203
## ns(time, df = 18 * 15)261 -0.44824 1.79554 -0.250 0.8029
## ns(time, df = 18 * 15)262 2.41928 1.55611 1.555 0.1200
## ns(time, df = 18 * 15)263 2.01726 1.54246 1.308 0.1909
## ns(time, df = 18 * 15)264 1.02382 1.66884 0.613 0.5396
## ns(time, df = 18 * 15)265 0.39838 1.68447 0.237 0.8130
## ns(time, df = 18 * 15)266 2.85906 1.51243 1.890 0.0587 .
## ns(time, df = 18 * 15)267 1.24045 1.54765 0.802 0.4228
## ns(time, df = 18 * 15)268 1.27426 1.18394 1.076 0.2818
## ns(time, df = 18 * 15)269 3.03017 3.12832 0.969 0.3327
## ns(time, df = 18 * 15)270 -0.67333 1.12614 -0.598 0.5499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21819.87) family taken to be 1)
##
## Null deviance: 1149.74 on 938 degrees of freedom
## Residual deviance: 819.81 on 668 degrees of freedom
## AIC: 3145.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21820
## Std. Err.: 89240
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2601.71## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 16), data = week,
## init.theta = 19516.88827, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5333 -0.7909 -0.1167 0.5088 2.0898
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.32761 1.38795 -0.957 0.33881
## ns(time, df = 18 * 16)1 -2.38737 2.08750 -1.144 0.25277
## ns(time, df = 18 * 16)2 4.54881 2.18132 2.085 0.03704 *
## ns(time, df = 18 * 16)3 -2.72724 2.75264 -0.991 0.32180
## ns(time, df = 18 * 16)4 -0.42149 2.69682 -0.156 0.87580
## ns(time, df = 18 * 16)5 1.65110 1.89190 0.873 0.38282
## ns(time, df = 18 * 16)6 2.40678 1.78118 1.351 0.17662
## ns(time, df = 18 * 16)7 0.44532 1.90693 0.234 0.81535
## ns(time, df = 18 * 16)8 1.58457 1.84156 0.860 0.38954
## ns(time, df = 18 * 16)9 1.22729 1.75476 0.699 0.48430
## ns(time, df = 18 * 16)10 2.77156 1.66965 1.660 0.09692 .
## ns(time, df = 18 * 16)11 0.97149 1.76722 0.550 0.58250
## ns(time, df = 18 * 16)12 1.56179 1.73818 0.899 0.36891
## ns(time, df = 18 * 16)13 2.24166 1.67240 1.340 0.18012
## ns(time, df = 18 * 16)14 1.57857 1.68602 0.936 0.34913
## ns(time, df = 18 * 16)15 2.21326 1.67158 1.324 0.18548
## ns(time, df = 18 * 16)16 1.32824 1.70307 0.780 0.43545
## ns(time, df = 18 * 16)17 2.24660 1.63954 1.370 0.17061
## ns(time, df = 18 * 16)18 2.99699 1.67970 1.784 0.07438 .
## ns(time, df = 18 * 16)19 -1.81335 2.09832 -0.864 0.38748
## ns(time, df = 18 * 16)20 4.17905 1.69928 2.459 0.01392 *
## ns(time, df = 18 * 16)21 0.18531 1.84808 0.100 0.92013
## ns(time, df = 18 * 16)22 1.55659 1.86099 0.836 0.40291
## ns(time, df = 18 * 16)23 1.95597 1.93332 1.012 0.31167
## ns(time, df = 18 * 16)24 -0.89719 2.51568 -0.357 0.72136
## ns(time, df = 18 * 16)25 -0.01282 2.24529 -0.006 0.99545
## ns(time, df = 18 * 16)26 2.12520 1.78157 1.193 0.23292
## ns(time, df = 18 * 16)27 2.04316 1.68843 1.210 0.22624
## ns(time, df = 18 * 16)28 1.89014 1.70790 1.107 0.26842
## ns(time, df = 18 * 16)29 1.16153 1.75831 0.661 0.50887
## ns(time, df = 18 * 16)30 1.94558 1.67690 1.160 0.24596
## ns(time, df = 18 * 16)31 2.46652 1.63972 1.504 0.13252
## ns(time, df = 18 * 16)32 1.54671 1.71058 0.904 0.36589
## ns(time, df = 18 * 16)33 1.20638 1.73410 0.696 0.48663
## ns(time, df = 18 * 16)34 2.55280 1.64577 1.551 0.12087
## ns(time, df = 18 * 16)35 1.75576 1.67304 1.049 0.29397
## ns(time, df = 18 * 16)36 1.93155 1.72372 1.121 0.26247
## ns(time, df = 18 * 16)37 0.82681 1.81111 0.457 0.64801
## ns(time, df = 18 * 16)38 2.01269 1.70648 1.179 0.23822
## ns(time, df = 18 * 16)39 2.50359 1.71175 1.463 0.14358
## ns(time, df = 18 * 16)40 -0.36005 1.93130 -0.186 0.85211
## ns(time, df = 18 * 16)41 3.12520 1.73608 1.800 0.07184 .
## ns(time, df = 18 * 16)42 0.12827 1.85692 0.069 0.94493
## ns(time, df = 18 * 16)43 2.47392 1.72455 1.435 0.15142
## ns(time, df = 18 * 16)44 1.20983 1.73989 0.695 0.48684
## ns(time, df = 18 * 16)45 2.29201 1.71406 1.337 0.18116
## ns(time, df = 18 * 16)46 1.19263 1.82228 0.654 0.51281
## ns(time, df = 18 * 16)47 0.44098 1.83251 0.241 0.80983
## ns(time, df = 18 * 16)48 3.28033 1.63970 2.001 0.04544 *
## ns(time, df = 18 * 16)49 1.16479 1.69952 0.685 0.49311
## ns(time, df = 18 * 16)50 1.92423 1.68178 1.144 0.25256
## ns(time, df = 18 * 16)51 1.99902 1.64573 1.215 0.22449
## ns(time, df = 18 * 16)52 2.54599 1.65146 1.542 0.12316
## ns(time, df = 18 * 16)53 0.83361 1.79593 0.464 0.64253
## ns(time, df = 18 * 16)54 1.55288 1.77532 0.875 0.38173
## ns(time, df = 18 * 16)55 1.85927 1.72535 1.078 0.28121
## ns(time, df = 18 * 16)56 1.45957 1.70694 0.855 0.39251
## ns(time, df = 18 * 16)57 2.59214 1.66356 1.558 0.11919
## ns(time, df = 18 * 16)58 0.87415 1.74251 0.502 0.61590
## ns(time, df = 18 * 16)59 2.39722 1.66596 1.439 0.15017
## ns(time, df = 18 * 16)60 1.77602 1.67255 1.062 0.28830
## ns(time, df = 18 * 16)61 2.62206 1.74679 1.501 0.13334
## ns(time, df = 18 * 16)62 -1.10823 2.23654 -0.496 0.62024
## ns(time, df = 18 * 16)63 1.86569 1.96435 0.950 0.34223
## ns(time, df = 18 * 16)64 1.22957 1.98872 0.618 0.53640
## ns(time, df = 18 * 16)65 -0.23957 2.13262 -0.112 0.91056
## ns(time, df = 18 * 16)66 1.97921 1.78539 1.109 0.26762
## ns(time, df = 18 * 16)67 1.99996 1.68594 1.186 0.23552
## ns(time, df = 18 * 16)68 1.82770 1.66530 1.098 0.27241
## ns(time, df = 18 * 16)69 2.28580 1.65084 1.385 0.16617
## ns(time, df = 18 * 16)70 1.43637 1.69086 0.849 0.39561
## ns(time, df = 18 * 16)71 2.24089 1.66614 1.345 0.17864
## ns(time, df = 18 * 16)72 1.45036 1.68133 0.863 0.38834
## ns(time, df = 18 * 16)73 2.50760 1.63811 1.531 0.12582
## ns(time, df = 18 * 16)74 2.13650 1.71504 1.246 0.21286
## ns(time, df = 18 * 16)75 0.11371 2.02828 0.056 0.95529
## ns(time, df = 18 * 16)76 0.91079 1.98515 0.459 0.64638
## ns(time, df = 18 * 16)77 1.59938 1.82983 0.874 0.38208
## ns(time, df = 18 * 16)78 1.40643 1.75732 0.800 0.42352
## ns(time, df = 18 * 16)79 2.34665 1.69689 1.383 0.16669
## ns(time, df = 18 * 16)80 0.96088 1.74310 0.551 0.58146
## ns(time, df = 18 * 16)81 2.43256 1.65010 1.474 0.14043
## ns(time, df = 18 * 16)82 1.97844 1.64943 1.199 0.23034
## ns(time, df = 18 * 16)83 1.74961 1.66846 1.049 0.29435
## ns(time, df = 18 * 16)84 2.07082 1.65018 1.255 0.20951
## ns(time, df = 18 * 16)85 2.14545 1.66148 1.291 0.19660
## ns(time, df = 18 * 16)86 1.13679 1.70437 0.667 0.50478
## ns(time, df = 18 * 16)87 2.61176 1.61603 1.616 0.10606
## ns(time, df = 18 * 16)88 2.22154 1.62427 1.368 0.17140
## ns(time, df = 18 * 16)89 1.74114 1.69902 1.025 0.30546
## ns(time, df = 18 * 16)90 0.98265 1.74195 0.564 0.57268
## ns(time, df = 18 * 16)91 2.91029 1.65193 1.762 0.07811 .
## ns(time, df = 18 * 16)92 0.61559 1.70785 0.360 0.71851
## ns(time, df = 18 * 16)93 3.48471 1.60450 2.172 0.02987 *
## ns(time, df = 18 * 16)94 0.92554 1.69737 0.545 0.58556
## ns(time, df = 18 * 16)95 2.65521 1.71649 1.547 0.12189
## ns(time, df = 18 * 16)96 0.64732 1.98396 0.326 0.74422
## ns(time, df = 18 * 16)97 -0.89994 2.19459 -0.410 0.68175
## ns(time, df = 18 * 16)98 3.11345 1.71780 1.812 0.06992 .
## ns(time, df = 18 * 16)99 1.68248 1.72225 0.977 0.32862
## ns(time, df = 18 * 16)100 0.96864 1.81269 0.534 0.59309
## ns(time, df = 18 * 16)101 1.83104 1.72878 1.059 0.28953
## ns(time, df = 18 * 16)102 1.66264 1.65402 1.005 0.31480
## ns(time, df = 18 * 16)103 3.19464 1.60013 1.996 0.04588 *
## ns(time, df = 18 * 16)104 0.58577 1.69357 0.346 0.72944
## ns(time, df = 18 * 16)105 3.53608 1.62236 2.180 0.02929 *
## ns(time, df = 18 * 16)106 0.11012 1.76973 0.062 0.95038
## ns(time, df = 18 * 16)107 3.04283 1.66781 1.824 0.06809 .
## ns(time, df = 18 * 16)108 0.65536 1.73997 0.377 0.70643
## ns(time, df = 18 * 16)109 2.80227 1.65584 1.692 0.09058 .
## ns(time, df = 18 * 16)110 1.18094 1.70948 0.691 0.48968
## ns(time, df = 18 * 16)111 2.07534 1.67021 1.243 0.21403
## ns(time, df = 18 * 16)112 1.88398 1.63918 1.149 0.25041
## ns(time, df = 18 * 16)113 2.88131 1.63780 1.759 0.07853 .
## ns(time, df = 18 * 16)114 0.24725 1.81669 0.136 0.89174
## ns(time, df = 18 * 16)115 2.43497 1.71938 1.416 0.15672
## ns(time, df = 18 * 16)116 0.83384 1.71156 0.487 0.62613
## ns(time, df = 18 * 16)117 3.27622 1.58881 2.062 0.03920 *
## ns(time, df = 18 * 16)118 1.38321 1.60911 0.860 0.39000
## ns(time, df = 18 * 16)119 3.62759 1.58732 2.285 0.02229 *
## ns(time, df = 18 * 16)120 0.24407 1.78978 0.136 0.89153
## ns(time, df = 18 * 16)121 1.81749 1.75886 1.033 0.30145
## ns(time, df = 18 * 16)122 1.68606 1.70660 0.988 0.32317
## ns(time, df = 18 * 16)123 2.50594 1.70634 1.469 0.14194
## ns(time, df = 18 * 16)124 0.24699 1.90636 0.130 0.89692
## ns(time, df = 18 * 16)125 1.61978 1.77924 0.910 0.36263
## ns(time, df = 18 * 16)126 2.76808 1.74996 1.582 0.11370
## ns(time, df = 18 * 16)127 -1.03745 2.06678 -0.502 0.61569
## ns(time, df = 18 * 16)128 3.20593 1.76838 1.813 0.06984 .
## ns(time, df = 18 * 16)129 -0.11900 1.85048 -0.064 0.94873
## ns(time, df = 18 * 16)130 3.19498 1.66381 1.920 0.05482 .
## ns(time, df = 18 * 16)131 0.95127 1.71664 0.554 0.57948
## ns(time, df = 18 * 16)132 2.32162 1.65969 1.399 0.16187
## ns(time, df = 18 * 16)133 1.95605 1.66580 1.174 0.24030
## ns(time, df = 18 * 16)134 1.57007 1.69626 0.926 0.35465
## ns(time, df = 18 * 16)135 2.30897 1.69836 1.360 0.17398
## ns(time, df = 18 * 16)136 0.56424 1.79147 0.315 0.75279
## ns(time, df = 18 * 16)137 2.70023 1.67265 1.614 0.10645
## ns(time, df = 18 * 16)138 1.38057 1.71366 0.806 0.42046
## ns(time, df = 18 * 16)139 1.57085 1.68872 0.930 0.35227
## ns(time, df = 18 * 16)140 2.85265 1.63762 1.742 0.08152 .
## ns(time, df = 18 * 16)141 0.64961 1.72742 0.376 0.70687
## ns(time, df = 18 * 16)142 2.99158 1.63752 1.827 0.06772 .
## ns(time, df = 18 * 16)143 1.32170 1.71100 0.772 0.43983
## ns(time, df = 18 * 16)144 2.04445 1.79643 1.138 0.25509
## ns(time, df = 18 * 16)145 -0.71071 1.96210 -0.362 0.71719
## ns(time, df = 18 * 16)146 3.67975 1.63985 2.244 0.02484 *
## ns(time, df = 18 * 16)147 0.99786 1.66683 0.599 0.54940
## ns(time, df = 18 * 16)148 3.10615 1.63109 1.904 0.05687 .
## ns(time, df = 18 * 16)149 0.37383 1.74215 0.215 0.83009
## ns(time, df = 18 * 16)150 3.09577 1.62600 1.904 0.05692 .
## ns(time, df = 18 * 16)151 1.45350 1.67639 0.867 0.38592
## ns(time, df = 18 * 16)152 1.88884 1.69977 1.111 0.26647
## ns(time, df = 18 * 16)153 1.52553 1.70749 0.893 0.37162
## ns(time, df = 18 * 16)154 1.72079 1.62838 1.057 0.29063
## ns(time, df = 18 * 16)155 3.43342 1.55563 2.207 0.02731 *
## ns(time, df = 18 * 16)156 1.40668 1.61420 0.871 0.38352
## ns(time, df = 18 * 16)157 2.73319 1.60279 1.705 0.08814 .
## ns(time, df = 18 * 16)158 1.30909 1.63607 0.800 0.42363
## ns(time, df = 18 * 16)159 3.16357 1.58145 2.000 0.04546 *
## ns(time, df = 18 * 16)160 1.99418 1.67203 1.193 0.23300
## ns(time, df = 18 * 16)161 0.19715 1.93400 0.102 0.91880
## ns(time, df = 18 * 16)162 1.88050 1.84544 1.019 0.30821
## ns(time, df = 18 * 16)163 1.12256 1.88663 0.595 0.55184
## ns(time, df = 18 * 16)164 1.08904 1.92084 0.567 0.57074
## ns(time, df = 18 * 16)165 1.49477 1.92135 0.778 0.43658
## ns(time, df = 18 * 16)166 1.63591 2.18440 0.749 0.45391
## ns(time, df = 18 * 16)167 -4.30816 3.35269 -1.285 0.19880
## ns(time, df = 18 * 16)168 4.45898 2.00837 2.220 0.02641 *
## ns(time, df = 18 * 16)169 -2.06868 2.11032 -0.980 0.32695
## ns(time, df = 18 * 16)170 5.10282 1.73157 2.947 0.00321 **
## ns(time, df = 18 * 16)171 -1.26069 2.13790 -0.590 0.55540
## ns(time, df = 18 * 16)172 -0.21932 2.12545 -0.103 0.91781
## ns(time, df = 18 * 16)173 3.07917 1.68782 1.824 0.06810 .
## ns(time, df = 18 * 16)174 2.02425 1.69163 1.197 0.23145
## ns(time, df = 18 * 16)175 0.82055 1.84976 0.444 0.65733
## ns(time, df = 18 * 16)176 1.33261 1.81239 0.735 0.46217
## ns(time, df = 18 * 16)177 1.90881 1.72244 1.108 0.26777
## ns(time, df = 18 * 16)178 1.69619 1.69128 1.003 0.31591
## ns(time, df = 18 * 16)179 2.25716 1.66627 1.355 0.17554
## ns(time, df = 18 * 16)180 1.20822 1.68731 0.716 0.47395
## ns(time, df = 18 * 16)181 3.13369 1.63686 1.914 0.05556 .
## ns(time, df = 18 * 16)182 0.33184 1.77536 0.187 0.85173
## ns(time, df = 18 * 16)183 2.80175 1.68865 1.659 0.09708 .
## ns(time, df = 18 * 16)184 0.55853 1.73936 0.321 0.74813
## ns(time, df = 18 * 16)185 3.95891 1.69413 2.337 0.01945 *
## ns(time, df = 18 * 16)186 -1.99737 2.19864 -0.908 0.36364
## ns(time, df = 18 * 16)187 2.97668 1.81097 1.644 0.10024
## ns(time, df = 18 * 16)188 0.60028 1.84504 0.325 0.74492
## ns(time, df = 18 * 16)189 2.32951 1.81491 1.284 0.19930
## ns(time, df = 18 * 16)190 -0.13650 1.99004 -0.069 0.94531
## ns(time, df = 18 * 16)191 2.13161 1.76519 1.208 0.22721
## ns(time, df = 18 * 16)192 1.76818 1.71485 1.031 0.30250
## ns(time, df = 18 * 16)193 2.40674 1.77923 1.353 0.17616
## ns(time, df = 18 * 16)194 -1.25761 2.16821 -0.580 0.56190
## ns(time, df = 18 * 16)195 3.57778 1.89334 1.890 0.05880 .
## ns(time, df = 18 * 16)196 -1.71508 2.35311 -0.729 0.46609
## ns(time, df = 18 * 16)197 1.69551 1.81961 0.932 0.35144
## ns(time, df = 18 * 16)198 3.11612 1.68264 1.852 0.06404 .
## ns(time, df = 18 * 16)199 0.11116 1.87254 0.059 0.95266
## ns(time, df = 18 * 16)200 1.85240 1.74456 1.062 0.28832
## ns(time, df = 18 * 16)201 2.36004 1.69005 1.396 0.16258
## ns(time, df = 18 * 16)202 0.98547 1.77810 0.554 0.57943
## ns(time, df = 18 * 16)203 2.13544 1.76714 1.208 0.22689
## ns(time, df = 18 * 16)204 0.99112 1.90457 0.520 0.60279
## ns(time, df = 18 * 16)205 0.91737 2.07058 0.443 0.65773
## ns(time, df = 18 * 16)206 -0.37577 2.04970 -0.183 0.85454
## ns(time, df = 18 * 16)207 3.45814 1.72453 2.005 0.04493 *
## ns(time, df = 18 * 16)208 0.10680 1.82582 0.058 0.95336
## ns(time, df = 18 * 16)209 2.79139 1.70096 1.641 0.10078
## ns(time, df = 18 * 16)210 1.02895 1.77247 0.581 0.56156
## ns(time, df = 18 * 16)211 1.59772 1.75428 0.911 0.36242
## ns(time, df = 18 * 16)212 2.05395 1.72016 1.194 0.23246
## ns(time, df = 18 * 16)213 1.60266 1.80343 0.889 0.37418
## ns(time, df = 18 * 16)214 1.15392 2.06259 0.559 0.57585
## ns(time, df = 18 * 16)215 -1.90284 2.45167 -0.776 0.43767
## ns(time, df = 18 * 16)216 3.85281 1.85750 2.074 0.03806 *
## ns(time, df = 18 * 16)217 -0.85063 2.07423 -0.410 0.68174
## ns(time, df = 18 * 16)218 2.06320 1.80090 1.146 0.25194
## ns(time, df = 18 * 16)219 1.72863 1.71588 1.007 0.31373
## ns(time, df = 18 * 16)220 1.92666 1.68661 1.142 0.25332
## ns(time, df = 18 * 16)221 1.95349 1.69163 1.155 0.24817
## ns(time, df = 18 * 16)222 1.49537 1.72750 0.866 0.38670
## ns(time, df = 18 * 16)223 2.97648 1.83465 1.622 0.10472
## ns(time, df = 18 * 16)224 -2.86660 2.88969 -0.992 0.32119
## ns(time, df = 18 * 16)225 1.29208 2.00097 0.646 0.51845
## ns(time, df = 18 * 16)226 2.26670 1.69814 1.335 0.18194
## ns(time, df = 18 * 16)227 2.23378 1.65315 1.351 0.17662
## ns(time, df = 18 * 16)228 1.48029 1.68210 0.880 0.37884
## ns(time, df = 18 * 16)229 2.18102 1.63318 1.335 0.18173
## ns(time, df = 18 * 16)230 2.29902 1.61254 1.426 0.15395
## ns(time, df = 18 * 16)231 2.01185 1.63074 1.234 0.21731
## ns(time, df = 18 * 16)232 2.02426 1.65009 1.227 0.21991
## ns(time, df = 18 * 16)233 1.74689 1.67299 1.044 0.29641
## ns(time, df = 18 * 16)234 1.88337 1.65539 1.138 0.25524
## ns(time, df = 18 * 16)235 2.60354 1.65315 1.575 0.11528
## ns(time, df = 18 * 16)236 0.31769 1.76062 0.180 0.85680
## ns(time, df = 18 * 16)237 3.24831 1.60963 2.018 0.04359 *
## ns(time, df = 18 * 16)238 1.52717 1.64122 0.931 0.35211
## ns(time, df = 18 * 16)239 2.18215 1.62891 1.340 0.18036
## ns(time, df = 18 * 16)240 2.29035 1.62793 1.407 0.15945
## ns(time, df = 18 * 16)241 1.81708 1.69124 1.074 0.28264
## ns(time, df = 18 * 16)242 1.02947 1.74839 0.589 0.55599
## ns(time, df = 18 * 16)243 3.03940 1.71541 1.772 0.07642 .
## ns(time, df = 18 * 16)244 -0.75088 1.99702 -0.376 0.70692
## ns(time, df = 18 * 16)245 2.86398 1.75921 1.628 0.10353
## ns(time, df = 18 * 16)246 0.75102 1.82603 0.411 0.68086
## ns(time, df = 18 * 16)247 1.71094 1.78258 0.960 0.33715
## ns(time, df = 18 * 16)248 1.45997 1.74110 0.839 0.40173
## ns(time, df = 18 * 16)249 1.98595 1.66844 1.190 0.23393
## ns(time, df = 18 * 16)250 2.37396 1.64215 1.446 0.14828
## ns(time, df = 18 * 16)251 1.63722 1.70805 0.959 0.33779
## ns(time, df = 18 * 16)252 1.10526 1.74142 0.635 0.52563
## ns(time, df = 18 * 16)253 2.52888 1.63922 1.543 0.12290
## ns(time, df = 18 * 16)254 1.90954 1.63936 1.165 0.24410
## ns(time, df = 18 * 16)255 2.47662 1.67569 1.478 0.13942
## ns(time, df = 18 * 16)256 0.49151 1.87981 0.261 0.79373
## ns(time, df = 18 * 16)257 1.45656 1.87188 0.778 0.43649
## ns(time, df = 18 * 16)258 0.95792 1.80647 0.530 0.59592
## ns(time, df = 18 * 16)259 2.41559 1.65342 1.461 0.14403
## ns(time, df = 18 * 16)260 2.20861 1.63275 1.353 0.17615
## ns(time, df = 18 * 16)261 1.81952 1.67293 1.088 0.27676
## ns(time, df = 18 * 16)262 1.73364 1.70743 1.015 0.30994
## ns(time, df = 18 * 16)263 1.95649 1.75968 1.112 0.26620
## ns(time, df = 18 * 16)264 0.21788 1.89779 0.115 0.90860
## ns(time, df = 18 * 16)265 2.41151 1.71337 1.407 0.15929
## ns(time, df = 18 * 16)266 1.69639 1.70419 0.995 0.31953
## ns(time, df = 18 * 16)267 1.88658 1.73590 1.087 0.27713
## ns(time, df = 18 * 16)268 1.34927 1.84790 0.730 0.46529
## ns(time, df = 18 * 16)269 0.64884 1.99945 0.325 0.74555
## ns(time, df = 18 * 16)270 1.13015 1.96554 0.575 0.56530
## ns(time, df = 18 * 16)271 0.87219 1.85774 0.469 0.63872
## ns(time, df = 18 * 16)272 2.79877 1.73028 1.618 0.10576
## ns(time, df = 18 * 16)273 1.54436 1.97642 0.781 0.43457
## ns(time, df = 18 * 16)274 -3.89672 3.09122 -1.261 0.20746
## ns(time, df = 18 * 16)275 4.12647 1.87545 2.200 0.02779 *
## ns(time, df = 18 * 16)276 -0.12778 1.89668 -0.067 0.94629
## ns(time, df = 18 * 16)277 2.75458 1.76825 1.558 0.11928
## ns(time, df = 18 * 16)278 0.39169 1.90139 0.206 0.83679
## ns(time, df = 18 * 16)279 1.28023 1.77092 0.723 0.46973
## ns(time, df = 18 * 16)280 2.89530 1.63850 1.767 0.07722 .
## ns(time, df = 18 * 16)281 1.57545 1.70394 0.925 0.35518
## ns(time, df = 18 * 16)282 1.34067 1.80066 0.745 0.45655
## ns(time, df = 18 * 16)283 0.85415 1.76230 0.485 0.62790
## ns(time, df = 18 * 16)284 3.24951 1.61900 2.007 0.04474 *
## ns(time, df = 18 * 16)285 1.20067 1.68045 0.714 0.47492
## ns(time, df = 18 * 16)286 1.48074 1.25136 1.183 0.23669
## ns(time, df = 18 * 16)287 3.75931 3.39476 1.107 0.26813
## ns(time, df = 18 * 16)288 -0.86431 1.17320 -0.737 0.46130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(19516.89) family taken to be 1)
##
## Null deviance: 1149.7 on 938 degrees of freedom
## Residual deviance: 801.4 on 650 degrees of freedom
## AIC: 3163.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 19517
## Std. Err.: 71769
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2583.313## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 18), data = week,
## init.theta = 23118.62024, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2971 -0.7967 -0.1101 0.4761 2.2569
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.45136 1.51753 -0.956 0.33887
## ns(time, df = 18 * 18)1 -1.21722 2.14263 -0.568 0.56997
## ns(time, df = 18 * 18)2 1.91737 2.36648 0.810 0.41781
## ns(time, df = 18 * 18)3 3.03722 2.36556 1.284 0.19916
## ns(time, df = 18 * 18)4 -7.40177 6.92602 -1.069 0.28521
## ns(time, df = 18 * 18)5 1.47389 2.59019 0.569 0.56934
## ns(time, df = 18 * 18)6 1.95860 2.02400 0.968 0.33320
## ns(time, df = 18 * 18)7 2.08832 1.91269 1.092 0.27491
## ns(time, df = 18 * 18)8 1.22011 2.03812 0.599 0.54941
## ns(time, df = 18 * 18)9 0.66407 2.05893 0.323 0.74705
## ns(time, df = 18 * 18)10 2.54355 1.88080 1.352 0.17625
## ns(time, df = 18 * 18)11 1.02132 1.87052 0.546 0.58506
## ns(time, df = 18 * 18)12 3.74741 1.81833 2.061 0.03931 *
## ns(time, df = 18 * 18)13 -1.30244 2.16996 -0.600 0.54836
## ns(time, df = 18 * 18)14 3.63738 1.82410 1.994 0.04614 *
## ns(time, df = 18 * 18)15 0.97968 1.86137 0.526 0.59866
## ns(time, df = 18 * 18)16 2.60814 1.80267 1.447 0.14795
## ns(time, df = 18 * 18)17 1.64235 1.82878 0.898 0.36915
## ns(time, df = 18 * 18)18 2.21072 1.82483 1.211 0.22572
## ns(time, df = 18 * 18)19 1.26885 1.81957 0.697 0.48559
## ns(time, df = 18 * 18)20 4.04622 1.77556 2.279 0.02268 *
## ns(time, df = 18 * 18)21 -0.76125 2.17857 -0.349 0.72677
## ns(time, df = 18 * 18)22 1.22002 1.97210 0.619 0.53616
## ns(time, df = 18 * 18)23 3.70499 1.82434 2.031 0.04227 *
## ns(time, df = 18 * 18)24 -0.31501 2.10885 -0.149 0.88126
## ns(time, df = 18 * 18)25 2.12055 1.99327 1.064 0.28739
## ns(time, df = 18 * 18)26 1.89554 2.08616 0.909 0.36355
## ns(time, df = 18 * 18)27 -0.78715 2.70445 -0.291 0.77101
## ns(time, df = 18 * 18)28 0.50870 2.48678 0.205 0.83791
## ns(time, df = 18 * 18)29 1.01248 2.05923 0.492 0.62295
## ns(time, df = 18 * 18)30 2.79687 1.82475 1.533 0.12534
## ns(time, df = 18 * 18)31 1.68088 1.83709 0.915 0.36021
## ns(time, df = 18 * 18)32 2.14743 1.85831 1.156 0.24785
## ns(time, df = 18 * 18)33 1.04614 1.91593 0.546 0.58505
## ns(time, df = 18 * 18)34 2.35145 1.80048 1.306 0.19155
## ns(time, df = 18 * 18)35 2.47083 1.77531 1.392 0.16399
## ns(time, df = 18 * 18)36 1.73464 1.84382 0.941 0.34681
## ns(time, df = 18 * 18)37 1.50978 1.88171 0.802 0.42235
## ns(time, df = 18 * 18)38 1.96950 1.81251 1.087 0.27721
## ns(time, df = 18 * 18)39 2.73823 1.77379 1.544 0.12266
## ns(time, df = 18 * 18)40 1.36559 1.85733 0.735 0.46219
## ns(time, df = 18 * 18)41 2.27336 1.87853 1.210 0.22621
## ns(time, df = 18 * 18)42 0.67871 1.97634 0.343 0.73128
## ns(time, df = 18 * 18)43 2.42980 1.83324 1.325 0.18503
## ns(time, df = 18 * 18)44 2.47404 1.85460 1.334 0.18220
## ns(time, df = 18 * 18)45 -0.10792 2.09510 -0.052 0.95892
## ns(time, df = 18 * 18)46 2.77068 1.88822 1.467 0.14228
## ns(time, df = 18 * 18)47 1.23226 1.94437 0.634 0.52624
## ns(time, df = 18 * 18)48 1.32317 1.93595 0.683 0.49431
## ns(time, df = 18 * 18)49 2.46875 1.84756 1.336 0.18148
## ns(time, df = 18 * 18)50 1.22078 1.88956 0.646 0.51824
## ns(time, df = 18 * 18)51 2.60521 1.85685 1.403 0.16061
## ns(time, df = 18 * 18)52 0.98562 2.01181 0.490 0.62419
## ns(time, df = 18 * 18)53 0.63156 1.99571 0.316 0.75165
## ns(time, df = 18 * 18)54 3.21917 1.77818 1.810 0.07024 .
## ns(time, df = 18 * 18)55 1.73388 1.81325 0.956 0.33896
## ns(time, df = 18 * 18)56 1.69106 1.84482 0.917 0.35933
## ns(time, df = 18 * 18)57 2.11366 1.79449 1.178 0.23885
## ns(time, df = 18 * 18)58 2.46320 1.76864 1.393 0.16371
## ns(time, df = 18 * 18)59 2.13677 1.82668 1.170 0.24210
## ns(time, df = 18 * 18)60 1.00102 1.96386 0.510 0.61025
## ns(time, df = 18 * 18)61 1.78213 1.91291 0.932 0.35153
## ns(time, df = 18 * 18)62 1.90848 1.87069 1.020 0.30763
## ns(time, df = 18 * 18)63 1.61497 1.84837 0.874 0.38227
## ns(time, df = 18 * 18)64 2.64295 1.80022 1.468 0.14207
## ns(time, df = 18 * 18)65 1.22936 1.86567 0.659 0.50994
## ns(time, df = 18 * 18)66 2.39293 1.81918 1.315 0.18838
## ns(time, df = 18 * 18)67 1.50468 1.82369 0.825 0.40933
## ns(time, df = 18 * 18)68 3.05355 1.79123 1.705 0.08825 .
## ns(time, df = 18 * 18)69 1.20147 1.99993 0.601 0.54800
## ns(time, df = 18 * 18)70 -0.21130 2.35212 -0.090 0.92842
## ns(time, df = 18 * 18)71 2.06283 2.10028 0.982 0.32602
## ns(time, df = 18 * 18)72 0.82663 2.16040 0.383 0.70199
## ns(time, df = 18 * 18)73 1.18067 2.18549 0.540 0.58904
## ns(time, df = 18 * 18)74 0.42361 2.10407 0.201 0.84044
## ns(time, df = 18 * 18)75 2.74893 1.83247 1.500 0.13358
## ns(time, df = 18 * 18)76 1.85166 1.81547 1.020 0.30776
## ns(time, df = 18 * 18)77 1.93475 1.80376 1.073 0.28344
## ns(time, df = 18 * 18)78 2.56175 1.78349 1.436 0.15090
## ns(time, df = 18 * 18)79 1.37502 1.84658 0.745 0.45649
## ns(time, df = 18 * 18)80 2.32367 1.80675 1.286 0.19841
## ns(time, df = 18 * 18)81 1.89016 1.81043 1.044 0.29647
## ns(time, df = 18 * 18)82 2.01776 1.79058 1.127 0.25979
## ns(time, df = 18 * 18)83 2.89576 1.79432 1.614 0.10656
## ns(time, df = 18 * 18)84 0.92792 2.04569 0.454 0.65012
## ns(time, df = 18 * 18)85 -0.07619 2.32857 -0.033 0.97390
## ns(time, df = 18 * 18)86 2.03260 2.04181 0.995 0.31950
## ns(time, df = 18 * 18)87 1.00256 1.99409 0.503 0.61513
## ns(time, df = 18 * 18)88 2.20480 1.86847 1.180 0.23800
## ns(time, df = 18 * 18)89 1.82879 1.85110 0.988 0.32318
## ns(time, df = 18 * 18)90 1.94941 1.85444 1.051 0.29316
## ns(time, df = 18 * 18)91 1.40244 1.83959 0.762 0.44584
## ns(time, df = 18 * 18)92 3.11229 1.75905 1.769 0.07684 .
## ns(time, df = 18 * 18)93 1.24091 1.83466 0.676 0.49881
## ns(time, df = 18 * 18)94 2.30542 1.79297 1.286 0.19851
## ns(time, df = 18 * 18)95 2.24744 1.78319 1.260 0.20754
## ns(time, df = 18 * 18)96 1.94325 1.82221 1.066 0.28623
## ns(time, df = 18 * 18)97 1.47570 1.83476 0.804 0.42122
## ns(time, df = 18 * 18)98 2.69870 1.75037 1.542 0.12312
## ns(time, df = 18 * 18)99 2.29535 1.75535 1.308 0.19100
## ns(time, df = 18 * 18)100 2.23802 1.81470 1.233 0.21747
## ns(time, df = 18 * 18)101 0.78634 1.92470 0.409 0.68287
## ns(time, df = 18 * 18)102 2.70435 1.80503 1.498 0.13407
## ns(time, df = 18 * 18)103 1.71776 1.82193 0.943 0.34577
## ns(time, df = 18 * 18)104 1.80134 1.79072 1.006 0.31445
## ns(time, df = 18 * 18)105 3.21054 1.74093 1.844 0.06516 .
## ns(time, df = 18 * 18)106 1.00614 1.84506 0.545 0.58554
## ns(time, df = 18 * 18)107 3.07401 1.84655 1.665 0.09597 .
## ns(time, df = 18 * 18)108 0.12283 2.15614 0.057 0.95457
## ns(time, df = 18 * 18)109 1.05803 2.22692 0.475 0.63471
## ns(time, df = 18 * 18)110 0.44645 2.01132 0.222 0.82434
## ns(time, df = 18 * 18)111 4.16980 1.80536 2.310 0.02091 *
## ns(time, df = 18 * 18)112 -0.46675 2.05835 -0.227 0.82061
## ns(time, df = 18 * 18)113 2.52259 1.89683 1.330 0.18355
## ns(time, df = 18 * 18)114 1.33370 1.88149 0.709 0.47842
## ns(time, df = 18 * 18)115 2.05428 1.77942 1.154 0.24831
## ns(time, df = 18 * 18)116 3.37509 1.73319 1.947 0.05150 .
## ns(time, df = 18 * 18)117 0.43853 1.85715 0.236 0.81333
## ns(time, df = 18 * 18)118 3.71419 1.75285 2.119 0.03410 *
## ns(time, df = 18 * 18)119 0.71477 1.87785 0.381 0.70348
## ns(time, df = 18 * 18)120 2.38930 1.82979 1.306 0.19163
## ns(time, df = 18 * 18)121 1.80606 1.83813 0.983 0.32583
## ns(time, df = 18 * 18)122 1.76437 1.83427 0.962 0.33610
## ns(time, df = 18 * 18)123 2.42527 1.80238 1.346 0.17843
## ns(time, df = 18 * 18)124 1.49721 1.84380 0.812 0.41678
## ns(time, df = 18 * 18)125 2.24105 1.80705 1.240 0.21491
## ns(time, df = 18 * 18)126 1.81358 1.78686 1.015 0.31013
## ns(time, df = 18 * 18)127 3.02485 1.75690 1.722 0.08512 .
## ns(time, df = 18 * 18)128 1.34876 1.89210 0.713 0.47595
## ns(time, df = 18 * 18)129 0.88818 1.96967 0.451 0.65204
## ns(time, df = 18 * 18)130 2.67445 1.83265 1.459 0.14447
## ns(time, df = 18 * 18)131 0.99924 1.82754 0.547 0.58454
## ns(time, df = 18 * 18)132 3.61765 1.71116 2.114 0.03450 *
## ns(time, df = 18 * 18)133 1.27288 1.75068 0.727 0.46718
## ns(time, df = 18 * 18)134 3.90985 1.71718 2.277 0.02279 *
## ns(time, df = 18 * 18)135 0.43857 1.92411 0.228 0.81970
## ns(time, df = 18 * 18)136 1.72404 1.93171 0.892 0.37213
## ns(time, df = 18 * 18)137 1.78577 1.86284 0.959 0.33775
## ns(time, df = 18 * 18)138 2.34282 1.82453 1.284 0.19912
## ns(time, df = 18 * 18)139 1.93180 1.91452 1.009 0.31296
## ns(time, df = 18 * 18)140 0.02039 2.12171 0.010 0.99233
## ns(time, df = 18 * 18)141 2.60917 1.87678 1.390 0.16446
## ns(time, df = 18 * 18)142 2.18640 1.92212 1.137 0.25533
## ns(time, df = 18 * 18)143 -0.64670 2.24044 -0.289 0.77285
## ns(time, df = 18 * 18)144 3.03212 1.92075 1.579 0.11443
## ns(time, df = 18 * 18)145 0.65263 1.98375 0.329 0.74217
## ns(time, df = 18 * 18)146 2.01626 1.84971 1.090 0.27570
## ns(time, df = 18 * 18)147 2.76036 1.79798 1.535 0.12472
## ns(time, df = 18 * 18)148 0.67790 1.88958 0.359 0.71978
## ns(time, df = 18 * 18)149 3.22636 1.77413 1.819 0.06898 .
## ns(time, df = 18 * 18)150 1.25633 1.84613 0.681 0.49617
## ns(time, df = 18 * 18)151 2.12191 1.82381 1.163 0.24465
## ns(time, df = 18 * 18)152 2.42581 1.84365 1.316 0.18825
## ns(time, df = 18 * 18)153 0.17301 1.99165 0.087 0.93078
## ns(time, df = 18 * 18)154 3.29255 1.80790 1.821 0.06858 .
## ns(time, df = 18 * 18)155 0.95498 1.87784 0.509 0.61107
## ns(time, df = 18 * 18)156 2.45843 1.81776 1.352 0.17623
## ns(time, df = 18 * 18)157 1.57992 1.81264 0.872 0.38342
## ns(time, df = 18 * 18)158 2.97365 1.77851 1.672 0.09453 .
## ns(time, df = 18 * 18)159 0.63294 1.87929 0.337 0.73627
## ns(time, df = 18 * 18)160 3.26347 1.76819 1.846 0.06494 .
## ns(time, df = 18 * 18)161 1.44156 1.85076 0.779 0.43604
## ns(time, df = 18 * 18)162 1.90279 1.94331 0.979 0.32751
## ns(time, df = 18 * 18)163 0.42256 2.07405 0.204 0.83856
## ns(time, df = 18 * 18)164 1.99113 1.84138 1.081 0.27955
## ns(time, df = 18 * 18)165 3.04077 1.74954 1.738 0.08220 .
## ns(time, df = 18 * 18)166 1.31536 1.80607 0.728 0.46643
## ns(time, df = 18 * 18)167 3.05729 1.78037 1.717 0.08594 .
## ns(time, df = 18 * 18)168 0.44709 1.89162 0.236 0.81316
## ns(time, df = 18 * 18)169 3.38480 1.75616 1.927 0.05393 .
## ns(time, df = 18 * 18)170 1.50621 1.81976 0.828 0.40784
## ns(time, df = 18 * 18)171 1.88130 1.84833 1.018 0.30876
## ns(time, df = 18 * 18)172 1.94427 1.84427 1.054 0.29178
## ns(time, df = 18 * 18)173 1.43974 1.81528 0.793 0.42771
## ns(time, df = 18 * 18)174 3.06266 1.70267 1.799 0.07206 .
## ns(time, df = 18 * 18)175 2.73294 1.70244 1.605 0.10843
## ns(time, df = 18 * 18)176 1.79149 1.75648 1.020 0.30776
## ns(time, df = 18 * 18)177 2.70623 1.74514 1.551 0.12097
## ns(time, df = 18 * 18)178 1.49183 1.77040 0.843 0.39943
## ns(time, df = 18 * 18)179 3.31666 1.70986 1.940 0.05241 .
## ns(time, df = 18 * 18)180 2.12767 1.79164 1.188 0.23501
## ns(time, df = 18 * 18)181 0.86922 2.02050 0.430 0.66705
## ns(time, df = 18 * 18)182 1.21440 2.04708 0.593 0.55302
## ns(time, df = 18 * 18)183 1.84511 1.99198 0.926 0.35431
## ns(time, df = 18 * 18)184 1.07480 2.06319 0.521 0.60241
## ns(time, df = 18 * 18)185 1.36540 2.06054 0.663 0.50756
## ns(time, df = 18 * 18)186 1.69431 2.08842 0.811 0.41720
## ns(time, df = 18 * 18)187 1.41345 2.48162 0.570 0.56897
## ns(time, df = 18 * 18)188 -4.73173 4.04784 -1.169 0.24242
## ns(time, df = 18 * 18)189 4.35966 2.22034 1.964 0.04959 *
## ns(time, df = 18 * 18)190 -1.02662 2.25966 -0.454 0.64960
## ns(time, df = 18 * 18)191 3.03415 1.86083 1.631 0.10299
## ns(time, df = 18 * 18)192 2.85651 1.92784 1.482 0.13842
## ns(time, df = 18 * 18)193 -2.41216 2.90074 -0.832 0.40565
## ns(time, df = 18 * 18)194 1.63251 2.03836 0.801 0.42319
## ns(time, df = 18 * 18)195 2.90264 1.79508 1.617 0.10588
## ns(time, df = 18 * 18)196 2.13383 1.83243 1.164 0.24423
## ns(time, df = 18 * 18)197 0.99212 2.01017 0.494 0.62162
## ns(time, df = 18 * 18)198 1.08110 1.99636 0.542 0.58814
## ns(time, df = 18 * 18)199 2.42639 1.86301 1.302 0.19278
## ns(time, df = 18 * 18)200 1.21800 1.86667 0.653 0.51408
## ns(time, df = 18 * 18)201 2.89409 1.79144 1.616 0.10620
## ns(time, df = 18 * 18)202 1.09652 1.85701 0.590 0.55487
## ns(time, df = 18 * 18)203 2.55700 1.77991 1.437 0.15083
## ns(time, df = 18 * 18)204 2.42218 1.79862 1.347 0.17808
## ns(time, df = 18 * 18)205 0.75219 1.91611 0.393 0.69464
## ns(time, df = 18 * 18)206 2.97217 1.82806 1.626 0.10398
## ns(time, df = 18 * 18)207 0.40300 1.90478 0.212 0.83244
## ns(time, df = 18 * 18)208 4.06015 1.80516 2.249 0.02450 *
## ns(time, df = 18 * 18)209 -0.43018 2.16443 -0.199 0.84246
## ns(time, df = 18 * 18)210 0.77428 2.11735 0.366 0.71460
## ns(time, df = 18 * 18)211 2.95425 1.91902 1.539 0.12369
## ns(time, df = 18 * 18)212 -0.06342 2.07627 -0.031 0.97563
## ns(time, df = 18 * 18)213 3.33420 1.95745 1.703 0.08850 .
## ns(time, df = 18 * 18)214 -1.32777 2.31331 -0.574 0.56599
## ns(time, df = 18 * 18)215 3.23091 1.89952 1.701 0.08896 .
## ns(time, df = 18 * 18)216 0.76455 1.89855 0.403 0.68717
## ns(time, df = 18 * 18)217 4.15224 1.90399 2.181 0.02920 *
## ns(time, df = 18 * 18)218 -3.73591 2.84588 -1.313 0.18927
## ns(time, df = 18 * 18)219 4.61035 2.05980 2.238 0.02520 *
## ns(time, df = 18 * 18)220 -1.34695 2.42662 -0.555 0.57884
## ns(time, df = 18 * 18)221 1.55957 2.15073 0.725 0.46837
## ns(time, df = 18 * 18)222 1.19046 1.92102 0.620 0.53546
## ns(time, df = 18 * 18)223 3.63467 1.80795 2.010 0.04439 *
## ns(time, df = 18 * 18)224 -0.09231 2.06789 -0.045 0.96439
## ns(time, df = 18 * 18)225 1.67693 1.92647 0.870 0.38405
## ns(time, df = 18 * 18)226 2.66397 1.81701 1.466 0.14261
## ns(time, df = 18 * 18)227 1.47117 1.89592 0.776 0.43777
## ns(time, df = 18 * 18)228 1.34248 1.93305 0.694 0.48738
## ns(time, df = 18 * 18)229 2.61896 1.92489 1.361 0.17365
## ns(time, df = 18 * 18)230 -0.16073 2.23943 -0.072 0.94278
## ns(time, df = 18 * 18)231 1.90661 2.18473 0.873 0.38283
## ns(time, df = 18 * 18)232 -0.67725 2.22962 -0.304 0.76132
## ns(time, df = 18 * 18)233 3.83152 1.85784 2.062 0.03917 *
## ns(time, df = 18 * 18)234 0.20002 1.97658 0.101 0.91940
## ns(time, df = 18 * 18)235 2.62648 1.85145 1.419 0.15601
## ns(time, df = 18 * 18)236 1.60467 1.87830 0.854 0.39293
## ns(time, df = 18 * 18)237 1.63770 1.90928 0.858 0.39103
## ns(time, df = 18 * 18)238 1.58787 1.88409 0.843 0.39935
## ns(time, df = 18 * 18)239 2.53914 1.85363 1.370 0.17074
## ns(time, df = 18 * 18)240 1.05134 2.01551 0.522 0.60193
## ns(time, df = 18 * 18)241 1.82317 2.26314 0.806 0.42048
## ns(time, df = 18 * 18)242 -3.04319 2.98384 -1.020 0.30778
## ns(time, df = 18 * 18)243 4.48056 2.04378 2.192 0.02836 *
## ns(time, df = 18 * 18)244 -0.82071 2.25784 -0.363 0.71624
## ns(time, df = 18 * 18)245 1.95783 2.00349 0.977 0.32847
## ns(time, df = 18 * 18)246 1.66856 1.89136 0.882 0.37767
## ns(time, df = 18 * 18)247 2.12029 1.82696 1.161 0.24582
## ns(time, df = 18 * 18)248 2.08904 1.81854 1.149 0.25066
## ns(time, df = 18 * 18)249 1.81195 1.84706 0.981 0.32660
## ns(time, df = 18 * 18)250 1.89161 1.85742 1.018 0.30849
## ns(time, df = 18 * 18)251 3.00104 1.99303 1.506 0.13213
## ns(time, df = 18 * 18)252 -2.96618 3.24437 -0.914 0.36058
## ns(time, df = 18 * 18)253 1.44031 2.28294 0.631 0.52811
## ns(time, df = 18 * 18)254 1.50548 1.91254 0.787 0.43119
## ns(time, df = 18 * 18)255 3.09949 1.77336 1.748 0.08050 .
## ns(time, df = 18 * 18)256 1.29273 1.83286 0.705 0.48062
## ns(time, df = 18 * 18)257 2.33668 1.79047 1.305 0.19187
## ns(time, df = 18 * 18)258 2.02843 1.77112 1.145 0.25209
## ns(time, df = 18 * 18)259 2.49158 1.74474 1.428 0.15328
## ns(time, df = 18 * 18)260 2.36262 1.76549 1.338 0.18082
## ns(time, df = 18 * 18)261 1.42675 1.81276 0.787 0.43125
## ns(time, df = 18 * 18)262 3.21214 1.78430 1.800 0.07183 .
## ns(time, df = 18 * 18)263 -0.22370 1.92612 -0.116 0.90754
## ns(time, df = 18 * 18)264 4.72840 1.76986 2.672 0.00755 **
## ns(time, df = 18 * 18)265 -1.26068 2.07448 -0.608 0.54338
## ns(time, df = 18 * 18)266 3.41929 1.78433 1.916 0.05533 .
## ns(time, df = 18 * 18)267 1.68452 1.77278 0.950 0.34200
## ns(time, df = 18 * 18)268 2.75079 1.75111 1.571 0.11621
## ns(time, df = 18 * 18)269 1.53692 1.78630 0.860 0.38957
## ns(time, df = 18 * 18)270 2.99002 1.75095 1.708 0.08770 .
## ns(time, df = 18 * 18)271 1.41979 1.83214 0.775 0.43838
## ns(time, df = 18 * 18)272 2.21845 1.85169 1.198 0.23089
## ns(time, df = 18 * 18)273 1.10805 1.89597 0.584 0.55893
## ns(time, df = 18 * 18)274 3.38508 1.89389 1.787 0.07388 .
## ns(time, df = 18 * 18)275 -2.13563 2.41016 -0.886 0.37557
## ns(time, df = 18 * 18)276 4.19044 1.90053 2.205 0.02746 *
## ns(time, df = 18 * 18)277 -0.21193 2.06800 -0.102 0.91837
## ns(time, df = 18 * 18)278 2.27711 1.92361 1.184 0.23650
## ns(time, df = 18 * 18)279 1.46646 1.89462 0.774 0.43892
## ns(time, df = 18 * 18)280 1.97349 1.82698 1.080 0.28006
## ns(time, df = 18 * 18)281 2.38388 1.77557 1.343 0.17940
## ns(time, df = 18 * 18)282 2.35961 1.80335 1.308 0.19072
## ns(time, df = 18 * 18)283 0.98920 1.92088 0.515 0.60657
## ns(time, df = 18 * 18)284 2.02164 1.83019 1.105 0.26933
## ns(time, df = 18 * 18)285 2.48240 1.77130 1.401 0.16108
## ns(time, df = 18 * 18)286 1.85811 1.78005 1.044 0.29655
## ns(time, df = 18 * 18)287 3.24860 1.81198 1.793 0.07300 .
## ns(time, df = 18 * 18)288 -1.13820 2.20076 -0.517 0.60503
## ns(time, df = 18 * 18)289 3.97791 1.98298 2.006 0.04485 *
## ns(time, df = 18 * 18)290 -2.46737 2.43718 -1.012 0.31135
## ns(time, df = 18 * 18)291 4.24476 1.82490 2.326 0.02002 *
## ns(time, df = 18 * 18)292 0.68089 1.83815 0.370 0.71107
## ns(time, df = 18 * 18)293 3.53596 1.75364 2.016 0.04376 *
## ns(time, df = 18 * 18)294 0.85725 1.86816 0.459 0.64632
## ns(time, df = 18 * 18)295 2.44261 1.83738 1.329 0.18372
## ns(time, df = 18 * 18)296 1.85136 1.91713 0.966 0.33420
## ns(time, df = 18 * 18)297 0.18822 2.09794 0.090 0.92851
## ns(time, df = 18 * 18)298 2.46921 1.86910 1.321 0.18648
## ns(time, df = 18 * 18)299 1.88904 1.84285 1.025 0.30533
## ns(time, df = 18 * 18)300 1.83251 1.85601 0.987 0.32348
## ns(time, df = 18 * 18)301 2.29217 1.90061 1.206 0.22781
## ns(time, df = 18 * 18)302 0.26517 2.14757 0.123 0.90173
## ns(time, df = 18 * 18)303 1.80750 2.09348 0.863 0.38792
## ns(time, df = 18 * 18)304 0.62713 2.17642 0.288 0.77323
## ns(time, df = 18 * 18)305 1.04167 2.00585 0.519 0.60354
## ns(time, df = 18 * 18)306 3.04413 1.84282 1.652 0.09856 .
## ns(time, df = 18 * 18)307 1.45273 2.02019 0.719 0.47208
## ns(time, df = 18 * 18)308 -0.32063 2.60030 -0.123 0.90186
## ns(time, df = 18 * 18)309 -0.27046 2.30187 -0.117 0.90647
## ns(time, df = 18 * 18)310 3.84517 1.90476 2.019 0.04352 *
## ns(time, df = 18 * 18)311 -0.52012 2.09123 -0.249 0.80358
## ns(time, df = 18 * 18)312 3.36966 1.90536 1.769 0.07697 .
## ns(time, df = 18 * 18)313 -0.11820 2.11503 -0.056 0.95543
## ns(time, df = 18 * 18)314 1.82219 1.90502 0.957 0.33881
## ns(time, df = 18 * 18)315 2.54760 1.77960 1.432 0.15227
## ns(time, df = 18 * 18)316 2.41664 1.80191 1.341 0.17987
## ns(time, df = 18 * 18)317 1.07229 1.96100 0.547 0.58451
## ns(time, df = 18 * 18)318 1.20949 1.95644 0.618 0.53644
## ns(time, df = 18 * 18)319 2.19926 1.80392 1.219 0.22279
## ns(time, df = 18 * 18)320 2.78247 1.75633 1.584 0.11314
## ns(time, df = 18 * 18)321 1.48182 1.82397 0.812 0.41655
## ns(time, df = 18 * 18)322 1.48865 1.35587 1.098 0.27223
## ns(time, df = 18 * 18)323 3.87955 3.66740 1.058 0.29013
## ns(time, df = 18 * 18)324 -0.77668 1.23656 -0.628 0.52994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(23118.62) family taken to be 1)
##
## Null deviance: 1149.74 on 938 degrees of freedom
## Residual deviance: 761.04 on 614 degrees of freedom
## AIC: 3194.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 23119
## Std. Err.: 88324
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2542.938## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 20), data = week,
## init.theta = 21065.09862, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4767 -0.7110 -0.1282 0.4877 2.2062
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.66890 1.70108 -0.981 0.3266
## ns(time, df = 18 * 20)1 -0.41032 2.21548 -0.185 0.8531
## ns(time, df = 18 * 20)2 0.46717 2.66490 0.175 0.8608
## ns(time, df = 18 * 20)3 4.30619 2.23081 1.930 0.0536 .
## ns(time, df = 18 * 20)4 -3.24328 3.92050 -0.827 0.4081
## ns(time, df = 18 * 20)5 0.46662 3.41267 0.137 0.8912
## ns(time, df = 18 * 20)6 -0.01969 2.59608 -0.008 0.9939
## ns(time, df = 18 * 20)7 3.56924 2.08151 1.715 0.0864 .
## ns(time, df = 18 * 20)8 1.06025 2.15787 0.491 0.6232
## ns(time, df = 18 * 20)9 2.36551 2.17423 1.088 0.2766
## ns(time, df = 18 * 20)10 0.15735 2.32174 0.068 0.9460
## ns(time, df = 18 * 20)11 3.09333 2.07109 1.494 0.1353
## ns(time, df = 18 * 20)12 0.95207 2.08870 0.456 0.6485
## ns(time, df = 18 * 20)13 3.54689 1.97691 1.794 0.0728 .
## ns(time, df = 18 * 20)14 1.30363 2.12637 0.613 0.5398
## ns(time, df = 18 * 20)15 0.74875 2.18524 0.343 0.7319
## ns(time, df = 18 * 20)16 3.58614 1.98479 1.807 0.0708 .
## ns(time, df = 18 * 20)17 1.01738 2.05951 0.494 0.6213
## ns(time, df = 18 * 20)18 3.04584 1.97909 1.539 0.1238
## ns(time, df = 18 * 20)19 1.66914 2.01921 0.827 0.4084
## ns(time, df = 18 * 20)20 2.63144 2.00685 1.311 0.1898
## ns(time, df = 18 * 20)21 1.17551 2.02675 0.580 0.5619
## ns(time, df = 18 * 20)22 4.02012 1.92749 2.086 0.0370 *
## ns(time, df = 18 * 20)23 1.24832 2.11593 0.590 0.5552
## ns(time, df = 18 * 20)24 0.31896 2.34453 0.136 0.8918
## ns(time, df = 18 * 20)25 2.68620 2.03405 1.321 0.1866
## ns(time, df = 18 * 20)26 3.07077 2.02116 1.519 0.1287
## ns(time, df = 18 * 20)27 -0.07193 2.34654 -0.031 0.9755
## ns(time, df = 18 * 20)28 2.46235 2.17123 1.134 0.2568
## ns(time, df = 18 * 20)29 2.04914 2.26888 0.903 0.3664
## ns(time, df = 18 * 20)30 -0.44962 2.88114 -0.156 0.8760
## ns(time, df = 18 * 20)31 0.85300 2.78754 0.306 0.7596
## ns(time, df = 18 * 20)32 0.36483 2.44218 0.149 0.8812
## ns(time, df = 18 * 20)33 2.96036 2.04489 1.448 0.1477
## ns(time, df = 18 * 20)34 2.14035 2.00590 1.067 0.2860
## ns(time, df = 18 * 20)35 2.24009 2.01845 1.110 0.2671
## ns(time, df = 18 * 20)36 2.09114 2.06291 1.014 0.3107
## ns(time, df = 18 * 20)37 1.24853 2.10384 0.593 0.5529
## ns(time, df = 18 * 20)38 2.76631 1.97026 1.404 0.1603
## ns(time, df = 18 * 20)39 2.58538 1.95714 1.321 0.1865
## ns(time, df = 18 * 20)40 2.02516 2.02172 1.002 0.3165
## ns(time, df = 18 * 20)41 1.81512 2.07007 0.877 0.3806
## ns(time, df = 18 * 20)42 1.86742 2.02739 0.921 0.3570
## ns(time, df = 18 * 20)43 2.90124 1.95322 1.485 0.1374
## ns(time, df = 18 * 20)44 2.16374 1.99045 1.087 0.2770
## ns(time, df = 18 * 20)45 2.02556 2.04791 0.989 0.3226
## ns(time, df = 18 * 20)46 1.92349 2.10299 0.915 0.3604
## ns(time, df = 18 * 20)47 1.28854 2.12883 0.605 0.5450
## ns(time, df = 18 * 20)48 2.65568 2.00547 1.324 0.1854
## ns(time, df = 18 * 20)49 2.57623 2.03492 1.266 0.2055
## ns(time, df = 18 * 20)50 0.54887 2.25553 0.243 0.8077
## ns(time, df = 18 * 20)51 2.17381 2.10934 1.031 0.3027
## ns(time, df = 18 * 20)52 2.40643 2.07674 1.159 0.2466
## ns(time, df = 18 * 20)53 0.91453 2.18408 0.419 0.6754
## ns(time, df = 18 * 20)54 2.48427 2.05402 1.209 0.2265
## ns(time, df = 18 * 20)55 2.06172 2.04256 1.009 0.3128
## ns(time, df = 18 * 20)56 1.89310 2.05422 0.922 0.3568
## ns(time, df = 18 * 20)57 2.52094 2.05467 1.227 0.2198
## ns(time, df = 18 * 20)58 1.28773 2.21227 0.582 0.5605
## ns(time, df = 18 * 20)59 0.73523 2.20733 0.333 0.7391
## ns(time, df = 18 * 20)60 3.31008 1.96304 1.686 0.0918 .
## ns(time, df = 18 * 20)61 2.30074 1.97669 1.164 0.2444
## ns(time, df = 18 * 20)62 1.66650 2.04154 0.816 0.4143
## ns(time, df = 18 * 20)63 2.45563 1.99181 1.233 0.2176
## ns(time, df = 18 * 20)64 2.12620 1.96837 1.080 0.2801
## ns(time, df = 18 * 20)65 3.08173 1.94812 1.582 0.1137
## ns(time, df = 18 * 20)66 1.58910 2.07640 0.765 0.4441
## ns(time, df = 18 * 20)67 1.40776 2.15611 0.653 0.5138
## ns(time, df = 18 * 20)68 2.23429 2.08393 1.072 0.2837
## ns(time, df = 18 * 20)69 1.66519 2.07151 0.804 0.4215
## ns(time, df = 18 * 20)70 2.56544 2.01176 1.275 0.2022
## ns(time, df = 18 * 20)71 1.66384 2.01915 0.824 0.4099
## ns(time, df = 18 * 20)72 3.27741 1.98658 1.650 0.0990 .
## ns(time, df = 18 * 20)73 0.23048 2.14325 0.108 0.9144
## ns(time, df = 18 * 20)74 3.82880 1.96962 1.944 0.0519 .
## ns(time, df = 18 * 20)75 0.73735 2.04873 0.360 0.7189
## ns(time, df = 18 * 20)76 4.28554 1.99671 2.146 0.0318 *
## ns(time, df = 18 * 20)77 -0.46361 2.45360 -0.189 0.8501
## ns(time, df = 18 * 20)78 0.64195 2.52735 0.254 0.7995
## ns(time, df = 18 * 20)79 2.58580 2.28506 1.132 0.2578
## ns(time, df = 18 * 20)80 -0.08132 2.45626 -0.033 0.9736
## ns(time, df = 18 * 20)81 3.33435 2.32538 1.434 0.1516
## ns(time, df = 18 * 20)82 -2.52729 2.90300 -0.871 0.3840
## ns(time, df = 18 * 20)83 4.24840 2.07014 2.052 0.0401 *
## ns(time, df = 18 * 20)84 0.84636 2.06476 0.410 0.6819
## ns(time, df = 18 * 20)85 3.43992 1.96015 1.755 0.0793 .
## ns(time, df = 18 * 20)86 1.19391 2.02217 0.590 0.5549
## ns(time, df = 18 * 20)87 3.35749 1.95775 1.715 0.0864 .
## ns(time, df = 18 * 20)88 1.23868 2.05355 0.603 0.5464
## ns(time, df = 18 * 20)89 2.44830 1.99558 1.227 0.2199
## ns(time, df = 18 * 20)90 2.59609 1.98093 1.311 0.1900
## ns(time, df = 18 * 20)91 1.30657 2.01677 0.648 0.5171
## ns(time, df = 18 * 20)92 3.93686 1.94225 2.027 0.0427 *
## ns(time, df = 18 * 20)93 0.70797 2.15527 0.328 0.7425
## ns(time, df = 18 * 20)94 2.19429 2.30715 0.951 0.3416
## ns(time, df = 18 * 20)95 -0.86592 2.63150 -0.329 0.7421
## ns(time, df = 18 * 20)96 3.30822 2.17198 1.523 0.1277
## ns(time, df = 18 * 20)97 0.31853 2.22766 0.143 0.8863
## ns(time, df = 18 * 20)98 3.20248 2.02779 1.579 0.1143
## ns(time, df = 18 * 20)99 1.22959 2.06353 0.596 0.5513
## ns(time, df = 18 * 20)100 3.18320 2.01460 1.580 0.1141
## ns(time, df = 18 * 20)101 0.16775 2.12958 0.079 0.9372
## ns(time, df = 18 * 20)102 4.29563 1.93644 2.218 0.0265 *
## ns(time, df = 18 * 20)103 0.70882 2.04685 0.346 0.7291
## ns(time, df = 18 * 20)104 3.25507 1.96831 1.654 0.0982 .
## ns(time, df = 18 * 20)105 1.35512 2.00941 0.674 0.5001
## ns(time, df = 18 * 20)106 3.39996 1.95165 1.742 0.0815 .
## ns(time, df = 18 * 20)107 1.16826 2.06286 0.566 0.5712
## ns(time, df = 18 * 20)108 2.31619 1.99720 1.160 0.2462
## ns(time, df = 18 * 20)109 2.59235 1.93607 1.339 0.1806
## ns(time, df = 18 * 20)110 2.75157 1.92737 1.428 0.1534
## ns(time, df = 18 * 20)111 2.31844 1.98509 1.168 0.2428
## ns(time, df = 18 * 20)112 1.64051 2.08453 0.787 0.4313
## ns(time, df = 18 * 20)113 1.65754 2.05074 0.808 0.4189
## ns(time, df = 18 * 20)114 3.27036 1.97094 1.659 0.0971 .
## ns(time, df = 18 * 20)115 0.86697 2.06377 0.420 0.6744
## ns(time, df = 18 * 20)116 3.23084 1.93006 1.674 0.0941 .
## ns(time, df = 18 * 20)117 2.57864 1.94403 1.326 0.1847
## ns(time, df = 18 * 20)118 1.59049 2.02009 0.787 0.4311
## ns(time, df = 18 * 20)119 3.29869 2.02680 1.628 0.1036
## ns(time, df = 18 * 20)120 -0.03963 2.36494 -0.017 0.9866
## ns(time, df = 18 * 20)121 2.42030 2.35756 1.027 0.3046
## ns(time, df = 18 * 20)122 -1.24325 2.53775 -0.490 0.6242
## ns(time, df = 18 * 20)123 4.34597 2.00286 2.170 0.0300 *
## ns(time, df = 18 * 20)124 1.44905 2.07340 0.699 0.4846
## ns(time, df = 18 * 20)125 1.25788 2.18600 0.575 0.5650
## ns(time, df = 18 * 20)126 2.21790 2.07756 1.068 0.2857
## ns(time, df = 18 * 20)127 1.93194 2.03874 0.948 0.3433
## ns(time, df = 18 * 20)128 2.13849 1.95838 1.092 0.2748
## ns(time, df = 18 * 20)129 3.71594 1.91039 1.945 0.0518 .
## ns(time, df = 18 * 20)130 0.70110 2.04802 0.342 0.7321
## ns(time, df = 18 * 20)131 3.38269 1.93839 1.745 0.0810 .
## ns(time, df = 18 * 20)132 2.21991 1.99526 1.113 0.2659
## ns(time, df = 18 * 20)133 0.96064 2.09917 0.458 0.6472
## ns(time, df = 18 * 20)134 3.52952 1.98696 1.776 0.0757 .
## ns(time, df = 18 * 20)135 0.56275 2.11149 0.267 0.7898
## ns(time, df = 18 * 20)136 3.27598 1.97606 1.658 0.0974 .
## ns(time, df = 18 * 20)137 1.69832 2.02387 0.839 0.4014
## ns(time, df = 18 * 20)138 2.11911 2.01569 1.051 0.2931
## ns(time, df = 18 * 20)139 2.53694 1.98906 1.275 0.2022
## ns(time, df = 18 * 20)140 1.55008 1.99604 0.777 0.4374
## ns(time, df = 18 * 20)141 3.66001 1.92151 1.905 0.0568 .
## ns(time, df = 18 * 20)142 1.50489 2.05059 0.734 0.4630
## ns(time, df = 18 * 20)143 1.44328 2.14528 0.673 0.5011
## ns(time, df = 18 * 20)144 2.18407 2.06685 1.057 0.2906
## ns(time, df = 18 * 20)145 1.93264 2.02981 0.952 0.3410
## ns(time, df = 18 * 20)146 2.27613 1.94925 1.168 0.2429
## ns(time, df = 18 * 20)147 3.32607 1.89401 1.756 0.0791 .
## ns(time, df = 18 * 20)148 1.75468 1.92562 0.911 0.3622
## ns(time, df = 18 * 20)149 3.97607 1.89544 2.098 0.0359 *
## ns(time, df = 18 * 20)150 0.99685 2.08239 0.479 0.6321
## ns(time, df = 18 * 20)151 1.62245 2.14946 0.755 0.4504
## ns(time, df = 18 * 20)152 2.02655 2.06410 0.982 0.3262
## ns(time, df = 18 * 20)153 2.35591 2.00952 1.172 0.2410
## ns(time, df = 18 * 20)154 2.38267 2.02976 1.174 0.2404
## ns(time, df = 18 * 20)155 1.74397 2.17903 0.800 0.4235
## ns(time, df = 18 * 20)156 0.04978 2.33053 0.021 0.9830
## ns(time, df = 18 * 20)157 3.53967 2.03847 1.736 0.0825 .
## ns(time, df = 18 * 20)158 1.62596 2.16555 0.751 0.4528
## ns(time, df = 18 * 20)159 -0.17766 2.45581 -0.072 0.9423
## ns(time, df = 18 * 20)160 3.04752 2.11555 1.441 0.1497
## ns(time, df = 18 * 20)161 1.26166 2.15952 0.584 0.5591
## ns(time, df = 18 * 20)162 1.58744 2.09038 0.759 0.4476
## ns(time, df = 18 * 20)163 3.09989 1.97304 1.571 0.1162
## ns(time, df = 18 * 20)164 1.75163 2.03593 0.860 0.3896
## ns(time, df = 18 * 20)165 1.67177 2.02695 0.825 0.4095
## ns(time, df = 18 * 20)166 3.43754 1.95158 1.761 0.0782 .
## ns(time, df = 18 * 20)167 0.99402 2.07191 0.480 0.6314
## ns(time, df = 18 * 20)168 2.72197 1.99754 1.363 0.1730
## ns(time, df = 18 * 20)169 2.44178 2.03687 1.199 0.2306
## ns(time, df = 18 * 20)170 0.40013 2.19753 0.182 0.8555
## ns(time, df = 18 * 20)171 3.36634 1.99889 1.684 0.0922 .
## ns(time, df = 18 * 20)172 1.35172 2.04579 0.661 0.5088
## ns(time, df = 18 * 20)173 2.77996 2.00470 1.387 0.1655
## ns(time, df = 18 * 20)174 1.32742 2.03722 0.652 0.5147
## ns(time, df = 18 * 20)175 3.25988 1.95007 1.672 0.0946 .
## ns(time, df = 18 * 20)176 1.74390 2.01566 0.865 0.3869
## ns(time, df = 18 * 20)177 1.92124 2.01379 0.954 0.3401
## ns(time, df = 18 * 20)178 2.90605 1.95412 1.487 0.1370
## ns(time, df = 18 * 20)179 2.23929 2.01787 1.110 0.2671
## ns(time, df = 18 * 20)180 1.23728 2.16361 0.572 0.5674
## ns(time, df = 18 * 20)181 2.28041 2.18561 1.043 0.2968
## ns(time, df = 18 * 20)182 -0.30443 2.24833 -0.135 0.8923
## ns(time, df = 18 * 20)183 4.63994 1.93748 2.395 0.0166 *
## ns(time, df = 18 * 20)184 0.68529 2.02541 0.338 0.7351
## ns(time, df = 18 * 20)185 3.56188 1.94463 1.832 0.0670 .
## ns(time, df = 18 * 20)186 1.47980 2.03530 0.727 0.4672
## ns(time, df = 18 * 20)187 1.85747 2.01950 0.920 0.3577
## ns(time, df = 18 * 20)188 3.09650 1.93916 1.597 0.1103
## ns(time, df = 18 * 20)189 2.10745 1.99363 1.057 0.2905
## ns(time, df = 18 * 20)190 1.66575 2.05367 0.811 0.4173
## ns(time, df = 18 * 20)191 2.60173 2.01608 1.290 0.1969
## ns(time, df = 18 * 20)192 1.40477 2.04959 0.685 0.4931
## ns(time, df = 18 * 20)193 2.56302 1.92149 1.334 0.1822
## ns(time, df = 18 * 20)194 3.61731 1.86523 1.939 0.0525 .
## ns(time, df = 18 * 20)195 1.98106 1.92388 1.030 0.3031
## ns(time, df = 18 * 20)196 2.74072 1.92460 1.424 0.1544
## ns(time, df = 18 * 20)197 2.40453 1.94392 1.237 0.2161
## ns(time, df = 18 * 20)198 1.96683 1.94438 1.012 0.3118
## ns(time, df = 18 * 20)199 3.51717 1.88709 1.864 0.0623 .
## ns(time, df = 18 * 20)200 2.25778 1.96176 1.151 0.2498
## ns(time, df = 18 * 20)201 1.88659 2.13924 0.882 0.3778
## ns(time, df = 18 * 20)202 0.20365 2.37546 0.086 0.9317
## ns(time, df = 18 * 20)203 2.76591 2.15561 1.283 0.1994
## ns(time, df = 18 * 20)204 1.06937 2.24831 0.476 0.6343
## ns(time, df = 18 * 20)205 1.60760 2.25355 0.713 0.4756
## ns(time, df = 18 * 20)206 1.57205 2.23444 0.704 0.4817
## ns(time, df = 18 * 20)207 2.10715 2.29361 0.919 0.3583
## ns(time, df = 18 * 20)208 0.89703 2.86382 0.313 0.7541
## ns(time, df = 18 * 20)209 -3.95149 4.44073 -0.890 0.3736
## ns(time, df = 18 * 20)210 3.59876 2.42892 1.482 0.1384
## ns(time, df = 18 * 20)211 0.93602 2.34973 0.398 0.6904
## ns(time, df = 18 * 20)212 0.57723 2.20810 0.261 0.7938
## ns(time, df = 18 * 20)213 5.08968 2.05288 2.479 0.0132 *
## ns(time, df = 18 * 20)214 -1.65175 2.76673 -0.597 0.5505
## ns(time, df = 18 * 20)215 -0.38898 2.84441 -0.137 0.8912
## ns(time, df = 18 * 20)216 2.71559 2.09419 1.297 0.1947
## ns(time, df = 18 * 20)217 2.67159 1.96893 1.357 0.1748
## ns(time, df = 18 * 20)218 2.61525 2.01192 1.300 0.1936
## ns(time, df = 18 * 20)219 0.90805 2.23519 0.406 0.6846
## ns(time, df = 18 * 20)220 1.34681 2.20675 0.610 0.5417
## ns(time, df = 18 * 20)221 2.44837 2.06172 1.188 0.2350
## ns(time, df = 18 * 20)222 1.82267 2.04962 0.889 0.3739
## ns(time, df = 18 * 20)223 2.23316 2.00011 1.117 0.2642
## ns(time, df = 18 * 20)224 2.77911 1.98418 1.401 0.1613
## ns(time, df = 18 * 20)225 1.01544 2.05797 0.493 0.6217
## ns(time, df = 18 * 20)226 3.52330 1.94245 1.814 0.0697 .
## ns(time, df = 18 * 20)227 1.75947 2.02563 0.869 0.3851
## ns(time, df = 18 * 20)228 1.48496 2.08470 0.712 0.4763
## ns(time, df = 18 * 20)229 2.90075 2.01605 1.439 0.1502
## ns(time, df = 18 * 20)230 0.90412 2.07738 0.435 0.6634
## ns(time, df = 18 * 20)231 3.73572 1.96860 1.898 0.0577 .
## ns(time, df = 18 * 20)232 1.17584 2.14873 0.547 0.5842
## ns(time, df = 18 * 20)233 0.93975 2.35410 0.399 0.6897
## ns(time, df = 18 * 20)234 1.39134 2.19612 0.634 0.5264
## ns(time, df = 18 * 20)235 3.13688 2.09043 1.501 0.1335
## ns(time, df = 18 * 20)236 -0.23235 2.31928 -0.100 0.9202
## ns(time, df = 18 * 20)237 3.84922 2.15079 1.790 0.0735 .
## ns(time, df = 18 * 20)238 -1.50551 2.59684 -0.580 0.5621
## ns(time, df = 18 * 20)239 3.35016 2.08789 1.605 0.1086
## ns(time, df = 18 * 20)240 1.45227 2.05337 0.707 0.4794
## ns(time, df = 18 * 20)241 3.31836 2.03684 1.629 0.1033
## ns(time, df = 18 * 20)242 0.39177 2.38288 0.164 0.8694
## ns(time, df = 18 * 20)243 0.24521 2.43943 0.101 0.9199
## ns(time, df = 18 * 20)244 4.06967 2.25352 1.806 0.0709 .
## ns(time, df = 18 * 20)245 -2.75940 3.14249 -0.878 0.3799
## ns(time, df = 18 * 20)246 2.85509 2.24363 1.273 0.2032
## ns(time, df = 18 * 20)247 1.31282 2.07763 0.632 0.5275
## ns(time, df = 18 * 20)248 3.87578 1.98749 1.950 0.0512 .
## ns(time, df = 18 * 20)249 0.25236 2.28170 0.111 0.9119
## ns(time, df = 18 * 20)250 0.96394 2.20656 0.437 0.6622
## ns(time, df = 18 * 20)251 3.63459 1.99232 1.824 0.0681 .
## ns(time, df = 18 * 20)252 1.01661 2.09883 0.484 0.6281
## ns(time, df = 18 * 20)253 2.53299 2.07302 1.222 0.2218
## ns(time, df = 18 * 20)254 1.29686 2.13049 0.609 0.5427
## ns(time, df = 18 * 20)255 2.97908 2.15561 1.382 0.1670
## ns(time, df = 18 * 20)256 -0.91835 2.65583 -0.346 0.7295
## ns(time, df = 18 * 20)257 2.63484 2.35813 1.117 0.2638
## ns(time, df = 18 * 20)258 -0.49774 2.40970 -0.207 0.8364
## ns(time, df = 18 * 20)259 3.91585 2.03374 1.925 0.0542 .
## ns(time, df = 18 * 20)260 0.96717 2.13064 0.454 0.6499
## ns(time, df = 18 * 20)261 1.85906 2.08321 0.892 0.3722
## ns(time, df = 18 * 20)262 2.89328 2.02006 1.432 0.1521
## ns(time, df = 18 * 20)263 0.97012 2.14270 0.453 0.6507
## ns(time, df = 18 * 20)264 2.45394 2.06494 1.188 0.2347
## ns(time, df = 18 * 20)265 1.66636 2.05859 0.809 0.4182
## ns(time, df = 18 * 20)266 2.97581 2.04574 1.455 0.1458
## ns(time, df = 18 * 20)267 0.61431 2.29007 0.268 0.7885
## ns(time, df = 18 * 20)268 2.59798 2.50233 1.038 0.2992
## ns(time, df = 18 * 20)269 -4.02394 3.66814 -1.097 0.2726
## ns(time, df = 18 * 20)270 4.89534 2.26670 2.160 0.0308 *
## ns(time, df = 18 * 20)271 -0.29495 2.40048 -0.123 0.9022
## ns(time, df = 18 * 20)272 1.97785 2.24568 0.881 0.3785
## ns(time, df = 18 * 20)273 1.46278 2.13082 0.686 0.4924
## ns(time, df = 18 * 20)274 2.75292 2.01632 1.365 0.1722
## ns(time, df = 18 * 20)275 1.56782 2.02897 0.773 0.4397
## ns(time, df = 18 * 20)276 3.11065 1.98199 1.569 0.1165
## ns(time, df = 18 * 20)277 1.05834 2.08631 0.507 0.6120
## ns(time, df = 18 * 20)278 2.89468 2.01761 1.435 0.1514
## ns(time, df = 18 * 20)279 2.58804 2.19061 1.181 0.2374
## ns(time, df = 18 * 20)280 -2.10693 3.41758 -0.616 0.5376
## ns(time, df = 18 * 20)281 1.00812 2.64943 0.381 0.7036
## ns(time, df = 18 * 20)282 1.71247 2.17137 0.789 0.4303
## ns(time, df = 18 * 20)283 2.68528 1.97955 1.357 0.1749
## ns(time, df = 18 * 20)284 2.75587 1.96187 1.405 0.1601
## ns(time, df = 18 * 20)285 1.36094 2.03464 0.669 0.5036
## ns(time, df = 18 * 20)286 2.94824 1.95481 1.508 0.1315
## ns(time, df = 18 * 20)287 1.94331 1.96017 0.991 0.3215
## ns(time, df = 18 * 20)288 2.97578 1.91969 1.550 0.1211
## ns(time, df = 18 * 20)289 2.36246 1.95378 1.209 0.2266
## ns(time, df = 18 * 20)290 1.83085 1.99111 0.920 0.3578
## ns(time, df = 18 * 20)291 3.11891 1.96327 1.589 0.1121
## ns(time, df = 18 * 20)292 1.04960 2.07274 0.506 0.6126
## ns(time, df = 18 * 20)293 2.70987 1.96500 1.379 0.1679
## ns(time, df = 18 * 20)294 3.41113 1.99501 1.710 0.0873 .
## ns(time, df = 18 * 20)295 -1.71009 2.41420 -0.708 0.4787
## ns(time, df = 18 * 20)296 4.92093 1.94897 2.525 0.0116 *
## ns(time, df = 18 * 20)297 0.61789 2.01602 0.306 0.7592
## ns(time, df = 18 * 20)298 3.74386 1.92323 1.947 0.0516 .
## ns(time, df = 18 * 20)299 1.26547 1.98877 0.636 0.5246
## ns(time, df = 18 * 20)300 3.33421 1.92897 1.728 0.0839 .
## ns(time, df = 18 * 20)301 1.87041 1.99413 0.938 0.3483
## ns(time, df = 18 * 20)302 2.15228 2.04136 1.054 0.2917
## ns(time, df = 18 * 20)303 1.69563 2.06207 0.822 0.4109
## ns(time, df = 18 * 20)304 2.73537 2.03724 1.343 0.1794
## ns(time, df = 18 * 20)305 1.40909 2.20328 0.640 0.5225
## ns(time, df = 18 * 20)306 0.35655 2.29786 0.155 0.8767
## ns(time, df = 18 * 20)307 3.57509 2.06395 1.732 0.0832 .
## ns(time, df = 18 * 20)308 0.44572 2.23578 0.199 0.8420
## ns(time, df = 18 * 20)309 2.13071 2.12185 1.004 0.3153
## ns(time, df = 18 * 20)310 2.12589 2.06888 1.028 0.3042
## ns(time, df = 18 * 20)311 1.54730 2.04906 0.755 0.4502
## ns(time, df = 18 * 20)312 3.08204 1.94963 1.581 0.1139
## ns(time, df = 18 * 20)313 2.01983 1.98047 1.020 0.3078
## ns(time, df = 18 * 20)314 2.62328 2.02026 1.298 0.1941
## ns(time, df = 18 * 20)315 0.70556 2.14793 0.328 0.7425
## ns(time, df = 18 * 20)316 2.94478 1.97623 1.490 0.1362
## ns(time, df = 18 * 20)317 2.35564 1.96252 1.200 0.2300
## ns(time, df = 18 * 20)318 2.06962 1.96713 1.052 0.2928
## ns(time, df = 18 * 20)319 3.90636 2.00860 1.945 0.0518 .
## ns(time, df = 18 * 20)320 -2.53499 2.66923 -0.950 0.3423
## ns(time, df = 18 * 20)321 5.55607 2.23243 2.489 0.0128 *
## ns(time, df = 18 * 20)322 -3.94273 3.17285 -1.243 0.2140
## ns(time, df = 18 * 20)323 3.86196 2.09339 1.845 0.0651 .
## ns(time, df = 18 * 20)324 1.78359 2.00447 0.890 0.3736
## ns(time, df = 18 * 20)325 2.63342 1.95255 1.349 0.1774
## ns(time, df = 18 * 20)326 2.72988 1.95631 1.395 0.1629
## ns(time, df = 18 * 20)327 1.52540 2.04878 0.745 0.4565
## ns(time, df = 18 * 20)328 2.43540 2.02520 1.203 0.2292
## ns(time, df = 18 * 20)329 2.37965 2.10041 1.133 0.2572
## ns(time, df = 18 * 20)330 -0.23750 2.37630 -0.100 0.9204
## ns(time, df = 18 * 20)331 3.16346 2.06198 1.534 0.1250
## ns(time, df = 18 * 20)332 1.26761 2.06016 0.615 0.5384
## ns(time, df = 18 * 20)333 3.29148 1.99393 1.651 0.0988 .
## ns(time, df = 18 * 20)334 0.74479 2.12522 0.350 0.7260
## ns(time, df = 18 * 20)335 3.60449 2.11905 1.701 0.0889 .
## ns(time, df = 18 * 20)336 -1.60231 2.69014 -0.596 0.5514
## ns(time, df = 18 * 20)337 3.41476 2.26879 1.505 0.1323
## ns(time, df = 18 * 20)338 -0.16796 2.50944 -0.067 0.9466
## ns(time, df = 18 * 20)339 1.36680 2.20849 0.619 0.5360
## ns(time, df = 18 * 20)340 3.40925 2.01848 1.689 0.0912 .
## ns(time, df = 18 * 20)341 1.28368 2.17331 0.591 0.5548
## ns(time, df = 18 * 20)342 2.43893 2.57662 0.947 0.3439
## ns(time, df = 18 * 20)343 -6.17193 4.77688 -1.292 0.1963
## ns(time, df = 18 * 20)344 5.17037 2.31253 2.236 0.0254 *
## ns(time, df = 18 * 20)345 0.30502 2.25598 0.135 0.8924
## ns(time, df = 18 * 20)346 2.27346 2.13015 1.067 0.2858
## ns(time, df = 18 * 20)347 2.14967 2.12716 1.011 0.3122
## ns(time, df = 18 * 20)348 0.80249 2.26072 0.355 0.7226
## ns(time, df = 18 * 20)349 2.00384 2.09036 0.959 0.3378
## ns(time, df = 18 * 20)350 2.39373 1.96993 1.215 0.2243
## ns(time, df = 18 * 20)351 3.32506 1.95554 1.700 0.0891 .
## ns(time, df = 18 * 20)352 0.48980 2.16696 0.226 0.8212
## ns(time, df = 18 * 20)353 2.87694 2.10294 1.368 0.1713
## ns(time, df = 18 * 20)354 0.10488 2.18215 0.048 0.9617
## ns(time, df = 18 * 20)355 3.99966 1.93452 2.068 0.0387 *
## ns(time, df = 18 * 20)356 1.68296 1.98317 0.849 0.3961
## ns(time, df = 18 * 20)357 2.38781 1.99653 1.196 0.2317
## ns(time, df = 18 * 20)358 1.23602 1.48855 0.830 0.4063
## ns(time, df = 18 * 20)359 4.26216 4.05073 1.052 0.2927
## ns(time, df = 18 * 20)360 -0.60997 1.26328 -0.483 0.6292
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21065.1) family taken to be 1)
##
## Null deviance: 1149.73 on 938 degrees of freedom
## Residual deviance: 720.76 on 578 degrees of freedom
## AIC: 3226.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21065
## Std. Err.: 71660
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2502.663## model aic theta
## 1 ns1 py 2937.858 58.23421
## 2 ns5 py 3002.534 13015.44671
## 3 ns7 py 3031.901 16102.46868
## 4 ns9 py 3046.814 19631.96389
## 5 ns10 py 3058.863 20157.87656
## 6 ns14 py 3133.347 20479.98090
## 7 ns15 py 3145.710 21819.86914
## 8 ns16 py 3163.313 19516.88827
## 9 ns18 py 3194.938 23118.62024
## 10 ns20 py 3226.663 21065.09862##
## Call:
## glm.nb(formula = smptbBM ~ ns(time, df = 18 * 15), data = week,
## init.theta = 21819.86914, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5398 -0.8189 -0.1214 0.5090 2.2049
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.09154 1.26513 -0.863 0.3883
## ns(time, df = 18 * 15)1 -2.10234 1.91810 -1.096 0.2731
## ns(time, df = 18 * 15)2 4.62206 2.17796 2.122 0.0338 *
## ns(time, df = 18 * 15)3 -5.31501 3.58915 -1.481 0.1386
## ns(time, df = 18 * 15)4 0.06596 2.29180 0.029 0.9770
## ns(time, df = 18 * 15)5 2.49150 1.68355 1.480 0.1389
## ns(time, df = 18 * 15)6 0.80090 1.74701 0.458 0.6466
## ns(time, df = 18 * 15)7 0.86386 1.75354 0.493 0.6223
## ns(time, df = 18 * 15)8 1.22863 1.66663 0.737 0.4610
## ns(time, df = 18 * 15)9 1.79068 1.57462 1.137 0.2554
## ns(time, df = 18 * 15)10 1.93344 1.58610 1.219 0.2228
## ns(time, df = 18 * 15)11 0.26995 1.68146 0.161 0.8725
## ns(time, df = 18 * 15)12 2.57728 1.54883 1.664 0.0961 .
## ns(time, df = 18 * 15)13 0.93239 1.58224 0.589 0.5557
## ns(time, df = 18 * 15)14 2.33497 1.54738 1.509 0.1313
## ns(time, df = 18 * 15)15 0.59284 1.59968 0.371 0.7109
## ns(time, df = 18 * 15)16 2.82578 1.50727 1.875 0.0608 .
## ns(time, df = 18 * 15)17 1.45666 1.60809 0.906 0.3650
## ns(time, df = 18 * 15)18 -0.54551 1.77742 -0.307 0.7589
## ns(time, df = 18 * 15)19 3.86620 1.57688 2.452 0.0142 *
## ns(time, df = 18 * 15)20 -1.21687 1.86609 -0.652 0.5143
## ns(time, df = 18 * 15)21 2.79801 1.73072 1.617 0.1059
## ns(time, df = 18 * 15)22 -0.34816 2.07274 -0.168 0.8666
## ns(time, df = 18 * 15)23 -0.77515 2.31267 -0.335 0.7375
## ns(time, df = 18 * 15)24 1.21125 1.76675 0.686 0.4930
## ns(time, df = 18 * 15)25 2.11826 1.57787 1.342 0.1794
## ns(time, df = 18 * 15)26 1.55474 1.58862 0.979 0.3277
## ns(time, df = 18 * 15)27 1.09506 1.63143 0.671 0.5021
## ns(time, df = 18 * 15)28 1.52927 1.56859 0.975 0.3296
## ns(time, df = 18 * 15)29 2.33340 1.52253 1.533 0.1254
## ns(time, df = 18 * 15)30 1.18283 1.59886 0.740 0.4594
## ns(time, df = 18 * 15)31 1.08285 1.60387 0.675 0.4996
## ns(time, df = 18 * 15)32 2.42948 1.52455 1.594 0.1110
## ns(time, df = 18 * 15)33 1.27724 1.57686 0.810 0.4179
## ns(time, df = 18 * 15)34 1.70561 1.62260 1.051 0.2932
## ns(time, df = 18 * 15)35 0.32597 1.68095 0.194 0.8462
## ns(time, df = 18 * 15)36 2.94135 1.56376 1.881 0.0600 .
## ns(time, df = 18 * 15)37 0.08199 1.71726 0.048 0.9619
## ns(time, df = 18 * 15)38 1.82533 1.65027 1.106 0.2687
## ns(time, df = 18 * 15)39 1.09213 1.66300 0.657 0.5114
## ns(time, df = 18 * 15)40 1.31972 1.64305 0.803 0.4219
## ns(time, df = 18 * 15)41 1.56489 1.60334 0.976 0.3291
## ns(time, df = 18 * 15)42 1.65670 1.60393 1.033 0.3016
## ns(time, df = 18 * 15)43 1.29399 1.67786 0.771 0.4406
## ns(time, df = 18 * 15)44 0.13684 1.70766 0.080 0.9361
## ns(time, df = 18 * 15)45 3.10055 1.52180 2.037 0.0416 *
## ns(time, df = 18 * 15)46 0.77430 1.59248 0.486 0.6268
## ns(time, df = 18 * 15)47 1.76333 1.55448 1.134 0.2566
## ns(time, df = 18 * 15)48 2.00150 1.52155 1.315 0.1884
## ns(time, df = 18 * 15)49 1.88274 1.56665 1.202 0.2295
## ns(time, df = 18 * 15)50 0.49875 1.69787 0.294 0.7689
## ns(time, df = 18 * 15)51 1.76442 1.62422 1.086 0.2773
## ns(time, df = 18 * 15)52 1.17611 1.60206 0.734 0.4629
## ns(time, df = 18 * 15)53 2.14741 1.55298 1.383 0.1667
## ns(time, df = 18 * 15)54 1.08525 1.59537 0.680 0.4963
## ns(time, df = 18 * 15)55 1.85227 1.56274 1.185 0.2359
## ns(time, df = 18 * 15)56 1.50716 1.55821 0.967 0.3334
## ns(time, df = 18 * 15)57 2.59962 1.60692 1.618 0.1057
## ns(time, df = 18 * 15)58 -1.28490 2.07751 -0.618 0.5363
## ns(time, df = 18 * 15)59 1.54463 1.84830 0.836 0.4033
## ns(time, df = 18 * 15)60 1.05716 1.86742 0.566 0.5713
## ns(time, df = 18 * 15)61 -0.63270 2.00036 -0.316 0.7518
## ns(time, df = 18 * 15)62 2.15365 1.63040 1.321 0.1865
## ns(time, df = 18 * 15)63 1.57508 1.56594 1.006 0.3145
## ns(time, df = 18 * 15)64 1.69895 1.54498 1.100 0.2715
## ns(time, df = 18 * 15)65 1.95382 1.53933 1.269 0.2043
## ns(time, df = 18 * 15)66 1.17784 1.57692 0.747 0.4551
## ns(time, df = 18 * 15)67 2.05650 1.54297 1.333 0.1826
## ns(time, df = 18 * 15)68 1.22129 1.55368 0.786 0.4318
## ns(time, df = 18 * 15)69 2.85629 1.54613 1.847 0.0647 .
## ns(time, df = 18 * 15)70 -0.12643 1.83958 -0.069 0.9452
## ns(time, df = 18 * 15)71 0.38638 1.90807 0.202 0.8395
## ns(time, df = 18 * 15)72 1.48341 1.72055 0.862 0.3886
## ns(time, df = 18 * 15)73 1.02162 1.65162 0.619 0.5362
## ns(time, df = 18 * 15)74 2.22853 1.57850 1.412 0.1580
## ns(time, df = 18 * 15)75 0.56274 1.63047 0.345 0.7300
## ns(time, df = 18 * 15)76 2.45360 1.52379 1.610 0.1074
## ns(time, df = 18 * 15)77 1.49842 1.54405 0.970 0.3318
## ns(time, df = 18 * 15)78 1.51440 1.54865 0.978 0.3281
## ns(time, df = 18 * 15)79 2.18819 1.52493 1.435 0.1513
## ns(time, df = 18 * 15)80 1.14485 1.57805 0.725 0.4682
## ns(time, df = 18 * 15)81 1.68663 1.53592 1.098 0.2721
## ns(time, df = 18 * 15)82 2.27915 1.49328 1.526 0.1269
## ns(time, df = 18 * 15)83 1.88891 1.54316 1.224 0.2209
## ns(time, df = 18 * 15)84 0.39608 1.64754 0.240 0.8100
## ns(time, df = 18 * 15)85 2.71731 1.53929 1.765 0.0775 .
## ns(time, df = 18 * 15)86 0.45455 1.59044 0.286 0.7750
## ns(time, df = 18 * 15)87 3.04095 1.49110 2.039 0.0414 *
## ns(time, df = 18 * 15)88 0.97599 1.56538 0.623 0.5330
## ns(time, df = 18 * 15)89 2.20497 1.60383 1.375 0.1692
## ns(time, df = 18 * 15)90 0.56385 1.86032 0.303 0.7618
## ns(time, df = 18 * 15)91 -1.39849 2.03781 -0.686 0.4925
## ns(time, df = 18 * 15)92 3.72405 1.58532 2.349 0.0188 *
## ns(time, df = 18 * 15)93 0.11009 1.68480 0.065 0.9479
## ns(time, df = 18 * 15)94 1.85501 1.63754 1.133 0.2573
## ns(time, df = 18 * 15)95 0.69467 1.60696 0.432 0.6655
## ns(time, df = 18 * 15)96 2.98449 1.48801 2.006 0.0449 *
## ns(time, df = 18 * 15)97 0.96273 1.53637 0.627 0.5309
## ns(time, df = 18 * 15)98 2.61024 1.50583 1.733 0.0830 .
## ns(time, df = 18 * 15)99 0.88271 1.58339 0.557 0.5772
## ns(time, df = 18 * 15)100 2.09998 1.56183 1.345 0.1788
## ns(time, df = 18 * 15)101 0.95686 1.59889 0.598 0.5495
## ns(time, df = 18 * 15)102 2.18664 1.54492 1.415 0.1570
## ns(time, df = 18 * 15)103 1.27781 1.57865 0.809 0.4183
## ns(time, df = 18 * 15)104 1.55186 1.55933 0.995 0.3196
## ns(time, df = 18 * 15)105 1.98347 1.51380 1.310 0.1901
## ns(time, df = 18 * 15)106 2.24417 1.53595 1.461 0.1440
## ns(time, df = 18 * 15)107 0.28463 1.69227 0.168 0.8664
## ns(time, df = 18 * 15)108 1.87389 1.59220 1.177 0.2392
## ns(time, df = 18 * 15)109 1.37998 1.53952 0.896 0.3701
## ns(time, df = 18 * 15)110 2.46343 1.46821 1.678 0.0934 .
## ns(time, df = 18 * 15)111 1.98704 1.47193 1.350 0.1770
## ns(time, df = 18 * 15)112 2.37728 1.51988 1.564 0.1178
## ns(time, df = 18 * 15)113 0.01313 1.72218 0.008 0.9939
## ns(time, df = 18 * 15)114 1.88840 1.59681 1.183 0.2370
## ns(time, df = 18 * 15)115 2.00398 1.58112 1.267 0.2050
## ns(time, df = 18 * 15)116 0.69099 1.72110 0.401 0.6881
## ns(time, df = 18 * 15)117 0.76572 1.69459 0.452 0.6514
## ns(time, df = 18 * 15)118 2.86350 1.62269 1.765 0.0776 .
## ns(time, df = 18 * 15)119 -1.27487 1.92719 -0.662 0.5083
## ns(time, df = 18 * 15)120 2.91845 1.64783 1.771 0.0765 .
## ns(time, df = 18 * 15)121 -0.31559 1.71438 -0.184 0.8539
## ns(time, df = 18 * 15)122 3.24115 1.53977 2.105 0.0353 *
## ns(time, df = 18 * 15)123 0.04992 1.63006 0.031 0.9756
## ns(time, df = 18 * 15)124 3.03717 1.52173 1.996 0.0459 *
## ns(time, df = 18 * 15)125 0.42059 1.60596 0.262 0.7934
## ns(time, df = 18 * 15)126 2.74719 1.55210 1.770 0.0767 .
## ns(time, df = 18 * 15)127 0.14674 1.68074 0.087 0.9304
## ns(time, df = 18 * 15)128 2.18985 1.57196 1.393 0.1636
## ns(time, df = 18 * 15)129 1.47102 1.58135 0.930 0.3523
## ns(time, df = 18 * 15)130 1.14645 1.58219 0.725 0.4687
## ns(time, df = 18 * 15)131 2.56096 1.52189 1.683 0.0924 .
## ns(time, df = 18 * 15)132 0.68430 1.59239 0.430 0.6674
## ns(time, df = 18 * 15)133 2.47424 1.52552 1.622 0.1048
## ns(time, df = 18 * 15)134 1.38147 1.58467 0.872 0.3833
## ns(time, df = 18 * 15)135 1.38534 1.69045 0.820 0.4125
## ns(time, df = 18 * 15)136 -0.28147 1.75646 -0.160 0.8727
## ns(time, df = 18 * 15)137 3.25223 1.51095 2.152 0.0314 *
## ns(time, df = 18 * 15)138 0.94832 1.54697 0.613 0.5399
## ns(time, df = 18 * 15)139 2.43769 1.52839 1.595 0.1107
## ns(time, df = 18 * 15)140 0.57083 1.58728 0.360 0.7191
## ns(time, df = 18 * 15)141 3.03395 1.50598 2.015 0.0439 *
## ns(time, df = 18 * 15)142 0.46268 1.61299 0.287 0.7742
## ns(time, df = 18 * 15)143 2.23224 1.56566 1.426 0.1539
## ns(time, df = 18 * 15)144 0.58474 1.56639 0.373 0.7089
## ns(time, df = 18 * 15)145 3.36101 1.44268 2.330 0.0198 *
## ns(time, df = 18 * 15)146 1.36467 1.48216 0.921 0.3572
## ns(time, df = 18 * 15)147 2.38162 1.48469 1.604 0.1087
## ns(time, df = 18 * 15)148 1.13125 1.51654 0.746 0.4557
## ns(time, df = 18 * 15)149 2.92135 1.46575 1.993 0.0463 *
## ns(time, df = 18 * 15)150 1.63323 1.56544 1.043 0.2968
## ns(time, df = 18 * 15)151 0.03945 1.81540 0.022 0.9827
## ns(time, df = 18 * 15)152 1.61050 1.72898 0.931 0.3516
## ns(time, df = 18 * 15)153 1.03035 1.78093 0.579 0.5629
## ns(time, df = 18 * 15)154 0.21899 1.84878 0.118 0.9057
## ns(time, df = 18 * 15)155 2.90008 1.86105 1.558 0.1192
## ns(time, df = 18 * 15)156 -3.84551 2.96347 -1.298 0.1944
## ns(time, df = 18 * 15)157 2.54981 1.93766 1.316 0.1882
## ns(time, df = 18 * 15)158 -0.39396 1.85833 -0.212 0.8321
## ns(time, df = 18 * 15)159 2.59848 1.59457 1.630 0.1032
## ns(time, df = 18 * 15)160 1.98273 1.68454 1.177 0.2392
## ns(time, df = 18 * 15)161 -2.62010 2.30672 -1.136 0.2560
## ns(time, df = 18 * 15)162 3.18642 1.60002 1.991 0.0464 *
## ns(time, df = 18 * 15)163 1.73402 1.57838 1.099 0.2719
## ns(time, df = 18 * 15)164 0.55352 1.72830 0.320 0.7488
## ns(time, df = 18 * 15)165 1.35691 1.67582 0.810 0.4181
## ns(time, df = 18 * 15)166 1.30891 1.61046 0.813 0.4164
## ns(time, df = 18 * 15)167 2.07373 1.55426 1.334 0.1821
## ns(time, df = 18 * 15)168 1.09389 1.57660 0.694 0.4878
## ns(time, df = 18 * 15)169 2.25997 1.52504 1.482 0.1384
## ns(time, df = 18 * 15)170 1.46931 1.56345 0.940 0.3473
## ns(time, df = 18 * 15)171 1.49445 1.60433 0.932 0.3516
## ns(time, df = 18 * 15)172 0.99940 1.60208 0.624 0.5328
## ns(time, df = 18 * 15)173 2.83463 1.54918 1.830 0.0673 .
## ns(time, df = 18 * 15)174 0.09657 1.75815 0.055 0.9562
## ns(time, df = 18 * 15)175 1.07059 1.74720 0.613 0.5400
## ns(time, df = 18 * 15)176 1.52871 1.67506 0.913 0.3614
## ns(time, df = 18 * 15)177 1.24903 1.71856 0.727 0.4674
## ns(time, df = 18 * 15)178 0.46726 1.80349 0.259 0.7956
## ns(time, df = 18 * 15)179 1.22692 1.66575 0.737 0.4614
## ns(time, df = 18 * 15)180 2.28687 1.58384 1.444 0.1488
## ns(time, df = 18 * 15)181 0.90171 1.71327 0.526 0.5987
## ns(time, df = 18 * 15)182 0.43737 1.83055 0.239 0.8112
## ns(time, df = 18 * 15)183 1.78258 1.79056 0.996 0.3195
## ns(time, df = 18 * 15)184 -0.79232 1.96539 -0.403 0.6868
## ns(time, df = 18 * 15)185 2.24813 1.60793 1.398 0.1621
## ns(time, df = 18 * 15)186 2.29335 1.59350 1.439 0.1501
## ns(time, df = 18 * 15)187 -0.92192 1.83258 -0.503 0.6149
## ns(time, df = 18 * 15)188 3.28238 1.56892 2.092 0.0364 *
## ns(time, df = 18 * 15)189 0.35799 1.66748 0.215 0.8300
## ns(time, df = 18 * 15)190 1.86208 1.64813 1.130 0.2586
## ns(time, df = 18 * 15)191 1.12002 1.74067 0.643 0.5199
## ns(time, df = 18 * 15)192 0.47955 1.95225 0.246 0.8060
## ns(time, df = 18 * 15)193 -0.48153 1.92609 -0.250 0.8026
## ns(time, df = 18 * 15)194 3.08344 1.61146 1.913 0.0557 .
## ns(time, df = 18 * 15)195 -0.03336 1.69993 -0.020 0.9843
## ns(time, df = 18 * 15)196 2.61621 1.58366 1.652 0.0985 .
## ns(time, df = 18 * 15)197 0.54519 1.67107 0.326 0.7442
## ns(time, df = 18 * 15)198 1.70233 1.62088 1.050 0.2936
## ns(time, df = 18 * 15)199 1.57113 1.62220 0.969 0.3328
## ns(time, df = 18 * 15)200 1.68523 1.75699 0.959 0.3375
## ns(time, df = 18 * 15)201 -1.62601 2.26741 -0.717 0.4733
## ns(time, df = 18 * 15)202 1.97666 1.78935 1.105 0.2693
## ns(time, df = 18 * 15)203 1.01946 1.75768 0.580 0.5619
## ns(time, df = 18 * 15)204 0.48739 1.76519 0.276 0.7825
## ns(time, df = 18 * 15)205 1.96264 1.60329 1.224 0.2209
## ns(time, df = 18 * 15)206 1.43321 1.57832 0.908 0.3638
## ns(time, df = 18 * 15)207 1.89429 1.56880 1.207 0.2272
## ns(time, df = 18 * 15)208 1.15342 1.61270 0.715 0.4745
## ns(time, df = 18 * 15)209 2.74938 1.71039 1.607 0.1080
## ns(time, df = 18 * 15)210 -2.97450 2.67134 -1.113 0.2655
## ns(time, df = 18 * 15)211 1.19387 1.81703 0.657 0.5112
## ns(time, df = 18 * 15)212 2.36196 1.55939 1.515 0.1299
## ns(time, df = 18 * 15)213 1.45198 1.55435 0.934 0.3502
## ns(time, df = 18 * 15)214 1.73049 1.53980 1.124 0.2611
## ns(time, df = 18 * 15)215 1.72086 1.51170 1.138 0.2550
## ns(time, df = 18 * 15)216 2.39314 1.49075 1.605 0.1084
## ns(time, df = 18 * 15)217 1.13495 1.54389 0.735 0.4623
## ns(time, df = 18 * 15)218 2.44871 1.52622 1.604 0.1086
## ns(time, df = 18 * 15)219 0.42103 1.59271 0.264 0.7915
## ns(time, df = 18 * 15)220 3.44173 1.51478 2.272 0.0231 *
## ns(time, df = 18 * 15)221 -0.79409 1.70634 -0.465 0.6417
## ns(time, df = 18 * 15)222 3.29328 1.49683 2.200 0.0278 *
## ns(time, df = 18 * 15)223 1.13894 1.52881 0.745 0.4563
## ns(time, df = 18 * 15)224 2.04258 1.50904 1.354 0.1759
## ns(time, df = 18 * 15)225 2.01765 1.51416 1.333 0.1827
## ns(time, df = 18 * 15)226 1.43945 1.58220 0.910 0.3629
## ns(time, df = 18 * 15)227 1.08044 1.61914 0.667 0.5046
## ns(time, df = 18 * 15)228 2.47329 1.61662 1.530 0.1260
## ns(time, df = 18 * 15)229 -0.95930 1.87244 -0.512 0.6084
## ns(time, df = 18 * 15)230 3.03623 1.62990 1.863 0.0625 .
## ns(time, df = 18 * 15)231 -0.20019 1.76243 -0.114 0.9096
## ns(time, df = 18 * 15)232 2.05442 1.62376 1.265 0.2058
## ns(time, df = 18 * 15)233 1.01812 1.59674 0.638 0.5237
## ns(time, df = 18 * 15)234 2.36172 1.52140 1.552 0.1206
## ns(time, df = 18 * 15)235 1.61358 1.56764 1.029 0.3033
## ns(time, df = 18 * 15)236 0.71373 1.63800 0.436 0.6630
## ns(time, df = 18 * 15)237 2.19842 1.52961 1.437 0.1506
## ns(time, df = 18 * 15)238 1.82258 1.51994 1.199 0.2305
## ns(time, df = 18 * 15)239 1.96497 1.56445 1.256 0.2091
## ns(time, df = 18 * 15)240 0.79867 1.73191 0.461 0.6447
## ns(time, df = 18 * 15)241 0.33760 1.80611 0.187 0.8517
## ns(time, df = 18 * 15)242 1.74847 1.61701 1.081 0.2796
## ns(time, df = 18 * 15)243 1.62059 1.53927 1.053 0.2924
## ns(time, df = 18 * 15)244 2.50060 1.51203 1.654 0.0982 .
## ns(time, df = 18 * 15)245 0.63674 1.60705 0.396 0.6919
## ns(time, df = 18 * 15)246 2.64535 1.58481 1.669 0.0951 .
## ns(time, df = 18 * 15)247 -0.31797 1.79715 -0.177 0.8596
## ns(time, df = 18 * 15)248 1.82904 1.63332 1.120 0.2628
## ns(time, df = 18 * 15)249 1.65054 1.58544 1.041 0.2978
## ns(time, df = 18 * 15)250 1.54524 1.60853 0.961 0.3367
## ns(time, df = 18 * 15)251 1.47288 1.69275 0.870 0.3842
## ns(time, df = 18 * 15)252 0.13050 1.89128 0.069 0.9450
## ns(time, df = 18 * 15)253 1.13429 1.83136 0.619 0.5357
## ns(time, df = 18 * 15)254 0.41102 1.74723 0.235 0.8140
## ns(time, df = 18 * 15)255 2.92189 1.61513 1.809 0.0704 .
## ns(time, df = 18 * 15)256 0.34036 1.91255 0.178 0.8588
## ns(time, df = 18 * 15)257 -2.21664 2.36531 -0.937 0.3487
## ns(time, df = 18 * 15)258 3.55680 1.68858 2.106 0.0352 *
## ns(time, df = 18 * 15)259 -0.35537 1.77150 -0.201 0.8410
## ns(time, df = 18 * 15)260 2.57515 1.65780 1.553 0.1203
## ns(time, df = 18 * 15)261 -0.44824 1.79554 -0.250 0.8029
## ns(time, df = 18 * 15)262 2.41928 1.55611 1.555 0.1200
## ns(time, df = 18 * 15)263 2.01726 1.54246 1.308 0.1909
## ns(time, df = 18 * 15)264 1.02382 1.66884 0.613 0.5396
## ns(time, df = 18 * 15)265 0.39838 1.68447 0.237 0.8130
## ns(time, df = 18 * 15)266 2.85906 1.51243 1.890 0.0587 .
## ns(time, df = 18 * 15)267 1.24045 1.54765 0.802 0.4228
## ns(time, df = 18 * 15)268 1.27426 1.18394 1.076 0.2818
## ns(time, df = 18 * 15)269 3.03017 3.12832 0.969 0.3327
## ns(time, df = 18 * 15)270 -0.67333 1.12614 -0.598 0.5499
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21819.87) family taken to be 1)
##
## Null deviance: 1149.74 on 938 degrees of freedom
## Residual deviance: 819.81 on 668 degrees of freedom
## AIC: 3145.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21820
## Std. Err.: 89240
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2601.71##
## Call:
## glm.nb(formula = smptbBM ~ poly(time, degree = 3), data = week,
## init.theta = 29.86636665, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8037 -0.5479 -0.3625 0.4480 3.4560
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.40427 0.02737 14.770 <2e-16 ***
## poly(time, degree = 3)1 0.15663 0.86018 0.182 0.8555
## poly(time, degree = 3)2 -1.88486 0.85379 -2.208 0.0273 *
## poly(time, degree = 3)3 1.87812 0.85241 2.203 0.0276 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(29.8664) family taken to be 1)
##
## Null deviance: 1102.6 on 938 degrees of freedom
## Residual deviance: 1093.0 on 935 degrees of freedom
## AIC: 2930.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 29.9
## Std. Err.: 29.4
##
## 2 x log-likelihood: -2920.45
For both ptbBM & smptbBM, the AIC increases with increasing df value. While this may indicate model with 0 long-term trend has the smallest AIC value, subsequent final model for this version (see results for SA1) show flat relationship for all variables & across all lags. This could suggest there is a need to adjust for long-term trend & seasonality. Despite the AIC results, it was decided to follow what has been used in previous TB studies & also recommended for pollution and mortality studies: natural cubic spline with 7df per year
Codes to create Figure 1 in main paper
####make time series plots for each climate var & also for ptb
library(ggplot2)
library(dplyr)
library(reshape2)
library(RColorBrewer)
mypal<-brewer.pal(11,"Paired")
names(week)## [1] "epiYr" "epiWk" "avgWindSp" "sun" "RF" "rainyD"
## [7] "minRH" "meanRH" "maxRH" "minT" "aveT" "maxT"
## [13] "AH" "eptb" "ptb" "allTB" "eptbInW" "ptbInW"
## [19] "allTBInW" "ptbBM" "smptbBM" "date" "time"figPtb <- ggplot(week, aes(x=date, y=ptbBM)) + geom_line() + xlab("") +
ylab("Weekly PTB cases") +
scale_x_date(limit=c(as.Date("2001-01-01"),as.Date("2018-12-31"))) +
theme(plot.margin = unit(c(0.2,0.3,0.3,0.3), "cm"),
axis.title.y = element_text(size=7),
axis.text=element_text(size=8))
figTemp<- week %>% select(minT:maxT,date)
head(figTemp)## # A tibble: 6 x 4
## minT aveT maxT date
## <dbl> <dbl> <dbl> <date>
## 1 23.2 26.6 30.1 2001-01-08
## 2 23.5 26.3 30.4 2001-01-15
## 3 23.2 26.7 31.3 2001-01-22
## 4 23.3 27.3 31.6 2001-01-29
## 5 23.6 26.7 30.6 2001-02-05
## 6 23.8 27.1 31.1 2001-02-12str(figTemp)## tibble [939 x 4] (S3: tbl_df/tbl/data.frame)
## $ minT: num [1:939] 23.2 23.5 23.2 23.3 23.6 ...
## $ aveT: num [1:939] 26.6 26.3 26.7 27.3 26.7 ...
## $ maxT: num [1:939] 30.1 30.4 31.3 31.6 30.6 ...
## $ date: Date[1:939], format: "2001-01-08" "2001-01-15" ...figTemp<- melt(figTemp,id.vars = "date")
fig1 <- ggplot(figTemp,aes(x=date,y=value,colour=variable,group=variable)) +
geom_line() + xlab("") + ylab("Temperature (°C)") +
scale_colour_manual(values = mypal[1:3],labels = c("Min Temp", "Average Temp", "Max Temp")) +
theme(legend.title = element_blank(), legend.position="bottom",
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-15,-15,-15,-15),
plot.margin = unit(c(0.1,0.3,0.5,0.3), "cm"),
legend.text = element_text(size = 6),
axis.title.y = element_text(size=7),
axis.text=element_text(size=8))
figHum<- week %>% select(minRH:maxRH,date)
head(figHum)## # A tibble: 6 x 4
## minRH meanRH maxRH date
## <dbl> <dbl> <dbl> <date>
## 1 68.3 84.6 96.7 2001-01-08
## 2 71 88.4 97.6 2001-01-15
## 3 63.9 85.4 97 2001-01-22
## 4 66 85.6 97.1 2001-01-29
## 5 68.6 86.9 96.9 2001-02-05
## 6 67.6 86 96.7 2001-02-12str(figHum)## tibble [939 x 4] (S3: tbl_df/tbl/data.frame)
## $ minRH : num [1:939] 68.3 71 63.9 66 68.6 ...
## $ meanRH: num [1:939] 84.6 88.4 85.4 85.6 86.9 ...
## $ maxRH : num [1:939] 96.7 97.6 97 97.1 96.9 ...
## $ date : Date[1:939], format: "2001-01-08" "2001-01-15" ...figHum<- melt(figHum,id.vars = "date")
fig2 <- ggplot(figHum,aes(x=date,y=value,colour=variable,group=variable)) +
geom_line() + xlab("") + ylab("Humidity (%)") +
scale_colour_manual(values = mypal[4:6],labels = c("Min RH", "Mean RH", "Max RH")) +
theme(legend.title = element_blank(), legend.position="bottom",
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-15,-15,-15,-15),
plot.margin = unit(c(0.1,0.3,0.5,0.3), "cm"),
legend.text = element_text(size = 6),
axis.title.y = element_text(size=7),
axis.text=element_text(size=8))
figRF<- week %>% select(RF,date)
head(figRF)## # A tibble: 6 x 2
## RF date
## <dbl> <date>
## 1 92.2 2001-01-08
## 2 218 2001-01-15
## 3 44.2 2001-01-22
## 4 224. 2001-01-29
## 5 170. 2001-02-05
## 6 33.0 2001-02-12str(figRF)## tibble [939 x 2] (S3: tbl_df/tbl/data.frame)
## $ RF : num [1:939] 92.2 218 44.1 224.3 170.2 ...
## $ date: Date[1:939], format: "2001-01-08" "2001-01-15" ...figRF<- melt(figRF,id.vars = "date")
fig3<- ggplot(figRF,aes(x=date,y=value,colour=variable,group=variable)) +
geom_line() + xlab("") + ylab("Total Rainfall (mm)") +
scale_colour_manual(values = mypal[7]) +
theme(legend.position = "none",
plot.margin = unit(c(0.2,0.3,0.3,0.3), "cm"),
axis.title.y = element_text(size=7),
axis.text=element_text(size=8))
figWind<- week %>% select(avgWindSp,date)
head(figWind)## # A tibble: 6 x 2
## avgWindSp date
## <dbl> <date>
## 1 4.37 2001-01-08
## 2 4.38 2001-01-15
## 3 3.93 2001-01-22
## 4 4.63 2001-01-29
## 5 4.96 2001-02-05
## 6 4.77 2001-02-12str(figWind)## tibble [939 x 2] (S3: tbl_df/tbl/data.frame)
## $ avgWindSp: num [1:939] 4.37 4.38 3.93 4.63 4.96 ...
## $ date : Date[1:939], format: "2001-01-08" "2001-01-15" ...figWind<- melt(figWind,id.vars = "date")
fig4<- ggplot(figWind,aes(x=date,y=value,colour=variable,group=variable)) +
geom_line() + xlab("") + ylab("Average Wind speed (knots)") +
scale_colour_manual(values = mypal[8]) +
theme(legend.position = "none",
plot.margin = unit(c(0.3,0.3,0.5,0.3), "cm"),
axis.title.y = element_text(size=7),
axis.text=element_text(size=8))
figOth<- week %>% select(sun,AH,date)
head(figOth)## # A tibble: 6 x 3
## sun AH date
## <dbl> <dbl> <date>
## 1 41 30.0 2001-01-08
## 2 35.4 30.8 2001-01-15
## 3 47.0 30.4 2001-01-22
## 4 64.4 31.6 2001-01-29
## 5 38.2 31.0 2001-02-05
## 6 45.8 31.5 2001-02-12str(figOth)## tibble [939 x 3] (S3: tbl_df/tbl/data.frame)
## $ sun : num [1:939] 41 35.4 47 64.4 38.2 ...
## $ AH : num [1:939] 30 30.8 30.4 31.6 31 ...
## $ date: Date[1:939], format: "2001-01-08" "2001-01-15" ...figOth<- melt(figOth,id.vars = "date")
fig5<- ggplot(figOth,aes(x=date,y=value,colour=variable,group=variable)) +
geom_line() + xlab("") + ylab("Sunshine (hours)/ VP (kPa)") +
scale_colour_manual(values = mypal[9:10],labels = c("Total sunshine","Vapour Pressure")) +
theme(legend.title = element_blank(), legend.position="bottom",
legend.margin = margin(0, 0, 0, 0),
legend.box.margin = margin(-15,-15,-15,-15),
plot.margin = unit(c(0.3,0.3,0.5,0.3), "cm"),
legend.text = element_text(size = 6),
axis.title.y = element_text(size=7),
axis.text=element_text(size=8))
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combinetiff("figFull_Apr10.tiff", units="in", width=7, height=4, res=400)
figFull<- grid.arrange(figPtb, fig3, fig2, fig1, fig4,fig5, nrow=3,ncol=2)
dev.off()## png
## 2Creating crossbasis terms for climate variates
##for argvar, if no function is mentioned, the default is ns (perhaps due to df=3 mentioned)
##for arglag, not specifying the df for ns function means not giving it knots
cb1.avgWindSp <- crossbasis(week$avgWindSp,lag=52,argvar=list(df=3), arglag=list(fun="ns"))
summary(cb1.avgWindSp)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 2.964286 to 10.17262
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 4.207341 4.755952
## intercept: FALSE
## Boundary.knots: 2.964286 10.17262
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb2.sun <- crossbasis(week$sun,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb2.sun)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 11.15 to 76
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 45.78333 55.71667
## intercept: FALSE
## Boundary.knots: 11.15 76
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb3.RF <- crossbasis(week$RF,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb3.RF)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 0 to 414.5
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 25.91667 72.25
## intercept: FALSE
## Boundary.knots: 0 414.5
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52##Number of Rainy days was included in the initial analysis, but later excluded due to the nature of variable (count, not continuous)
##that's why cb4 was removed in this coding script.
cb5.minRH <- crossbasis(week$minRH,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb5.minRH)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 45 to 80.57143
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 60.80952 64.71429
## intercept: FALSE
## Boundary.knots: 45 80.57143
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb6.meanRH <- crossbasis(week$meanRH,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb6.meanRH)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 70 to 92.14286
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 81.28571 84.14286
## intercept: FALSE
## Boundary.knots: 70 92.14286
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb7.maxRH <- crossbasis(week$maxRH,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb7.maxRH)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 87.71429 to 100
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 95.85714 97.57143
## intercept: FALSE
## Boundary.knots: 87.71429 100
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb8.AH <- crossbasis(week$AH,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb8.AH)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 26.39459 to 35.1129
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 30.72354 31.72263
## intercept: FALSE
## Boundary.knots: 26.39459 35.1129
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb9.minT <- crossbasis(week$minT,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb9.minT)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 21.84286 to 26.32857
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 23.65714 24.24286
## intercept: FALSE
## Boundary.knots: 21.84286 26.32857
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb10.aveT <- crossbasis(week$aveT,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb10.aveT)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 24.51429 to 30.01429
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 27.32857 27.92857
## intercept: FALSE
## Boundary.knots: 24.51429 30.01429
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52cb11.maxT <- crossbasis(week$maxT,lag=52,argvar=list(df=3), arglag=list(fun="ns") )
summary(cb11.maxT)## CROSSBASIS FUNCTIONS
## observations: 939
## range: 27.12857 to 35.45714
## lag period: 0 52
## total df: 6
##
## BASIS FOR VAR:
## fun: ns
## knots: 31.78571 32.51429
## intercept: FALSE
## Boundary.knots: 27.12857 35.45714
##
## BASIS FOR LAG:
## fun: ns
## knots:
## intercept: TRUE
## Boundary.knots: 0 52Analysis fot ptbBM with ns7 for long-term trend
##ptbBM = Weekly PTB case counts from Brunei-Muara district
sp_ptbBM <-ns(week$time,df=18*7)
attributes(sp_ptbBM)## $dim
## [1] 939 126
##
## $dimnames
## $dimnames[[1]]
## NULL
##
## $dimnames[[2]]
## [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12"
## [13] "13" "14" "15" "16" "17" "18" "19" "20" "21" "22" "23" "24"
## [25] "25" "26" "27" "28" "29" "30" "31" "32" "33" "34" "35" "36"
## [37] "37" "38" "39" "40" "41" "42" "43" "44" "45" "46" "47" "48"
## [49] "49" "50" "51" "52" "53" "54" "55" "56" "57" "58" "59" "60"
## [61] "61" "62" "63" "64" "65" "66" "67" "68" "69" "70" "71" "72"
## [73] "73" "74" "75" "76" "77" "78" "79" "80" "81" "82" "83" "84"
## [85] "85" "86" "87" "88" "89" "90" "91" "92" "93" "94" "95" "96"
## [97] "97" "98" "99" "100" "101" "102" "103" "104" "105" "106" "107" "108"
## [109] "109" "110" "111" "112" "113" "114" "115" "116" "117" "118" "119" "120"
## [121] "121" "122" "123" "124" "125" "126"
##
##
## $degree
## [1] 3
##
## $knots
## 0.7936508% 1.5873016% 2.3809524% 3.1746032% 3.9682540% 4.7619048%
## 8.444444 15.888889 23.333333 30.777778 38.222222 45.666667
## 5.5555556% 6.3492063% 7.1428571% 7.9365079% 8.7301587% 9.5238095%
## 53.111111 60.555556 68.000000 75.444444 82.888889 90.333333
## 10.3174603% 11.1111111% 11.9047619% 12.6984127% 13.4920635% 14.2857143%
## 97.777778 105.222222 112.666667 120.111111 127.555556 135.000000
## 15.0793651% 15.8730159% 16.6666667% 17.4603175% 18.2539683% 19.0476190%
## 142.444444 149.888889 157.333333 164.777778 172.222222 179.666667
## 19.8412698% 20.6349206% 21.4285714% 22.2222222% 23.0158730% 23.8095238%
## 187.111111 194.555556 202.000000 209.444444 216.888889 224.333333
## 24.6031746% 25.3968254% 26.1904762% 26.9841270% 27.7777778% 28.5714286%
## 231.777778 239.222222 246.666667 254.111111 261.555556 269.000000
## 29.3650794% 30.1587302% 30.9523810% 31.7460317% 32.5396825% 33.3333333%
## 276.444444 283.888889 291.333333 298.777778 306.222222 313.666667
## 34.1269841% 34.9206349% 35.7142857% 36.5079365% 37.3015873% 38.0952381%
## 321.111111 328.555556 336.000000 343.444444 350.888889 358.333333
## 38.8888889% 39.6825397% 40.4761905% 41.2698413% 42.0634921% 42.8571429%
## 365.777778 373.222222 380.666667 388.111111 395.555556 403.000000
## 43.6507937% 44.4444444% 45.2380952% 46.0317460% 46.8253968% 47.6190476%
## 410.444444 417.888889 425.333333 432.777778 440.222222 447.666667
## 48.4126984% 49.2063492% 50.0000000% 50.7936508% 51.5873016% 52.3809524%
## 455.111111 462.555556 470.000000 477.444444 484.888889 492.333333
## 53.1746032% 53.9682540% 54.7619048% 55.5555556% 56.3492063% 57.1428571%
## 499.777778 507.222222 514.666667 522.111111 529.555556 537.000000
## 57.9365079% 58.7301587% 59.5238095% 60.3174603% 61.1111111% 61.9047619%
## 544.444444 551.888889 559.333333 566.777778 574.222222 581.666667
## 62.6984127% 63.4920635% 64.2857143% 65.0793651% 65.8730159% 66.6666667%
## 589.111111 596.555556 604.000000 611.444444 618.888889 626.333333
## 67.4603175% 68.2539683% 69.0476190% 69.8412698% 70.6349206% 71.4285714%
## 633.777778 641.222222 648.666667 656.111111 663.555556 671.000000
## 72.2222222% 73.0158730% 73.8095238% 74.6031746% 75.3968254% 76.1904762%
## 678.444444 685.888889 693.333333 700.777778 708.222222 715.666667
## 76.9841270% 77.7777778% 78.5714286% 79.3650794% 80.1587302% 80.9523810%
## 723.111111 730.555556 738.000000 745.444444 752.888889 760.333333
## 81.7460317% 82.5396825% 83.3333333% 84.1269841% 84.9206349% 85.7142857%
## 767.777778 775.222222 782.666667 790.111111 797.555556 805.000000
## 86.5079365% 87.3015873% 88.0952381% 88.8888889% 89.6825397% 90.4761905%
## 812.444444 819.888889 827.333333 834.777778 842.222222 849.666667
## 91.2698413% 92.0634921% 92.8571429% 93.6507937% 94.4444444% 95.2380952%
## 857.111111 864.555556 872.000000 879.444444 886.888889 894.333333
## 96.0317460% 96.8253968% 97.6190476% 98.4126984% 99.2063492%
## 901.777778 909.222222 916.666667 924.111111 931.555556
##
## $Boundary.knots
## [1] 1 939
##
## $intercept
## [1] FALSE
##
## $class
## [1] "ns" "basis" "matrix"options(na.action="na.exclude")
m1a <- glm.nb(ptbBM ~ cb1.avgWindSp + sp_ptbBM,data=week); summary(m1a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb1.avgWindSp + sp_ptbBM, data = week,
## init.theta = 22883.16985, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4315 -0.7819 -0.1192 0.5577 2.6021
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.352e+01 6.567e+00 -2.059 0.03946 *
## cb1.avgWindSpv1.l1 2.040e-01 3.072e-01 0.664 0.50672
## cb1.avgWindSpv1.l2 2.983e-01 2.204e-01 1.354 0.17586
## cb1.avgWindSpv2.l1 1.739e+00 6.882e-01 2.526 0.01153 *
## cb1.avgWindSpv2.l2 2.022e-01 4.649e-01 0.435 0.66358
## cb1.avgWindSpv3.l1 1.454e+00 7.644e-01 1.902 0.05717 .
## cb1.avgWindSpv3.l2 -1.978e-01 4.615e-01 -0.428 0.66832
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.993e+06 2.163e+06 -0.921 0.35687
## sp_ptbBM5 2.125e+01 1.220e+01 1.741 0.08172 .
## sp_ptbBM6 -4.737e+00 2.266e+00 -2.090 0.03660 *
## sp_ptbBM7 -1.335e+00 1.843e+00 -0.725 0.46870
## sp_ptbBM8 -4.039e+00 1.954e+00 -2.067 0.03873 *
## sp_ptbBM9 -2.134e+00 1.724e+00 -1.238 0.21578
## sp_ptbBM10 -5.283e+00 1.688e+00 -3.131 0.00174 **
## sp_ptbBM11 -2.065e+00 1.550e+00 -1.332 0.18288
## sp_ptbBM12 -2.974e+00 1.425e+00 -2.087 0.03689 *
## sp_ptbBM13 -1.487e+00 1.601e+00 -0.929 0.35313
## sp_ptbBM14 -2.700e+00 1.397e+00 -1.933 0.05328 .
## sp_ptbBM15 -1.506e-01 9.556e-01 -0.158 0.87480
## sp_ptbBM16 -1.695e+00 9.470e-01 -1.790 0.07347 .
## sp_ptbBM17 -1.015e+00 8.812e-01 -1.152 0.24935
## sp_ptbBM18 -9.394e-01 9.895e-01 -0.949 0.34246
## sp_ptbBM19 -8.421e-01 9.378e-01 -0.898 0.36920
## sp_ptbBM20 -1.356e+00 1.016e+00 -1.335 0.18182
## sp_ptbBM21 1.104e-02 1.276e+00 0.009 0.99310
## sp_ptbBM22 -8.949e-02 9.857e-01 -0.091 0.92766
## sp_ptbBM23 -1.739e+00 9.963e-01 -1.746 0.08087 .
## sp_ptbBM24 -9.344e-01 1.043e+00 -0.896 0.37049
## sp_ptbBM25 -8.940e-01 8.851e-01 -1.010 0.31250
## sp_ptbBM26 -2.970e-01 9.231e-01 -0.322 0.74766
## sp_ptbBM27 -2.462e+00 1.177e+00 -2.092 0.03640 *
## sp_ptbBM28 -3.098e-01 1.162e+00 -0.267 0.78974
## sp_ptbBM29 -1.148e-01 1.272e+00 -0.090 0.92808
## sp_ptbBM30 -1.298e+00 9.385e-01 -1.383 0.16675
## sp_ptbBM31 -8.907e-02 9.479e-01 -0.094 0.92514
## sp_ptbBM32 -1.565e+00 9.564e-01 -1.637 0.10171
## sp_ptbBM33 -1.732e+00 1.056e+00 -1.640 0.10109
## sp_ptbBM34 -7.037e-01 8.818e-01 -0.798 0.42482
## sp_ptbBM35 -7.154e-01 8.670e-01 -0.825 0.40927
## sp_ptbBM36 1.668e+00 8.668e-01 1.924 0.05438 .
## sp_ptbBM37 -1.353e+00 9.719e-01 -1.392 0.16387
## sp_ptbBM38 -5.412e-02 1.022e+00 -0.053 0.95779
## sp_ptbBM39 -1.809e+00 1.243e+00 -1.456 0.14549
## sp_ptbBM40 -3.711e-01 1.099e+00 -0.338 0.73570
## sp_ptbBM41 -2.258e+00 1.419e+00 -1.592 0.11142
## sp_ptbBM42 -2.682e+00 1.513e+00 -1.773 0.07621 .
## sp_ptbBM43 -2.095e+00 1.626e+00 -1.288 0.19775
## sp_ptbBM44 -2.178e+00 1.627e+00 -1.339 0.18071
## sp_ptbBM45 -1.583e+00 1.604e+00 -0.987 0.32364
## sp_ptbBM46 -2.581e+00 1.637e+00 -1.576 0.11502
## sp_ptbBM47 -2.156e+00 1.788e+00 -1.206 0.22789
## sp_ptbBM48 -2.618e+00 1.926e+00 -1.359 0.17407
## sp_ptbBM49 -1.993e+00 1.838e+00 -1.084 0.27825
## sp_ptbBM50 -3.265e+00 1.704e+00 -1.916 0.05531 .
## sp_ptbBM51 -1.229e+00 1.472e+00 -0.835 0.40375
## sp_ptbBM52 -2.941e+00 1.300e+00 -2.261 0.02375 *
## sp_ptbBM53 -1.523e+00 1.226e+00 -1.242 0.21432
## sp_ptbBM54 -1.990e+00 1.246e+00 -1.598 0.11014
## sp_ptbBM55 -1.843e+00 1.104e+00 -1.669 0.09502 .
## sp_ptbBM56 -9.141e-01 1.155e+00 -0.792 0.42860
## sp_ptbBM57 -9.811e-01 1.054e+00 -0.931 0.35187
## sp_ptbBM58 -8.931e-01 1.017e+00 -0.879 0.37963
## sp_ptbBM59 -2.125e+00 1.038e+00 -2.047 0.04063 *
## sp_ptbBM60 -1.884e+00 1.189e+00 -1.585 0.11302
## sp_ptbBM61 -8.797e-01 1.002e+00 -0.878 0.37993
## sp_ptbBM62 -1.514e+00 1.255e+00 -1.206 0.22778
## sp_ptbBM63 -1.540e+00 1.419e+00 -1.085 0.27783
## sp_ptbBM64 -9.056e-01 1.666e+00 -0.544 0.58676
## sp_ptbBM65 -1.478e+00 1.668e+00 -0.886 0.37557
## sp_ptbBM66 -2.644e+00 2.004e+00 -1.319 0.18722
## sp_ptbBM67 -1.614e+00 1.973e+00 -0.818 0.41340
## sp_ptbBM68 -2.543e+00 2.169e+00 -1.173 0.24090
## sp_ptbBM69 -2.303e+00 2.114e+00 -1.089 0.27612
## sp_ptbBM70 -3.033e+00 1.963e+00 -1.545 0.12232
## sp_ptbBM71 -2.777e+00 1.982e+00 -1.401 0.16122
## sp_ptbBM72 -2.920e+00 1.918e+00 -1.522 0.12802
## sp_ptbBM73 -3.166e+00 1.818e+00 -1.742 0.08152 .
## sp_ptbBM74 -1.671e+00 1.744e+00 -0.958 0.33788
## sp_ptbBM75 -2.500e+00 1.702e+00 -1.469 0.14179
## sp_ptbBM76 -1.921e+00 1.725e+00 -1.114 0.26537
## sp_ptbBM77 -2.413e+00 1.790e+00 -1.348 0.17777
## sp_ptbBM78 -1.475e+00 1.768e+00 -0.834 0.40424
## sp_ptbBM79 -1.778e+00 1.691e+00 -1.052 0.29296
## sp_ptbBM80 -2.617e+00 1.791e+00 -1.461 0.14390
## sp_ptbBM81 -1.927e+00 1.786e+00 -1.079 0.28072
## sp_ptbBM82 -2.413e+00 1.817e+00 -1.328 0.18417
## sp_ptbBM83 -2.445e+00 1.773e+00 -1.379 0.16799
## sp_ptbBM84 -2.152e+00 1.994e+00 -1.079 0.28045
## sp_ptbBM85 -3.614e+00 2.016e+00 -1.793 0.07297 .
## sp_ptbBM86 -1.785e+00 1.862e+00 -0.958 0.33783
## sp_ptbBM87 -2.841e+00 1.839e+00 -1.544 0.12248
## sp_ptbBM88 -2.556e+00 1.797e+00 -1.423 0.15486
## sp_ptbBM89 -3.488e+00 1.902e+00 -1.833 0.06674 .
## sp_ptbBM90 -2.353e+00 1.865e+00 -1.262 0.20694
## sp_ptbBM91 -2.679e+00 1.809e+00 -1.481 0.13871
## sp_ptbBM92 -1.612e+00 1.923e+00 -0.838 0.40204
## sp_ptbBM93 -2.376e+00 1.861e+00 -1.277 0.20175
## sp_ptbBM94 -3.240e+00 1.795e+00 -1.805 0.07108 .
## sp_ptbBM95 -2.588e+00 1.804e+00 -1.434 0.15151
## sp_ptbBM96 -2.101e+00 1.729e+00 -1.215 0.22430
## sp_ptbBM97 -2.412e+00 1.773e+00 -1.361 0.17360
## sp_ptbBM98 -3.342e+00 1.928e+00 -1.733 0.08306 .
## sp_ptbBM99 -1.795e+00 1.894e+00 -0.948 0.34330
## sp_ptbBM100 -2.501e+00 1.985e+00 -1.260 0.20753
## sp_ptbBM101 -1.794e+00 1.837e+00 -0.977 0.32862
## sp_ptbBM102 -1.714e+00 1.541e+00 -1.112 0.26602
## sp_ptbBM103 -1.673e+00 1.303e+00 -1.284 0.19902
## sp_ptbBM104 -1.233e+00 1.202e+00 -1.026 0.30487
## sp_ptbBM105 -1.375e+00 1.193e+00 -1.153 0.24907
## sp_ptbBM106 -2.607e+00 1.588e+00 -1.642 0.10056
## sp_ptbBM107 -1.408e+00 1.263e+00 -1.115 0.26472
## sp_ptbBM108 -9.474e-01 1.201e+00 -0.789 0.43009
## sp_ptbBM109 -1.924e+00 1.248e+00 -1.542 0.12306
## sp_ptbBM110 -1.562e+00 1.318e+00 -1.184 0.23622
## sp_ptbBM111 -2.310e+00 1.432e+00 -1.613 0.10680
## sp_ptbBM112 -1.653e+00 1.090e+00 -1.516 0.12953
## sp_ptbBM113 4.095e-01 8.014e-01 0.511 0.60936
## sp_ptbBM114 3.037e-01 7.086e-01 0.429 0.66823
## sp_ptbBM115 -1.655e+00 7.805e-01 -2.121 0.03396 *
## sp_ptbBM116 1.566e+00 6.448e-01 2.429 0.01513 *
## sp_ptbBM117 -1.165e+00 7.587e-01 -1.535 0.12483
## sp_ptbBM118 1.927e+00 7.181e-01 2.683 0.00730 **
## sp_ptbBM119 -1.481e+00 8.511e-01 -1.740 0.08182 .
## sp_ptbBM120 1.594e+00 8.888e-01 1.793 0.07298 .
## sp_ptbBM121 -4.082e-01 6.985e-01 -0.584 0.55899
## sp_ptbBM122 7.269e-01 6.779e-01 1.072 0.28363
## sp_ptbBM123 7.357e-02 5.971e-01 0.123 0.90194
## sp_ptbBM124 8.271e-01 7.618e-01 1.086 0.27759
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22883.17) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 937.56 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3228.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22883
## Std. Err.: 146249
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2970.42m2a <- glm.nb(ptbBM ~ cb2.sun + sp_ptbBM,data=week); summary(m2a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb2.sun + sp_ptbBM, data = week, init.theta = 22834.64112,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3950 -0.7776 -0.1304 0.5625 2.5851
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.546e+00 9.030e+00 -0.946 0.34393
## cb2.sunv1.l1 2.457e-01 1.946e-01 1.263 0.20665
## cb2.sunv1.l2 -3.919e-02 1.398e-01 -0.280 0.77914
## cb2.sunv2.l1 8.866e-01 7.914e-01 1.120 0.26258
## cb2.sunv2.l2 4.458e-01 5.431e-01 0.821 0.41176
## cb2.sunv3.l1 6.421e-01 3.004e-01 2.138 0.03252 *
## cb2.sunv3.l2 3.638e-01 2.067e-01 1.760 0.07835 .
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.457e+06 2.237e+06 -0.651 0.51483
## sp_ptbBM5 1.637e+01 1.203e+01 1.360 0.17381
## sp_ptbBM6 -2.509e+00 1.994e+00 -1.258 0.20829
## sp_ptbBM7 1.398e+00 1.056e+00 1.324 0.18551
## sp_ptbBM8 -1.246e+00 9.158e-01 -1.361 0.17351
## sp_ptbBM9 5.623e-01 8.071e-01 0.697 0.48594
## sp_ptbBM10 -3.464e+00 1.064e+00 -3.255 0.00113 **
## sp_ptbBM11 5.633e-01 7.594e-01 0.742 0.45826
## sp_ptbBM12 -1.134e+00 8.704e-01 -1.303 0.19265
## sp_ptbBM13 -7.862e-02 8.713e-01 -0.090 0.92810
## sp_ptbBM14 -1.852e+00 9.216e-01 -2.009 0.04453 *
## sp_ptbBM15 -7.575e-01 8.831e-01 -0.858 0.39106
## sp_ptbBM16 -1.463e+00 8.951e-01 -1.634 0.10220
## sp_ptbBM17 -1.044e+00 8.743e-01 -1.195 0.23227
## sp_ptbBM18 -1.006e+00 8.871e-01 -1.134 0.25667
## sp_ptbBM19 -1.196e+00 8.992e-01 -1.330 0.18353
## sp_ptbBM20 -6.762e-01 8.443e-01 -0.801 0.42323
## sp_ptbBM21 -2.532e-01 9.144e-01 -0.277 0.78190
## sp_ptbBM22 3.135e-01 8.292e-01 0.378 0.70540
## sp_ptbBM23 -8.043e-01 9.396e-01 -0.856 0.39203
## sp_ptbBM24 3.883e-02 8.417e-01 0.046 0.96320
## sp_ptbBM25 -3.668e-01 8.824e-01 -0.416 0.67763
## sp_ptbBM26 -3.520e-01 9.480e-01 -0.371 0.71040
## sp_ptbBM27 -1.912e+00 1.114e+00 -1.716 0.08615 .
## sp_ptbBM28 -1.796e+00 1.003e+00 -1.791 0.07333 .
## sp_ptbBM29 -7.802e-01 9.680e-01 -0.806 0.42024
## sp_ptbBM30 -1.072e+00 9.040e-01 -1.186 0.23548
## sp_ptbBM31 -2.243e-01 9.020e-01 -0.249 0.80357
## sp_ptbBM32 -1.667e+00 9.940e-01 -1.677 0.09348 .
## sp_ptbBM33 -1.202e+00 9.786e-01 -1.228 0.21937
## sp_ptbBM34 -5.696e-02 9.585e-01 -0.059 0.95261
## sp_ptbBM35 -1.568e-01 9.818e-01 -0.160 0.87315
## sp_ptbBM36 1.475e+00 9.497e-01 1.553 0.12035
## sp_ptbBM37 -9.219e-02 9.471e-01 -0.097 0.92246
## sp_ptbBM38 1.435e+00 9.198e-01 1.560 0.11881
## sp_ptbBM39 6.036e-02 9.284e-01 0.065 0.94816
## sp_ptbBM40 1.603e+00 1.009e+00 1.589 0.11202
## sp_ptbBM41 1.325e-01 9.037e-01 0.147 0.88343
## sp_ptbBM42 -1.545e-01 1.040e+00 -0.149 0.88184
## sp_ptbBM43 5.178e-02 8.486e-01 0.061 0.95134
## sp_ptbBM44 -1.632e-01 7.695e-01 -0.212 0.83200
## sp_ptbBM45 2.200e-01 7.278e-01 0.302 0.76247
## sp_ptbBM46 -1.177e+00 7.989e-01 -1.474 0.14049
## sp_ptbBM47 -6.994e-01 8.749e-01 -0.799 0.42405
## sp_ptbBM48 -3.204e-01 7.665e-01 -0.418 0.67595
## sp_ptbBM49 -6.950e-01 1.009e+00 -0.689 0.49110
## sp_ptbBM50 -6.352e-01 8.211e-01 -0.774 0.43913
## sp_ptbBM51 7.741e-01 8.400e-01 0.922 0.35676
## sp_ptbBM52 -6.493e-01 7.781e-01 -0.834 0.40403
## sp_ptbBM53 5.614e-01 7.589e-01 0.740 0.45951
## sp_ptbBM54 -4.564e-01 7.692e-01 -0.593 0.55301
## sp_ptbBM55 -6.105e-01 8.041e-01 -0.759 0.44775
## sp_ptbBM56 1.619e-01 9.410e-01 0.172 0.86342
## sp_ptbBM57 -8.778e-02 9.089e-01 -0.097 0.92306
## sp_ptbBM58 -2.205e-01 8.368e-01 -0.264 0.79213
## sp_ptbBM59 -5.513e-01 8.648e-01 -0.637 0.52381
## sp_ptbBM60 -4.463e-01 8.151e-01 -0.548 0.58400
## sp_ptbBM61 6.038e-02 8.739e-01 0.069 0.94492
## sp_ptbBM62 -7.509e-01 8.883e-01 -0.845 0.39796
## sp_ptbBM63 -1.652e+00 1.004e+00 -1.646 0.09983 .
## sp_ptbBM64 -9.097e-01 9.166e-01 -0.993 0.32093
## sp_ptbBM65 -3.562e-01 8.306e-01 -0.429 0.66807
## sp_ptbBM66 -1.210e+00 7.892e-01 -1.533 0.12535
## sp_ptbBM67 -6.703e-02 7.437e-01 -0.090 0.92819
## sp_ptbBM68 -7.037e-01 7.752e-01 -0.908 0.36399
## sp_ptbBM69 -4.101e-01 8.359e-01 -0.491 0.62374
## sp_ptbBM70 -1.026e+00 1.072e+00 -0.957 0.33873
## sp_ptbBM71 3.612e-01 1.025e+00 0.352 0.72451
## sp_ptbBM72 1.082e-02 1.025e+00 0.011 0.99158
## sp_ptbBM73 -1.701e-01 9.775e-01 -0.174 0.86183
## sp_ptbBM74 1.184e+00 9.730e-01 1.217 0.22352
## sp_ptbBM75 2.544e-01 9.061e-01 0.281 0.77887
## sp_ptbBM76 8.757e-01 9.301e-01 0.942 0.34643
## sp_ptbBM77 4.288e-01 9.377e-01 0.457 0.64747
## sp_ptbBM78 6.772e-01 8.961e-01 0.756 0.44982
## sp_ptbBM79 4.560e-01 8.409e-01 0.542 0.58758
## sp_ptbBM80 -4.086e-01 9.208e-01 -0.444 0.65724
## sp_ptbBM81 -4.644e-01 9.277e-01 -0.501 0.61665
## sp_ptbBM82 -4.128e-01 8.162e-01 -0.506 0.61299
## sp_ptbBM83 -9.705e-01 8.609e-01 -1.127 0.25959
## sp_ptbBM84 -6.024e-01 8.662e-01 -0.695 0.48682
## sp_ptbBM85 -2.166e+00 9.572e-01 -2.263 0.02362 *
## sp_ptbBM86 -2.240e-01 9.238e-01 -0.242 0.80844
## sp_ptbBM87 -2.014e-01 8.669e-01 -0.232 0.81630
## sp_ptbBM88 1.377e-01 9.314e-01 0.148 0.88244
## sp_ptbBM89 -9.040e-01 1.044e+00 -0.866 0.38653
## sp_ptbBM90 6.946e-01 1.012e+00 0.686 0.49252
## sp_ptbBM91 -1.368e-02 1.071e+00 -0.013 0.98981
## sp_ptbBM92 9.233e-01 8.623e-01 1.071 0.28427
## sp_ptbBM93 3.977e-01 8.857e-01 0.449 0.65338
## sp_ptbBM94 -3.136e-01 9.116e-01 -0.344 0.73084
## sp_ptbBM95 4.179e-01 8.186e-01 0.511 0.60964
## sp_ptbBM96 1.044e+00 8.015e-01 1.303 0.19270
## sp_ptbBM97 6.812e-01 8.018e-01 0.850 0.39559
## sp_ptbBM98 -1.207e-01 1.121e+00 -0.108 0.91427
## sp_ptbBM99 8.321e-01 8.213e-01 1.013 0.31099
## sp_ptbBM100 1.848e-01 8.628e-01 0.214 0.83037
## sp_ptbBM101 4.602e-01 7.562e-01 0.609 0.54281
## sp_ptbBM102 1.340e-01 7.808e-01 0.172 0.86371
## sp_ptbBM103 -1.872e-01 8.044e-01 -0.233 0.81596
## sp_ptbBM104 -2.805e-01 7.641e-01 -0.367 0.71354
## sp_ptbBM105 -1.058e+00 1.041e+00 -1.017 0.30933
## sp_ptbBM106 -1.323e+00 8.701e-01 -1.521 0.12834
## sp_ptbBM107 -1.375e+00 9.437e-01 -1.457 0.14502
## sp_ptbBM108 -1.208e+00 9.779e-01 -1.235 0.21689
## sp_ptbBM109 -1.722e+00 9.437e-01 -1.825 0.06797 .
## sp_ptbBM110 -8.229e-01 1.071e+00 -0.769 0.44215
## sp_ptbBM111 -1.224e+00 9.663e-01 -1.267 0.20512
## sp_ptbBM112 -1.357e-01 8.136e-01 -0.167 0.86754
## sp_ptbBM113 7.432e-01 8.201e-01 0.906 0.36481
## sp_ptbBM114 9.927e-01 7.565e-01 1.312 0.18944
## sp_ptbBM115 -5.699e-01 8.797e-01 -0.648 0.51705
## sp_ptbBM116 2.203e+00 7.393e-01 2.980 0.00288 **
## sp_ptbBM117 -1.338e+00 8.911e-01 -1.502 0.13317
## sp_ptbBM118 2.195e+00 8.312e-01 2.640 0.00829 **
## sp_ptbBM119 -1.257e+00 1.040e+00 -1.208 0.22707
## sp_ptbBM120 1.083e+00 8.267e-01 1.310 0.19008
## sp_ptbBM121 -2.234e-01 7.989e-01 -0.280 0.77979
## sp_ptbBM122 9.936e-01 7.035e-01 1.412 0.15787
## sp_ptbBM123 1.153e+00 6.239e-01 1.849 0.06450 .
## sp_ptbBM124 9.738e-01 7.797e-01 1.249 0.21168
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22834.64) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 936.71 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3227.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22835
## Std. Err.: 145868
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2969.576m3a <- glm.nb(ptbBM ~ cb3.RF + sp_ptbBM,data=week); summary(m3a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + sp_ptbBM, data = week, init.theta = 21990.89142,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4107 -0.7737 -0.1194 0.5399 2.5834
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.752e+00 2.639e+00 1.422 0.15504
## cb3.RFv1.l1 1.915e-01 2.570e-01 0.745 0.45630
## cb3.RFv1.l2 1.224e-01 1.783e-01 0.687 0.49217
## cb3.RFv2.l1 -5.456e-01 3.757e-01 -1.452 0.14643
## cb3.RFv2.l2 5.501e-03 2.728e-01 0.020 0.98391
## cb3.RFv3.l1 -6.110e-01 6.007e-01 -1.017 0.30914
## cb3.RFv3.l2 -1.987e-01 4.648e-01 -0.428 0.66895
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.616e+06 2.229e+06 -0.725 0.46848
## sp_ptbBM5 1.612e+01 1.190e+01 1.355 0.17544
## sp_ptbBM6 -2.049e+00 1.982e+00 -1.033 0.30138
## sp_ptbBM7 9.356e-01 1.093e+00 0.856 0.39192
## sp_ptbBM8 -1.422e+00 9.968e-01 -1.426 0.15373
## sp_ptbBM9 9.562e-01 8.875e-01 1.077 0.28129
## sp_ptbBM10 -2.895e+00 1.142e+00 -2.534 0.01126 *
## sp_ptbBM11 7.994e-01 7.989e-01 1.001 0.31702
## sp_ptbBM12 -3.897e-01 8.838e-01 -0.441 0.65921
## sp_ptbBM13 1.382e+00 9.198e-01 1.502 0.13311
## sp_ptbBM14 -1.047e+00 9.248e-01 -1.132 0.25760
## sp_ptbBM15 5.983e-01 8.226e-01 0.727 0.46703
## sp_ptbBM16 -6.894e-01 9.636e-01 -0.715 0.47435
## sp_ptbBM17 3.788e-01 9.024e-01 0.420 0.67463
## sp_ptbBM18 -7.112e-02 8.988e-01 -0.079 0.93693
## sp_ptbBM19 1.107e-01 8.694e-01 0.127 0.89864
## sp_ptbBM20 4.349e-01 1.059e+00 0.411 0.68119
## sp_ptbBM21 7.659e-01 8.498e-01 0.901 0.36745
## sp_ptbBM22 8.331e-01 8.473e-01 0.983 0.32551
## sp_ptbBM23 -3.778e-01 8.960e-01 -0.422 0.67327
## sp_ptbBM24 4.352e-01 9.275e-01 0.469 0.63892
## sp_ptbBM25 2.050e-01 9.075e-01 0.226 0.82126
## sp_ptbBM26 8.037e-01 9.607e-01 0.837 0.40282
## sp_ptbBM27 -8.948e-01 1.155e+00 -0.775 0.43859
## sp_ptbBM28 -6.954e-01 1.052e+00 -0.661 0.50849
## sp_ptbBM29 5.247e-01 9.381e-01 0.559 0.57597
## sp_ptbBM30 -3.687e-01 9.401e-01 -0.392 0.69495
## sp_ptbBM31 8.586e-01 7.953e-01 1.080 0.28034
## sp_ptbBM32 -6.513e-01 8.960e-01 -0.727 0.46733
## sp_ptbBM33 -4.491e-01 9.428e-01 -0.476 0.63383
## sp_ptbBM34 4.711e-01 9.043e-01 0.521 0.60243
## sp_ptbBM35 9.325e-02 8.363e-01 0.112 0.91122
## sp_ptbBM36 1.127e+00 7.265e-01 1.551 0.12084
## sp_ptbBM37 1.013e-02 8.767e-01 0.012 0.99078
## sp_ptbBM38 5.836e-01 7.909e-01 0.738 0.46063
## sp_ptbBM39 -2.152e-01 8.350e-01 -0.258 0.79666
## sp_ptbBM40 5.584e-01 7.986e-01 0.699 0.48443
## sp_ptbBM41 1.921e-01 8.673e-01 0.221 0.82475
## sp_ptbBM42 -3.785e-01 9.546e-01 -0.396 0.69175
## sp_ptbBM43 1.249e-01 8.631e-01 0.145 0.88491
## sp_ptbBM44 4.531e-01 8.361e-01 0.542 0.58785
## sp_ptbBM45 9.719e-01 8.323e-01 1.168 0.24293
## sp_ptbBM46 -9.464e-02 8.405e-01 -0.113 0.91034
## sp_ptbBM47 6.331e-01 7.461e-01 0.848 0.39618
## sp_ptbBM48 3.450e-01 7.933e-01 0.435 0.66361
## sp_ptbBM49 1.191e+00 8.410e-01 1.417 0.15654
## sp_ptbBM50 -8.129e-02 8.628e-01 -0.094 0.92494
## sp_ptbBM51 1.840e+00 7.873e-01 2.337 0.01944 *
## sp_ptbBM52 1.842e-01 8.503e-01 0.217 0.82851
## sp_ptbBM53 8.987e-01 8.019e-01 1.121 0.26238
## sp_ptbBM54 5.042e-01 8.455e-01 0.596 0.55100
## sp_ptbBM55 1.710e-01 8.666e-01 0.197 0.84358
## sp_ptbBM56 9.194e-01 1.221e+00 0.753 0.45149
## sp_ptbBM57 6.278e-01 9.497e-01 0.661 0.50862
## sp_ptbBM58 2.778e-01 9.446e-01 0.294 0.76870
## sp_ptbBM59 -4.618e-01 9.132e-01 -0.506 0.61305
## sp_ptbBM60 -3.055e-02 9.455e-01 -0.032 0.97423
## sp_ptbBM61 4.372e-01 8.599e-01 0.508 0.61113
## sp_ptbBM62 5.010e-01 1.094e+00 0.458 0.64714
## sp_ptbBM63 -3.810e-01 1.238e+00 -0.308 0.75825
## sp_ptbBM64 6.193e-01 1.072e+00 0.578 0.56360
## sp_ptbBM65 7.559e-01 9.347e-01 0.809 0.41867
## sp_ptbBM66 4.820e-01 8.706e-01 0.554 0.57986
## sp_ptbBM67 1.596e+00 9.200e-01 1.735 0.08275 .
## sp_ptbBM68 8.460e-01 7.797e-01 1.085 0.27793
## sp_ptbBM69 1.581e+00 9.879e-01 1.600 0.10963
## sp_ptbBM70 -1.884e-01 1.003e+00 -0.188 0.85098
## sp_ptbBM71 2.578e-01 1.043e+00 0.247 0.80478
## sp_ptbBM72 7.082e-02 9.393e-01 0.075 0.93990
## sp_ptbBM73 -2.614e-01 8.686e-01 -0.301 0.76346
## sp_ptbBM74 8.836e-01 7.025e-01 1.258 0.20847
## sp_ptbBM75 2.695e-01 8.276e-01 0.326 0.74468
## sp_ptbBM76 7.926e-01 8.110e-01 0.977 0.32842
## sp_ptbBM77 5.305e-01 8.175e-01 0.649 0.51634
## sp_ptbBM78 1.336e+00 8.922e-01 1.497 0.13442
## sp_ptbBM79 1.125e+00 9.032e-01 1.245 0.21303
## sp_ptbBM80 4.905e-02 8.660e-01 0.057 0.95483
## sp_ptbBM81 7.175e-01 8.868e-01 0.809 0.41847
## sp_ptbBM82 1.242e-01 7.908e-01 0.157 0.87525
## sp_ptbBM83 1.891e-01 8.299e-01 0.228 0.81975
## sp_ptbBM84 6.184e-01 8.121e-01 0.761 0.44637
## sp_ptbBM85 -9.928e-01 8.856e-01 -1.121 0.26225
## sp_ptbBM86 1.285e+00 7.504e-01 1.712 0.08688 .
## sp_ptbBM87 6.764e-03 7.564e-01 0.009 0.99287
## sp_ptbBM88 4.594e-01 7.689e-01 0.598 0.55017
## sp_ptbBM89 -6.497e-01 8.417e-01 -0.772 0.44016
## sp_ptbBM90 9.597e-01 8.432e-01 1.138 0.25505
## sp_ptbBM91 9.982e-02 9.589e-01 0.104 0.91709
## sp_ptbBM92 5.419e-01 7.827e-01 0.692 0.48871
## sp_ptbBM93 5.026e-01 8.900e-01 0.565 0.57223
## sp_ptbBM94 -1.043e-01 9.093e-01 -0.115 0.90869
## sp_ptbBM95 5.599e-01 8.358e-01 0.670 0.50292
## sp_ptbBM96 8.995e-01 7.030e-01 1.280 0.20072
## sp_ptbBM97 8.022e-01 8.733e-01 0.919 0.35831
## sp_ptbBM98 1.085e-01 1.018e+00 0.107 0.91510
## sp_ptbBM99 7.395e-01 9.092e-01 0.813 0.41603
## sp_ptbBM100 2.023e-02 8.478e-01 0.024 0.98096
## sp_ptbBM101 5.643e-01 7.434e-01 0.759 0.44784
## sp_ptbBM102 1.577e-01 7.853e-01 0.201 0.84083
## sp_ptbBM103 7.282e-02 8.047e-01 0.091 0.92789
## sp_ptbBM104 4.079e-01 8.039e-01 0.507 0.61186
## sp_ptbBM105 1.008e-01 1.036e+00 0.097 0.92251
## sp_ptbBM106 -5.871e-01 9.315e-01 -0.630 0.52852
## sp_ptbBM107 -2.793e-01 8.419e-01 -0.332 0.74007
## sp_ptbBM108 2.943e-01 8.334e-01 0.353 0.72401
## sp_ptbBM109 -2.923e-01 9.176e-01 -0.319 0.75006
## sp_ptbBM110 6.300e-01 7.449e-01 0.846 0.39771
## sp_ptbBM111 -1.888e-03 1.036e+00 -0.002 0.99855
## sp_ptbBM112 -2.997e-01 9.151e-01 -0.327 0.74329
## sp_ptbBM113 8.883e-01 7.691e-01 1.155 0.24809
## sp_ptbBM114 8.528e-01 7.960e-01 1.071 0.28401
## sp_ptbBM115 -7.721e-01 8.698e-01 -0.888 0.37471
## sp_ptbBM116 2.046e+00 7.249e-01 2.822 0.00477 **
## sp_ptbBM117 -1.100e+00 8.341e-01 -1.319 0.18720
## sp_ptbBM118 2.252e+00 8.080e-01 2.787 0.00533 **
## sp_ptbBM119 -1.177e+00 1.044e+00 -1.127 0.25985
## sp_ptbBM120 1.256e+00 7.892e-01 1.591 0.11158
## sp_ptbBM121 -4.664e-01 7.812e-01 -0.597 0.55045
## sp_ptbBM122 8.489e-01 7.344e-01 1.156 0.24774
## sp_ptbBM123 3.753e-02 6.415e-01 0.059 0.95335
## sp_ptbBM124 6.630e-01 7.574e-01 0.875 0.38135
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21990.89) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 943.43 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3234.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21991
## Std. Err.: 143476
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2976.296m5a <- glm.nb(ptbBM ~ cb5.minRH + sp_ptbBM,data=week); summary(m5a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBM, data = week, init.theta = 22284.16938,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4192 -0.7548 -0.1076 0.5433 2.6199
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.720e+01 1.055e+01 1.630 0.10321
## cb5.minRHv1.l1 -1.483e-01 2.389e-01 -0.621 0.53474
## cb5.minRHv1.l2 6.178e-02 1.595e-01 0.387 0.69851
## cb5.minRHv2.l1 -1.606e+00 9.187e-01 -1.748 0.08039 .
## cb5.minRHv2.l2 -6.450e-01 6.677e-01 -0.966 0.33402
## cb5.minRHv3.l1 -9.477e-01 5.807e-01 -1.632 0.10264
## cb5.minRHv3.l2 -5.744e-01 4.347e-01 -1.321 0.18641
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.475e+06 2.237e+06 -0.659 0.50973
## sp_ptbBM5 1.534e+01 1.190e+01 1.289 0.19734
## sp_ptbBM6 -2.922e+00 2.005e+00 -1.457 0.14499
## sp_ptbBM7 8.026e-01 1.073e+00 0.748 0.45428
## sp_ptbBM8 -1.369e+00 9.932e-01 -1.378 0.16810
## sp_ptbBM9 7.567e-01 9.445e-01 0.801 0.42304
## sp_ptbBM10 -3.122e+00 1.049e+00 -2.975 0.00293 **
## sp_ptbBM11 -2.315e-01 9.566e-01 -0.242 0.80878
## sp_ptbBM12 -1.127e+00 8.206e-01 -1.373 0.16980
## sp_ptbBM13 -3.965e-02 9.349e-01 -0.042 0.96617
## sp_ptbBM14 -1.387e+00 1.083e+00 -1.280 0.20041
## sp_ptbBM15 -1.608e-01 1.137e+00 -0.142 0.88747
## sp_ptbBM16 -1.310e+00 1.286e+00 -1.019 0.30813
## sp_ptbBM17 -6.231e-01 1.068e+00 -0.584 0.55954
## sp_ptbBM18 -5.816e-01 1.079e+00 -0.539 0.58989
## sp_ptbBM19 -4.891e-01 9.940e-01 -0.492 0.62266
## sp_ptbBM20 -3.470e-01 1.101e+00 -0.315 0.75256
## sp_ptbBM21 7.269e-02 1.019e+00 0.071 0.94314
## sp_ptbBM22 3.655e-01 9.295e-01 0.393 0.69415
## sp_ptbBM23 -1.017e+00 9.589e-01 -1.060 0.28905
## sp_ptbBM24 -1.587e-01 8.420e-01 -0.188 0.85051
## sp_ptbBM25 -5.421e-01 7.700e-01 -0.704 0.48142
## sp_ptbBM26 -3.071e-01 8.328e-01 -0.369 0.71228
## sp_ptbBM27 -1.350e+00 9.270e-01 -1.457 0.14525
## sp_ptbBM28 -1.587e+00 1.024e+00 -1.550 0.12115
## sp_ptbBM29 3.401e-01 8.739e-01 0.389 0.69714
## sp_ptbBM30 -1.175e+00 1.003e+00 -1.171 0.24143
## sp_ptbBM31 -1.910e-02 1.060e+00 -0.018 0.98563
## sp_ptbBM32 -2.070e+00 1.369e+00 -1.512 0.13046
## sp_ptbBM33 -1.612e+00 1.269e+00 -1.271 0.20389
## sp_ptbBM34 -1.109e+00 1.354e+00 -0.819 0.41259
## sp_ptbBM35 -1.404e+00 1.536e+00 -0.914 0.36078
## sp_ptbBM36 -1.918e-01 1.551e+00 -0.124 0.90155
## sp_ptbBM37 -1.270e+00 1.351e+00 -0.941 0.34695
## sp_ptbBM38 7.275e-02 1.131e+00 0.064 0.94870
## sp_ptbBM39 -1.053e+00 1.149e+00 -0.916 0.35942
## sp_ptbBM40 -3.307e-02 9.692e-01 -0.034 0.97278
## sp_ptbBM41 -7.771e-01 1.055e+00 -0.737 0.46120
## sp_ptbBM42 -1.345e+00 1.092e+00 -1.232 0.21796
## sp_ptbBM43 -7.441e-01 9.343e-01 -0.796 0.42580
## sp_ptbBM44 -5.176e-01 9.457e-01 -0.547 0.58420
## sp_ptbBM45 -2.657e-01 8.920e-01 -0.298 0.76579
## sp_ptbBM46 -1.256e+00 9.571e-01 -1.312 0.18953
## sp_ptbBM47 -5.985e-01 9.798e-01 -0.611 0.54134
## sp_ptbBM48 -7.957e-01 7.947e-01 -1.001 0.31674
## sp_ptbBM49 -1.857e-01 1.169e+00 -0.159 0.87385
## sp_ptbBM50 -1.433e+00 9.687e-01 -1.479 0.13906
## sp_ptbBM51 2.306e-01 1.067e+00 0.216 0.82893
## sp_ptbBM52 -1.031e+00 9.856e-01 -1.046 0.29577
## sp_ptbBM53 1.796e-01 9.212e-01 0.195 0.84545
## sp_ptbBM54 -4.590e-01 8.971e-01 -0.512 0.60889
## sp_ptbBM55 -4.741e-01 9.946e-01 -0.477 0.63362
## sp_ptbBM56 -2.438e-01 1.079e+00 -0.226 0.82129
## sp_ptbBM57 -1.713e-01 1.049e+00 -0.163 0.87033
## sp_ptbBM58 -3.096e-01 8.906e-01 -0.348 0.72810
## sp_ptbBM59 -1.505e+00 1.076e+00 -1.399 0.16192
## sp_ptbBM60 -8.917e-01 1.070e+00 -0.833 0.40482
## sp_ptbBM61 -7.324e-01 1.055e+00 -0.694 0.48751
## sp_ptbBM62 -6.589e-01 1.071e+00 -0.615 0.53844
## sp_ptbBM63 -1.827e+00 1.311e+00 -1.394 0.16329
## sp_ptbBM64 -9.561e-01 1.415e+00 -0.676 0.49934
## sp_ptbBM65 -9.820e-01 1.136e+00 -0.865 0.38723
## sp_ptbBM66 -7.167e-01 8.801e-01 -0.814 0.41549
## sp_ptbBM67 2.977e-01 7.096e-01 0.420 0.67481
## sp_ptbBM68 -2.656e-01 6.724e-01 -0.395 0.69282
## sp_ptbBM69 4.190e-01 7.596e-01 0.552 0.58120
## sp_ptbBM70 -1.028e+00 1.016e+00 -1.012 0.31151
## sp_ptbBM71 -5.361e-01 9.877e-01 -0.543 0.58730
## sp_ptbBM72 -4.652e-01 9.202e-01 -0.506 0.61316
## sp_ptbBM73 -9.096e-01 8.782e-01 -1.036 0.30034
## sp_ptbBM74 7.332e-01 7.852e-01 0.934 0.35042
## sp_ptbBM75 -5.019e-01 8.481e-01 -0.592 0.55401
## sp_ptbBM76 4.294e-01 7.019e-01 0.612 0.54069
## sp_ptbBM77 -3.389e-01 9.105e-01 -0.372 0.70973
## sp_ptbBM78 7.166e-01 1.030e+00 0.696 0.48666
## sp_ptbBM79 4.498e-01 8.976e-01 0.501 0.61630
## sp_ptbBM80 -8.378e-01 9.603e-01 -0.872 0.38298
## sp_ptbBM81 -2.868e-01 8.403e-01 -0.341 0.73284
## sp_ptbBM82 -5.488e-01 7.643e-01 -0.718 0.47268
## sp_ptbBM83 -9.749e-01 8.511e-01 -1.145 0.25202
## sp_ptbBM84 -3.080e-01 9.544e-01 -0.323 0.74687
## sp_ptbBM85 -1.693e+00 1.099e+00 -1.540 0.12348
## sp_ptbBM86 2.341e-01 1.025e+00 0.228 0.81943
## sp_ptbBM87 -2.274e-01 7.985e-01 -0.285 0.77577
## sp_ptbBM88 -3.292e-01 8.538e-01 -0.386 0.69985
## sp_ptbBM89 -1.217e+00 8.333e-01 -1.460 0.14433
## sp_ptbBM90 1.142e-01 8.765e-01 0.130 0.89638
## sp_ptbBM91 -6.856e-01 1.646e+00 -0.417 0.67694
## sp_ptbBM92 8.631e-01 1.192e+00 0.724 0.46915
## sp_ptbBM93 7.456e-01 1.262e+00 0.591 0.55453
## sp_ptbBM94 3.079e-01 1.090e+00 0.283 0.77747
## sp_ptbBM95 1.341e+00 1.100e+00 1.220 0.22259
## sp_ptbBM96 1.883e+00 1.126e+00 1.673 0.09441 .
## sp_ptbBM97 2.131e+00 1.397e+00 1.525 0.12726
## sp_ptbBM98 -6.762e-01 1.337e+00 -0.506 0.61289
## sp_ptbBM99 9.335e-01 1.155e+00 0.808 0.41905
## sp_ptbBM100 5.583e-01 1.065e+00 0.524 0.60026
## sp_ptbBM101 9.865e-01 9.961e-01 0.990 0.32201
## sp_ptbBM102 1.137e+00 9.564e-01 1.189 0.23459
## sp_ptbBM103 8.712e-01 9.361e-01 0.931 0.35198
## sp_ptbBM104 1.060e+00 8.751e-01 1.212 0.22563
## sp_ptbBM105 -2.148e-01 9.297e-01 -0.231 0.81725
## sp_ptbBM106 2.272e-01 1.001e+00 0.227 0.82039
## sp_ptbBM107 -1.506e-01 7.850e-01 -0.192 0.84787
## sp_ptbBM108 4.777e-01 8.535e-01 0.560 0.57564
## sp_ptbBM109 -1.516e-01 6.680e-01 -0.227 0.82049
## sp_ptbBM110 1.052e+00 6.793e-01 1.549 0.12132
## sp_ptbBM111 5.152e-01 6.970e-01 0.739 0.45980
## sp_ptbBM112 -2.536e-01 7.185e-01 -0.353 0.72410
## sp_ptbBM113 4.126e-01 9.235e-01 0.447 0.65507
## sp_ptbBM114 6.130e-01 8.730e-01 0.702 0.48261
## sp_ptbBM115 -9.407e-01 8.881e-01 -1.059 0.28950
## sp_ptbBM116 1.891e+00 7.500e-01 2.521 0.01171 *
## sp_ptbBM117 -1.613e+00 9.618e-01 -1.677 0.09353 .
## sp_ptbBM118 1.808e+00 7.696e-01 2.349 0.01882 *
## sp_ptbBM119 -1.658e+00 1.074e+00 -1.544 0.12248
## sp_ptbBM120 6.088e-01 9.977e-01 0.610 0.54174
## sp_ptbBM121 -5.461e-01 9.366e-01 -0.583 0.55982
## sp_ptbBM122 7.548e-01 8.127e-01 0.929 0.35305
## sp_ptbBM123 4.695e-02 6.455e-01 0.073 0.94201
## sp_ptbBM124 8.533e-01 7.540e-01 1.132 0.25778
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22284.17) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 941.58 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3232.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22284
## Std. Err.: 144435
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2974.443m6a <- glm.nb(ptbBM ~ cb6.meanRH + sp_ptbBM,data=week); summary(m6a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb6.meanRH + sp_ptbBM, data = week,
## init.theta = 21891.10351, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4463 -0.7881 -0.1323 0.5381 2.6916
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.102e+01 1.250e+01 0.881 0.3782
## cb6.meanRHv1.l1 -2.221e-01 2.861e-01 -0.776 0.4375
## cb6.meanRHv1.l2 -8.627e-02 1.801e-01 -0.479 0.6320
## cb6.meanRHv2.l1 -8.449e-01 1.022e+00 -0.826 0.4086
## cb6.meanRHv2.l2 2.009e-01 6.840e-01 0.294 0.7690
## cb6.meanRHv3.l1 -3.194e-01 4.414e-01 -0.724 0.4693
## cb6.meanRHv3.l2 2.764e-01 3.224e-01 0.857 0.3912
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.338e+06 2.226e+06 -0.601 0.5478
## sp_ptbBM5 1.468e+01 1.182e+01 1.242 0.2143
## sp_ptbBM6 -2.040e+00 2.025e+00 -1.008 0.3136
## sp_ptbBM7 1.444e+00 1.067e+00 1.353 0.1761
## sp_ptbBM8 -1.016e+00 9.766e-01 -1.041 0.2980
## sp_ptbBM9 8.134e-01 9.457e-01 0.860 0.3897
## sp_ptbBM10 -2.611e+00 1.096e+00 -2.383 0.0172 *
## sp_ptbBM11 6.370e-01 8.965e-01 0.711 0.4774
## sp_ptbBM12 -6.359e-01 8.247e-01 -0.771 0.4407
## sp_ptbBM13 9.946e-01 1.071e+00 0.928 0.3532
## sp_ptbBM14 -8.158e-01 1.113e+00 -0.733 0.4637
## sp_ptbBM15 4.559e-01 1.058e+00 0.431 0.6665
## sp_ptbBM16 -9.322e-01 1.239e+00 -0.752 0.4519
## sp_ptbBM17 7.200e-03 1.060e+00 0.007 0.9946
## sp_ptbBM18 -6.874e-02 1.084e+00 -0.063 0.9494
## sp_ptbBM19 3.086e-02 1.051e+00 0.029 0.9766
## sp_ptbBM20 -2.315e-02 1.165e+00 -0.020 0.9841
## sp_ptbBM21 5.344e-01 9.587e-01 0.557 0.5772
## sp_ptbBM22 3.629e-01 1.082e+00 0.336 0.7372
## sp_ptbBM23 -1.443e+00 1.489e+00 -0.969 0.3325
## sp_ptbBM24 -9.378e-01 1.932e+00 -0.485 0.6275
## sp_ptbBM25 -1.867e+00 2.333e+00 -0.800 0.4237
## sp_ptbBM26 -1.606e+00 2.528e+00 -0.635 0.5252
## sp_ptbBM27 -3.267e+00 2.802e+00 -1.166 0.2435
## sp_ptbBM28 -2.738e+00 2.824e+00 -0.970 0.3323
## sp_ptbBM29 -1.317e+00 2.812e+00 -0.469 0.6394
## sp_ptbBM30 -2.278e+00 2.747e+00 -0.829 0.4070
## sp_ptbBM31 -1.257e+00 2.779e+00 -0.452 0.6510
## sp_ptbBM32 -2.636e+00 2.801e+00 -0.941 0.3467
## sp_ptbBM33 -2.544e+00 2.780e+00 -0.915 0.3601
## sp_ptbBM34 -1.812e+00 2.650e+00 -0.684 0.4940
## sp_ptbBM35 -2.420e+00 2.971e+00 -0.815 0.4154
## sp_ptbBM36 -1.017e+00 2.752e+00 -0.370 0.7117
## sp_ptbBM37 -2.062e+00 2.286e+00 -0.902 0.3671
## sp_ptbBM38 -4.145e-01 2.052e+00 -0.202 0.8399
## sp_ptbBM39 -1.486e+00 1.814e+00 -0.819 0.4127
## sp_ptbBM40 -3.092e-02 1.488e+00 -0.021 0.9834
## sp_ptbBM41 -8.306e-01 1.483e+00 -0.560 0.5753
## sp_ptbBM42 -1.078e+00 1.242e+00 -0.868 0.3854
## sp_ptbBM43 -2.583e-01 1.233e+00 -0.209 0.8341
## sp_ptbBM44 -3.630e-01 1.311e+00 -0.277 0.7819
## sp_ptbBM45 1.426e-01 1.393e+00 0.102 0.9185
## sp_ptbBM46 -9.680e-01 1.420e+00 -0.682 0.4955
## sp_ptbBM47 -3.028e-01 1.561e+00 -0.194 0.8461
## sp_ptbBM48 -8.081e-01 1.365e+00 -0.592 0.5538
## sp_ptbBM49 -1.169e-01 1.638e+00 -0.071 0.9431
## sp_ptbBM50 -1.050e+00 1.279e+00 -0.821 0.4116
## sp_ptbBM51 6.503e-01 1.295e+00 0.502 0.6157
## sp_ptbBM52 -6.944e-01 1.138e+00 -0.610 0.5416
## sp_ptbBM53 3.516e-01 1.100e+00 0.320 0.7492
## sp_ptbBM54 7.228e-02 9.999e-01 0.072 0.9424
## sp_ptbBM55 -6.655e-01 9.889e-01 -0.673 0.5010
## sp_ptbBM56 6.644e-01 1.008e+00 0.659 0.5099
## sp_ptbBM57 2.757e-01 1.076e+00 0.256 0.7978
## sp_ptbBM58 1.617e-01 9.816e-01 0.165 0.8692
## sp_ptbBM59 -1.053e+00 1.205e+00 -0.874 0.3823
## sp_ptbBM60 -7.594e-01 1.270e+00 -0.598 0.5500
## sp_ptbBM61 -3.452e-01 1.231e+00 -0.280 0.7792
## sp_ptbBM62 -1.326e+00 1.256e+00 -1.055 0.2912
## sp_ptbBM63 -9.390e-01 1.474e+00 -0.637 0.5242
## sp_ptbBM64 -4.911e-01 1.704e+00 -0.288 0.7732
## sp_ptbBM65 -3.319e-01 1.318e+00 -0.252 0.8011
## sp_ptbBM66 -3.435e-01 1.116e+00 -0.308 0.7583
## sp_ptbBM67 7.328e-01 9.347e-01 0.784 0.4331
## sp_ptbBM68 2.219e-01 8.770e-01 0.253 0.8002
## sp_ptbBM69 5.506e-01 9.250e-01 0.595 0.5517
## sp_ptbBM70 5.472e-02 1.089e+00 0.050 0.9599
## sp_ptbBM71 5.893e-03 1.054e+00 0.006 0.9955
## sp_ptbBM72 5.865e-03 9.392e-01 0.006 0.9950
## sp_ptbBM73 -4.587e-01 8.628e-01 -0.532 0.5950
## sp_ptbBM74 9.351e-01 7.895e-01 1.184 0.2363
## sp_ptbBM75 -1.558e-01 8.422e-01 -0.185 0.8533
## sp_ptbBM76 2.431e-01 7.036e-01 0.345 0.7297
## sp_ptbBM77 2.448e-01 8.831e-01 0.277 0.7816
## sp_ptbBM78 8.418e-01 1.037e+00 0.812 0.4169
## sp_ptbBM79 5.630e-01 9.166e-01 0.614 0.5391
## sp_ptbBM80 -5.060e-01 1.004e+00 -0.504 0.6142
## sp_ptbBM81 3.361e-01 9.307e-01 0.361 0.7180
## sp_ptbBM82 -1.240e-01 8.208e-01 -0.151 0.8800
## sp_ptbBM83 -3.755e-01 9.096e-01 -0.413 0.6797
## sp_ptbBM84 2.179e-01 9.565e-01 0.228 0.8198
## sp_ptbBM85 -1.418e+00 1.186e+00 -1.195 0.2321
## sp_ptbBM86 6.215e-01 1.084e+00 0.573 0.5664
## sp_ptbBM87 -6.570e-03 9.322e-01 -0.007 0.9944
## sp_ptbBM88 2.837e-01 9.546e-01 0.297 0.7663
## sp_ptbBM89 -7.076e-01 1.024e+00 -0.691 0.4897
## sp_ptbBM90 9.878e-01 9.908e-01 0.997 0.3188
## sp_ptbBM91 7.572e-01 1.279e+00 0.592 0.5539
## sp_ptbBM92 1.234e+00 9.508e-01 1.298 0.1944
## sp_ptbBM93 5.666e-01 9.435e-01 0.600 0.5482
## sp_ptbBM94 -3.433e-01 8.379e-01 -0.410 0.6820
## sp_ptbBM95 1.993e-01 7.658e-01 0.260 0.7947
## sp_ptbBM96 4.363e-01 8.482e-01 0.514 0.6069
## sp_ptbBM97 2.283e-01 8.673e-01 0.263 0.7924
## sp_ptbBM98 6.450e-01 1.231e+00 0.524 0.6002
## sp_ptbBM99 1.007e+00 9.029e-01 1.115 0.2649
## sp_ptbBM100 8.108e-01 8.785e-01 0.923 0.3561
## sp_ptbBM101 8.948e-01 7.492e-01 1.194 0.2323
## sp_ptbBM102 5.052e-01 7.204e-01 0.701 0.4831
## sp_ptbBM103 2.115e-02 7.645e-01 0.028 0.9779
## sp_ptbBM104 4.739e-01 7.934e-01 0.597 0.5503
## sp_ptbBM105 8.670e-01 9.342e-01 0.928 0.3534
## sp_ptbBM106 9.899e-02 7.580e-01 0.131 0.8961
## sp_ptbBM107 1.816e-02 7.635e-01 0.024 0.9810
## sp_ptbBM108 6.970e-01 7.478e-01 0.932 0.3513
## sp_ptbBM109 1.358e-01 7.072e-01 0.192 0.8477
## sp_ptbBM110 6.934e-01 6.424e-01 1.079 0.2804
## sp_ptbBM111 3.719e-01 8.912e-01 0.417 0.6765
## sp_ptbBM112 -7.663e-02 8.683e-01 -0.088 0.9297
## sp_ptbBM113 9.460e-01 9.131e-01 1.036 0.3002
## sp_ptbBM114 4.845e-01 8.476e-01 0.572 0.5676
## sp_ptbBM115 -9.617e-01 8.891e-01 -1.082 0.2794
## sp_ptbBM116 2.011e+00 8.082e-01 2.489 0.0128 *
## sp_ptbBM117 -1.222e+00 9.633e-01 -1.268 0.2047
## sp_ptbBM118 2.292e+00 9.014e-01 2.543 0.0110 *
## sp_ptbBM119 -9.940e-01 1.149e+00 -0.865 0.3869
## sp_ptbBM120 1.631e+00 9.790e-01 1.666 0.0957 .
## sp_ptbBM121 -6.391e-02 8.646e-01 -0.074 0.9411
## sp_ptbBM122 1.231e+00 7.862e-01 1.566 0.1173
## sp_ptbBM123 4.252e-01 6.606e-01 0.644 0.5198
## sp_ptbBM124 7.020e-01 7.556e-01 0.929 0.3529
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21891.1) family taken to be 1)
##
## Null deviance: 1101.3 on 886 degrees of freedom
## Residual deviance: 944.4 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3235.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21891
## Std. Err.: 143955
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2977.261m7a <- glm.nb(ptbBM ~ cb7.maxRH + sp_ptbBM,data=week); summary(m7a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb7.maxRH + sp_ptbBM, data = week, init.theta = 22024.84537,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4674 -0.7951 -0.1165 0.5319 2.5639
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.320e+00 1.250e+01 0.346 0.7296
## cb7.maxRHv1.l1 -2.357e-01 2.992e-01 -0.788 0.4309
## cb7.maxRHv1.l2 1.871e-02 2.040e-01 0.092 0.9269
## cb7.maxRHv2.l1 -1.392e-01 9.877e-01 -0.141 0.8879
## cb7.maxRHv2.l2 1.710e-01 6.943e-01 0.246 0.8055
## cb7.maxRHv3.l1 5.055e-02 2.824e-01 0.179 0.8579
## cb7.maxRHv3.l2 2.605e-01 1.898e-01 1.373 0.1699
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.327e+06 2.224e+06 -0.597 0.5507
## sp_ptbBM5 1.508e+01 1.204e+01 1.252 0.2105
## sp_ptbBM6 -7.682e-01 2.155e+00 -0.356 0.7215
## sp_ptbBM7 1.981e+00 1.341e+00 1.477 0.1397
## sp_ptbBM8 -2.493e-01 1.126e+00 -0.221 0.8248
## sp_ptbBM9 1.716e+00 9.949e-01 1.725 0.0845 .
## sp_ptbBM10 -2.259e+00 1.074e+00 -2.104 0.0353 *
## sp_ptbBM11 1.075e+00 7.960e-01 1.350 0.1769
## sp_ptbBM12 -7.990e-01 7.542e-01 -1.059 0.2894
## sp_ptbBM13 2.029e+00 9.767e-01 2.077 0.0378 *
## sp_ptbBM14 -6.962e-01 8.290e-01 -0.840 0.4010
## sp_ptbBM15 3.463e-01 7.202e-01 0.481 0.6307
## sp_ptbBM16 -2.908e-01 8.167e-01 -0.356 0.7218
## sp_ptbBM17 1.421e-02 7.504e-01 0.019 0.9849
## sp_ptbBM18 -6.563e-01 7.679e-01 -0.855 0.3927
## sp_ptbBM19 -5.617e-01 7.982e-01 -0.704 0.4816
## sp_ptbBM20 8.802e-01 1.152e+00 0.764 0.4448
## sp_ptbBM21 5.774e-01 7.416e-01 0.779 0.4363
## sp_ptbBM22 7.318e-01 7.586e-01 0.965 0.3347
## sp_ptbBM23 -9.483e-01 8.774e-01 -1.081 0.2798
## sp_ptbBM24 1.009e-01 7.708e-01 0.131 0.8959
## sp_ptbBM25 -7.471e-01 9.520e-01 -0.785 0.4326
## sp_ptbBM26 -4.297e-01 1.137e+00 -0.378 0.7054
## sp_ptbBM27 -6.022e-01 1.412e+00 -0.426 0.6698
## sp_ptbBM28 -1.531e+00 1.269e+00 -1.206 0.2276
## sp_ptbBM29 -1.156e+00 2.176e+00 -0.531 0.5953
## sp_ptbBM30 -2.758e+00 2.514e+00 -1.097 0.2726
## sp_ptbBM31 -2.924e+00 3.632e+00 -0.805 0.4209
## sp_ptbBM32 -4.804e+00 4.751e+00 -1.011 0.3120
## sp_ptbBM33 -4.585e+00 5.816e+00 -0.788 0.4305
## sp_ptbBM34 -2.688e+00 6.322e+00 -0.425 0.6707
## sp_ptbBM35 -3.371e+00 7.573e+00 -0.445 0.6562
## sp_ptbBM36 -1.766e+00 6.507e+00 -0.271 0.7861
## sp_ptbBM37 -2.352e+00 5.965e+00 -0.394 0.6934
## sp_ptbBM38 -8.224e-01 5.320e+00 -0.155 0.8772
## sp_ptbBM39 -1.712e+00 4.334e+00 -0.395 0.6929
## sp_ptbBM40 -2.702e-01 3.376e+00 -0.080 0.9362
## sp_ptbBM41 -6.058e-01 2.966e+00 -0.204 0.8382
## sp_ptbBM42 -7.989e-01 2.043e+00 -0.391 0.6958
## sp_ptbBM43 -2.441e-01 2.115e+00 -0.115 0.9081
## sp_ptbBM44 -5.178e-01 2.355e+00 -0.220 0.8260
## sp_ptbBM45 -1.501e-01 2.521e+00 -0.060 0.9525
## sp_ptbBM46 -1.364e+00 2.601e+00 -0.525 0.5998
## sp_ptbBM47 -9.843e-01 2.964e+00 -0.332 0.7398
## sp_ptbBM48 -1.076e+00 2.718e+00 -0.396 0.6922
## sp_ptbBM49 -8.903e-02 2.663e+00 -0.033 0.9733
## sp_ptbBM50 -7.152e-01 2.164e+00 -0.330 0.7411
## sp_ptbBM51 1.252e+00 2.032e+00 0.616 0.5379
## sp_ptbBM52 -1.818e-01 1.798e+00 -0.101 0.9195
## sp_ptbBM53 8.707e-01 1.597e+00 0.545 0.5857
## sp_ptbBM54 7.511e-01 1.351e+00 0.556 0.5784
## sp_ptbBM55 6.389e-01 1.332e+00 0.480 0.6314
## sp_ptbBM56 1.363e+00 1.207e+00 1.130 0.2586
## sp_ptbBM57 1.409e+00 1.291e+00 1.092 0.2750
## sp_ptbBM58 9.388e-01 1.266e+00 0.742 0.4583
## sp_ptbBM59 -1.234e-01 1.421e+00 -0.087 0.9308
## sp_ptbBM60 -1.015e-01 1.655e+00 -0.061 0.9511
## sp_ptbBM61 1.898e-02 1.654e+00 0.011 0.9908
## sp_ptbBM62 -5.046e-01 1.654e+00 -0.305 0.7603
## sp_ptbBM63 -2.652e-01 1.818e+00 -0.146 0.8840
## sp_ptbBM64 3.021e-01 2.112e+00 0.143 0.8863
## sp_ptbBM65 5.414e-01 1.777e+00 0.305 0.7606
## sp_ptbBM66 3.918e-01 1.636e+00 0.239 0.8108
## sp_ptbBM67 1.562e+00 1.373e+00 1.137 0.2555
## sp_ptbBM68 1.137e+00 1.301e+00 0.874 0.3823
## sp_ptbBM69 1.820e+00 1.179e+00 1.544 0.1226
## sp_ptbBM70 9.955e-01 1.197e+00 0.832 0.4057
## sp_ptbBM71 1.083e+00 1.184e+00 0.915 0.3604
## sp_ptbBM72 8.982e-01 1.125e+00 0.798 0.4248
## sp_ptbBM73 1.032e-01 1.061e+00 0.097 0.9226
## sp_ptbBM74 1.274e+00 1.097e+00 1.162 0.2454
## sp_ptbBM75 1.905e-01 1.073e+00 0.178 0.8590
## sp_ptbBM76 9.707e-01 1.078e+00 0.900 0.3681
## sp_ptbBM77 9.680e-01 1.038e+00 0.932 0.3512
## sp_ptbBM78 2.014e+00 1.093e+00 1.842 0.0655 .
## sp_ptbBM79 1.453e+00 1.027e+00 1.415 0.1571
## sp_ptbBM80 2.966e-01 1.074e+00 0.276 0.7824
## sp_ptbBM81 5.881e-01 1.143e+00 0.515 0.6068
## sp_ptbBM82 1.508e-01 1.063e+00 0.142 0.8872
## sp_ptbBM83 2.072e-01 9.644e-01 0.215 0.8299
## sp_ptbBM84 9.735e-01 1.105e+00 0.881 0.3783
## sp_ptbBM85 -3.314e-01 1.381e+00 -0.240 0.8103
## sp_ptbBM86 1.747e+00 1.205e+00 1.449 0.1472
## sp_ptbBM87 8.265e-01 1.194e+00 0.692 0.4887
## sp_ptbBM88 9.260e-01 1.163e+00 0.796 0.4258
## sp_ptbBM89 -1.709e-01 1.286e+00 -0.133 0.8943
## sp_ptbBM90 1.874e+00 1.150e+00 1.629 0.1032
## sp_ptbBM91 8.637e-01 1.056e+00 0.818 0.4136
## sp_ptbBM92 1.335e+00 9.308e-01 1.435 0.1514
## sp_ptbBM93 5.436e-01 9.141e-01 0.595 0.5520
## sp_ptbBM94 -4.983e-01 8.925e-01 -0.558 0.5767
## sp_ptbBM95 1.174e-02 8.592e-01 0.014 0.9891
## sp_ptbBM96 4.115e-02 8.178e-01 0.050 0.9599
## sp_ptbBM97 5.823e-01 8.043e-01 0.724 0.4690
## sp_ptbBM98 6.373e-01 1.097e+00 0.581 0.5613
## sp_ptbBM99 1.239e+00 8.238e-01 1.504 0.1327
## sp_ptbBM100 7.551e-01 7.732e-01 0.977 0.3288
## sp_ptbBM101 7.251e-01 6.832e-01 1.061 0.2886
## sp_ptbBM102 5.139e-01 7.154e-01 0.718 0.4725
## sp_ptbBM103 -2.059e-02 7.229e-01 -0.028 0.9773
## sp_ptbBM104 8.600e-01 7.525e-01 1.143 0.2531
## sp_ptbBM105 1.206e+00 9.129e-01 1.321 0.1865
## sp_ptbBM106 -3.331e-01 1.033e+00 -0.322 0.7472
## sp_ptbBM107 -4.476e-02 1.076e+00 -0.042 0.9668
## sp_ptbBM108 2.568e-01 8.848e-01 0.290 0.7716
## sp_ptbBM109 -4.236e-01 1.037e+00 -0.408 0.6830
## sp_ptbBM110 7.575e-03 9.991e-01 0.008 0.9940
## sp_ptbBM111 1.119e+00 1.319e+00 0.848 0.3964
## sp_ptbBM112 8.114e-02 1.129e+00 0.072 0.9427
## sp_ptbBM113 1.392e+00 8.542e-01 1.630 0.1031
## sp_ptbBM114 5.754e-01 8.336e-01 0.690 0.4900
## sp_ptbBM115 -8.360e-01 8.228e-01 -1.016 0.3096
## sp_ptbBM116 1.941e+00 7.784e-01 2.494 0.0126 *
## sp_ptbBM117 -1.767e+00 9.749e-01 -1.812 0.0699 .
## sp_ptbBM118 2.093e+00 1.071e+00 1.955 0.0506 .
## sp_ptbBM119 -1.811e+00 1.186e+00 -1.527 0.1268
## sp_ptbBM120 9.408e-01 9.017e-01 1.043 0.2968
## sp_ptbBM121 -5.242e-01 8.081e-01 -0.649 0.5166
## sp_ptbBM122 3.643e-01 8.314e-01 0.438 0.6613
## sp_ptbBM123 -6.105e-01 7.559e-01 -0.808 0.4193
## sp_ptbBM124 2.255e-01 8.244e-01 0.274 0.7844
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22024.85) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 942.52 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3233.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22025
## Std. Err.: 143520
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2975.382m8a <- glm.nb(ptbBM ~ cb8.AH + sp_ptbBM,data=week); summary(m8a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb8.AH + sp_ptbBM, data = week, init.theta = 22297.04219,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5092 -0.7695 -0.1027 0.5323 2.7731
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.850e+00 1.168e+01 -0.586 0.55760
## cb8.AHv1.l1 3.183e-01 2.924e-01 1.089 0.27632
## cb8.AHv1.l2 -5.072e-02 2.057e-01 -0.247 0.80523
## cb8.AHv2.l1 5.157e-01 9.564e-01 0.539 0.58972
## cb8.AHv2.l2 -2.815e-01 6.913e-01 -0.407 0.68386
## cb8.AHv3.l1 4.144e-01 5.457e-01 0.759 0.44768
## cb8.AHv3.l2 5.219e-01 3.866e-01 1.350 0.17700
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.306e+06 2.243e+06 -0.582 0.56055
## sp_ptbBM5 1.655e+01 1.213e+01 1.364 0.17247
## sp_ptbBM6 -1.267e+00 2.340e+00 -0.542 0.58809
## sp_ptbBM7 2.352e+00 1.902e+00 1.236 0.21636
## sp_ptbBM8 1.551e-01 1.629e+00 0.095 0.92416
## sp_ptbBM9 3.541e+00 1.650e+00 2.146 0.03189 *
## sp_ptbBM10 -8.370e-01 1.638e+00 -0.511 0.60931
## sp_ptbBM11 2.753e+00 1.587e+00 1.735 0.08272 .
## sp_ptbBM12 9.150e-01 1.595e+00 0.574 0.56611
## sp_ptbBM13 2.436e+00 1.364e+00 1.786 0.07409 .
## sp_ptbBM14 -2.857e-03 1.450e+00 -0.002 0.99843
## sp_ptbBM15 1.165e+00 1.453e+00 0.802 0.42268
## sp_ptbBM16 1.899e+00 1.569e+00 1.210 0.22623
## sp_ptbBM17 1.873e+00 1.539e+00 1.218 0.22338
## sp_ptbBM18 1.337e+00 1.504e+00 0.889 0.37410
## sp_ptbBM19 8.508e-01 1.509e+00 0.564 0.57280
## sp_ptbBM20 1.187e+00 1.586e+00 0.748 0.45424
## sp_ptbBM21 1.900e+00 1.790e+00 1.061 0.28854
## sp_ptbBM22 1.898e+00 1.970e+00 0.963 0.33537
## sp_ptbBM23 2.787e+00 2.564e+00 1.087 0.27693
## sp_ptbBM24 4.314e+00 2.881e+00 1.497 0.13427
## sp_ptbBM25 4.828e+00 3.491e+00 1.383 0.16663
## sp_ptbBM26 5.182e+00 3.718e+00 1.394 0.16338
## sp_ptbBM27 3.752e+00 3.942e+00 0.952 0.34116
## sp_ptbBM28 4.047e+00 3.975e+00 1.018 0.30857
## sp_ptbBM29 4.948e+00 3.766e+00 1.314 0.18887
## sp_ptbBM30 3.410e+00 3.787e+00 0.900 0.36797
## sp_ptbBM31 4.990e+00 3.719e+00 1.342 0.17962
## sp_ptbBM32 3.696e+00 3.533e+00 1.046 0.29556
## sp_ptbBM33 4.483e+00 3.678e+00 1.219 0.22289
## sp_ptbBM34 5.297e+00 3.873e+00 1.368 0.17146
## sp_ptbBM35 5.533e+00 4.096e+00 1.351 0.17681
## sp_ptbBM36 6.179e+00 3.905e+00 1.582 0.11355
## sp_ptbBM37 3.947e+00 3.685e+00 1.071 0.28414
## sp_ptbBM38 4.739e+00 3.363e+00 1.409 0.15877
## sp_ptbBM39 3.180e+00 3.231e+00 0.984 0.32503
## sp_ptbBM40 3.903e+00 3.018e+00 1.293 0.19587
## sp_ptbBM41 2.885e+00 2.627e+00 1.098 0.27207
## sp_ptbBM42 2.173e+00 2.508e+00 0.866 0.38624
## sp_ptbBM43 2.969e+00 2.548e+00 1.165 0.24396
## sp_ptbBM44 2.841e+00 2.437e+00 1.166 0.24372
## sp_ptbBM45 3.840e+00 2.610e+00 1.471 0.14125
## sp_ptbBM46 2.858e+00 2.684e+00 1.065 0.28679
## sp_ptbBM47 3.839e+00 2.816e+00 1.363 0.17287
## sp_ptbBM48 3.643e+00 2.890e+00 1.260 0.20755
## sp_ptbBM49 4.419e+00 2.883e+00 1.533 0.12525
## sp_ptbBM50 2.799e+00 2.769e+00 1.011 0.31220
## sp_ptbBM51 4.790e+00 2.902e+00 1.650 0.09888 .
## sp_ptbBM52 3.029e+00 2.844e+00 1.065 0.28699
## sp_ptbBM53 3.807e+00 2.770e+00 1.374 0.16929
## sp_ptbBM54 3.016e+00 2.565e+00 1.175 0.23980
## sp_ptbBM55 2.501e+00 2.403e+00 1.041 0.29810
## sp_ptbBM56 3.058e+00 2.330e+00 1.312 0.18942
## sp_ptbBM57 2.722e+00 2.158e+00 1.261 0.20716
## sp_ptbBM58 2.758e+00 2.027e+00 1.361 0.17364
## sp_ptbBM59 1.688e+00 2.051e+00 0.823 0.41072
## sp_ptbBM60 1.846e+00 1.977e+00 0.934 0.35032
## sp_ptbBM61 2.113e+00 1.944e+00 1.087 0.27712
## sp_ptbBM62 1.696e+00 1.994e+00 0.850 0.39511
## sp_ptbBM63 1.572e+00 1.931e+00 0.814 0.41549
## sp_ptbBM64 2.430e+00 1.770e+00 1.372 0.16991
## sp_ptbBM65 3.176e+00 1.542e+00 2.060 0.03945 *
## sp_ptbBM66 1.672e+00 1.246e+00 1.341 0.17976
## sp_ptbBM67 2.029e+00 1.033e+00 1.965 0.04939 *
## sp_ptbBM68 9.479e-01 1.037e+00 0.914 0.36080
## sp_ptbBM69 9.102e-01 1.128e+00 0.807 0.41958
## sp_ptbBM70 -4.613e-01 1.425e+00 -0.324 0.74616
## sp_ptbBM71 -4.909e-01 1.685e+00 -0.291 0.77078
## sp_ptbBM72 1.770e+00 1.926e+00 0.919 0.35812
## sp_ptbBM73 1.487e+00 1.885e+00 0.789 0.42997
## sp_ptbBM74 2.886e+00 1.949e+00 1.481 0.13867
## sp_ptbBM75 1.981e+00 1.902e+00 1.042 0.29759
## sp_ptbBM76 2.228e+00 1.719e+00 1.296 0.19499
## sp_ptbBM77 1.631e+00 1.612e+00 1.012 0.31150
## sp_ptbBM78 1.670e+00 1.497e+00 1.115 0.26468
## sp_ptbBM79 1.846e+00 1.517e+00 1.217 0.22357
## sp_ptbBM80 1.421e+00 1.623e+00 0.876 0.38121
## sp_ptbBM81 1.427e+00 1.609e+00 0.887 0.37515
## sp_ptbBM82 1.132e+00 1.662e+00 0.681 0.49569
## sp_ptbBM83 1.274e+00 1.604e+00 0.794 0.42708
## sp_ptbBM84 1.740e+00 1.612e+00 1.079 0.28046
## sp_ptbBM85 2.759e-01 1.459e+00 0.189 0.84995
## sp_ptbBM86 2.834e+00 1.327e+00 2.136 0.03270 *
## sp_ptbBM87 1.193e+00 1.215e+00 0.982 0.32624
## sp_ptbBM88 7.450e-01 1.193e+00 0.624 0.53241
## sp_ptbBM89 -7.846e-01 1.351e+00 -0.581 0.56142
## sp_ptbBM90 9.863e-01 1.098e+00 0.898 0.36894
## sp_ptbBM91 4.725e-02 1.351e+00 0.035 0.97211
## sp_ptbBM92 1.109e+00 1.344e+00 0.825 0.40923
## sp_ptbBM93 2.323e+00 1.368e+00 1.698 0.08944 .
## sp_ptbBM94 1.041e+00 1.226e+00 0.849 0.39604
## sp_ptbBM95 1.159e+00 1.099e+00 1.054 0.29179
## sp_ptbBM96 6.652e-01 9.514e-01 0.699 0.48441
## sp_ptbBM97 1.016e+00 1.008e+00 1.007 0.31379
## sp_ptbBM98 -5.431e-01 9.675e-01 -0.561 0.57454
## sp_ptbBM99 -2.960e-01 9.926e-01 -0.298 0.76556
## sp_ptbBM100 8.452e-01 1.028e+00 0.822 0.41121
## sp_ptbBM101 1.650e+00 8.908e-01 1.852 0.06396 .
## sp_ptbBM102 8.980e-01 7.777e-01 1.155 0.24823
## sp_ptbBM103 1.189e-01 7.449e-01 0.160 0.87315
## sp_ptbBM104 1.246e+00 7.899e-01 1.578 0.11463
## sp_ptbBM105 7.432e-01 8.418e-01 0.883 0.37731
## sp_ptbBM106 -7.517e-01 1.117e+00 -0.673 0.50088
## sp_ptbBM107 6.143e-01 2.314e+00 0.265 0.79066
## sp_ptbBM108 -1.414e-01 1.697e+00 -0.083 0.93357
## sp_ptbBM109 -1.755e+00 1.922e+00 -0.913 0.36101
## sp_ptbBM110 -2.185e+00 2.046e+00 -1.068 0.28552
## sp_ptbBM111 -2.549e+00 1.879e+00 -1.356 0.17500
## sp_ptbBM112 -2.549e+00 1.507e+00 -1.692 0.09068 .
## sp_ptbBM113 -1.995e+00 1.795e+00 -1.111 0.26653
## sp_ptbBM114 1.383e+00 9.734e-01 1.421 0.15531
## sp_ptbBM115 -7.118e-01 8.234e-01 -0.864 0.38732
## sp_ptbBM116 2.296e+00 8.302e-01 2.766 0.00567 **
## sp_ptbBM117 -1.880e+00 7.918e-01 -2.375 0.01757 *
## sp_ptbBM118 2.008e+00 7.564e-01 2.655 0.00794 **
## sp_ptbBM119 -2.215e+00 8.661e-01 -2.557 0.01056 *
## sp_ptbBM120 -2.270e-01 8.858e-01 -0.256 0.79778
## sp_ptbBM121 1.533e-01 8.212e-01 0.187 0.85189
## sp_ptbBM122 9.081e-01 6.757e-01 1.344 0.17897
## sp_ptbBM123 3.315e-01 5.787e-01 0.573 0.56678
## sp_ptbBM124 3.568e-01 7.566e-01 0.472 0.63722
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22297.04) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 939.93 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3230.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22297
## Std. Err.: 142938
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2972.79m9a <- glm.nb(ptbBM ~ cb9.minT + sp_ptbBM,data=week); summary(m9a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBM, data = week, init.theta = 22708.48563,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.44984 -0.78014 -0.08793 0.55896 2.66282
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.322e+00 1.044e+01 -0.318 0.75034
## cb9.minTv1.l1 1.134e-01 2.555e-01 0.444 0.65721
## cb9.minTv1.l2 -1.702e-01 1.800e-01 -0.945 0.34455
## cb9.minTv2.l1 8.252e-01 8.811e-01 0.937 0.34897
## cb9.minTv2.l2 -5.810e-01 6.655e-01 -0.873 0.38262
## cb9.minTv3.l1 9.618e-01 4.965e-01 1.937 0.05275 .
## cb9.minTv3.l2 3.776e-01 3.596e-01 1.050 0.29368
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.435e+06 2.224e+06 -0.645 0.51866
## sp_ptbBM5 1.492e+01 1.183e+01 1.262 0.20704
## sp_ptbBM6 -2.680e+00 1.973e+00 -1.358 0.17446
## sp_ptbBM7 6.651e-01 1.121e+00 0.594 0.55284
## sp_ptbBM8 -1.772e+00 1.063e+00 -1.667 0.09549 .
## sp_ptbBM9 6.644e-01 9.941e-01 0.668 0.50392
## sp_ptbBM10 -3.213e+00 1.268e+00 -2.534 0.01128 *
## sp_ptbBM11 6.634e-01 8.838e-01 0.751 0.45286
## sp_ptbBM12 -1.021e+00 9.257e-01 -1.103 0.27023
## sp_ptbBM13 1.666e-01 8.768e-01 0.190 0.84928
## sp_ptbBM14 -1.553e+00 9.743e-01 -1.594 0.11101
## sp_ptbBM15 -1.366e-01 9.026e-01 -0.151 0.87967
## sp_ptbBM16 -1.149e+00 1.099e+00 -1.045 0.29607
## sp_ptbBM17 -5.154e-01 9.746e-01 -0.529 0.59689
## sp_ptbBM18 -1.138e+00 1.057e+00 -1.078 0.28124
## sp_ptbBM19 -1.443e+00 1.099e+00 -1.313 0.18919
## sp_ptbBM20 -1.782e+00 1.066e+00 -1.671 0.09474 .
## sp_ptbBM21 -6.449e-01 9.245e-01 -0.698 0.48547
## sp_ptbBM22 -1.005e+00 9.002e-01 -1.117 0.26416
## sp_ptbBM23 -1.045e+00 9.345e-01 -1.118 0.26355
## sp_ptbBM24 -1.940e-01 8.407e-01 -0.231 0.81748
## sp_ptbBM25 -5.120e-01 7.572e-01 -0.676 0.49889
## sp_ptbBM26 -2.764e-01 8.161e-01 -0.339 0.73490
## sp_ptbBM27 -2.206e+00 1.058e+00 -2.085 0.03709 *
## sp_ptbBM28 -4.163e-01 9.248e-01 -0.450 0.65262
## sp_ptbBM29 4.159e-01 7.380e-01 0.564 0.57304
## sp_ptbBM30 -1.018e-01 8.372e-01 -0.122 0.90317
## sp_ptbBM31 9.503e-01 8.883e-01 1.070 0.28470
## sp_ptbBM32 -3.709e-01 9.229e-01 -0.402 0.68779
## sp_ptbBM33 -1.055e-01 1.014e+00 -0.104 0.91716
## sp_ptbBM34 8.521e-01 1.394e+00 0.611 0.54107
## sp_ptbBM35 8.474e-01 1.417e+00 0.598 0.54986
## sp_ptbBM36 2.490e+00 1.624e+00 1.533 0.12517
## sp_ptbBM37 1.004e+00 1.983e+00 0.506 0.61261
## sp_ptbBM38 1.911e+00 1.878e+00 1.018 0.30883
## sp_ptbBM39 2.122e-01 1.784e+00 0.119 0.90532
## sp_ptbBM40 9.172e-01 1.787e+00 0.513 0.60775
## sp_ptbBM41 -1.716e-01 1.355e+00 -0.127 0.89922
## sp_ptbBM42 -3.244e-01 1.209e+00 -0.268 0.78840
## sp_ptbBM43 7.194e-02 1.121e+00 0.064 0.94885
## sp_ptbBM44 -4.041e-02 7.925e-01 -0.051 0.95934
## sp_ptbBM45 7.014e-01 7.960e-01 0.881 0.37825
## sp_ptbBM46 -1.302e-01 8.519e-01 -0.153 0.87858
## sp_ptbBM47 6.420e-01 7.985e-01 0.804 0.42141
## sp_ptbBM48 -6.717e-02 9.677e-01 -0.069 0.94466
## sp_ptbBM49 1.500e+00 8.535e-01 1.758 0.07877 .
## sp_ptbBM50 1.413e-01 1.007e+00 0.140 0.88837
## sp_ptbBM51 1.944e+00 9.530e-01 2.040 0.04133 *
## sp_ptbBM52 4.737e-01 1.195e+00 0.396 0.69180
## sp_ptbBM53 1.486e+00 1.167e+00 1.273 0.20291
## sp_ptbBM54 6.195e-01 1.194e+00 0.519 0.60397
## sp_ptbBM55 -3.935e-01 1.113e+00 -0.354 0.72353
## sp_ptbBM56 8.898e-01 1.152e+00 0.773 0.43967
## sp_ptbBM57 5.793e-01 9.635e-01 0.601 0.54766
## sp_ptbBM58 5.807e-01 9.589e-01 0.606 0.54479
## sp_ptbBM59 -5.273e-01 8.295e-01 -0.636 0.52500
## sp_ptbBM60 -1.004e-02 7.749e-01 -0.013 0.98967
## sp_ptbBM61 -1.825e-02 7.456e-01 -0.024 0.98047
## sp_ptbBM62 -9.422e-01 8.362e-01 -1.127 0.25982
## sp_ptbBM63 -7.932e-01 8.777e-01 -0.904 0.36617
## sp_ptbBM64 -1.101e+00 1.238e+00 -0.889 0.37386
## sp_ptbBM65 -5.541e-01 1.316e+00 -0.421 0.67375
## sp_ptbBM66 -1.589e+00 1.256e+00 -1.265 0.20574
## sp_ptbBM67 -1.098e+00 1.177e+00 -0.932 0.35117
## sp_ptbBM68 -2.007e+00 1.230e+00 -1.632 0.10276
## sp_ptbBM69 -2.327e+00 1.253e+00 -1.858 0.06317 .
## sp_ptbBM70 -3.010e+00 1.256e+00 -2.396 0.01657 *
## sp_ptbBM71 -1.360e+00 9.716e-01 -1.400 0.16147
## sp_ptbBM72 -3.090e-01 8.510e-01 -0.363 0.71653
## sp_ptbBM73 -4.264e-01 8.201e-01 -0.520 0.60308
## sp_ptbBM74 8.071e-01 7.428e-01 1.087 0.27723
## sp_ptbBM75 9.959e-02 8.366e-01 0.119 0.90524
## sp_ptbBM76 9.570e-02 7.864e-01 0.122 0.90314
## sp_ptbBM77 -1.083e-01 8.202e-01 -0.132 0.89498
## sp_ptbBM78 3.518e-01 8.962e-01 0.393 0.69464
## sp_ptbBM79 1.076e-01 9.084e-01 0.118 0.90573
## sp_ptbBM80 -3.359e-01 8.887e-01 -0.378 0.70549
## sp_ptbBM81 -2.242e-01 8.860e-01 -0.253 0.80021
## sp_ptbBM82 -4.763e-01 9.014e-01 -0.528 0.59725
## sp_ptbBM83 -9.418e-01 9.605e-01 -0.980 0.32687
## sp_ptbBM84 -2.210e-01 1.078e+00 -0.205 0.83758
## sp_ptbBM85 -2.798e+00 1.328e+00 -2.108 0.03504 *
## sp_ptbBM86 1.034e-03 1.326e+00 0.001 0.99938
## sp_ptbBM87 -1.030e+00 1.328e+00 -0.776 0.43794
## sp_ptbBM88 -1.461e+00 1.297e+00 -1.126 0.26005
## sp_ptbBM89 -2.816e+00 1.325e+00 -2.125 0.03355 *
## sp_ptbBM90 -2.019e+00 1.399e+00 -1.443 0.14907
## sp_ptbBM91 -1.832e+00 1.071e+00 -1.711 0.08711 .
## sp_ptbBM92 -3.685e-01 1.001e+00 -0.368 0.71286
## sp_ptbBM93 -6.993e-01 1.146e+00 -0.610 0.54170
## sp_ptbBM94 -8.515e-01 1.197e+00 -0.712 0.47672
## sp_ptbBM95 -1.155e+00 1.185e+00 -0.975 0.32974
## sp_ptbBM96 -8.478e-01 1.157e+00 -0.733 0.46358
## sp_ptbBM97 -2.077e+00 1.328e+00 -1.564 0.11781
## sp_ptbBM98 -2.802e+00 1.392e+00 -2.013 0.04408 *
## sp_ptbBM99 -2.033e+00 1.378e+00 -1.476 0.13995
## sp_ptbBM100 -7.991e-01 1.388e+00 -0.576 0.56484
## sp_ptbBM101 -8.634e-01 1.511e+00 -0.571 0.56778
## sp_ptbBM102 -1.342e+00 1.392e+00 -0.964 0.33526
## sp_ptbBM103 -2.453e+00 1.568e+00 -1.565 0.11769
## sp_ptbBM104 -2.161e+00 1.513e+00 -1.428 0.15327
## sp_ptbBM105 -3.735e+00 2.333e+00 -1.601 0.10939
## sp_ptbBM106 -5.452e+00 3.255e+00 -1.675 0.09400 .
## sp_ptbBM107 -5.587e+00 3.962e+00 -1.410 0.15853
## sp_ptbBM108 -5.357e+00 3.465e+00 -1.546 0.12212
## sp_ptbBM109 -7.613e+00 3.713e+00 -2.050 0.04035 *
## sp_ptbBM110 -8.008e+00 3.537e+00 -2.264 0.02355 *
## sp_ptbBM111 -1.019e+01 3.956e+00 -2.575 0.01001 *
## sp_ptbBM112 -8.838e+00 3.343e+00 -2.644 0.00819 **
## sp_ptbBM113 -5.066e+00 2.196e+00 -2.307 0.02104 *
## sp_ptbBM114 -1.927e+00 1.466e+00 -1.314 0.18881
## sp_ptbBM115 -3.225e+00 1.376e+00 -2.345 0.01903 *
## sp_ptbBM116 4.348e-01 1.312e+00 0.332 0.74022
## sp_ptbBM117 -2.960e+00 1.098e+00 -2.696 0.00703 **
## sp_ptbBM118 7.208e-01 8.860e-01 0.814 0.41591
## sp_ptbBM119 -2.337e+00 9.264e-01 -2.522 0.01166 *
## sp_ptbBM120 1.540e-02 8.160e-01 0.019 0.98494
## sp_ptbBM121 -6.600e-01 7.197e-01 -0.917 0.35913
## sp_ptbBM122 7.531e-01 6.602e-01 1.141 0.25401
## sp_ptbBM123 5.647e-01 7.057e-01 0.800 0.42361
## sp_ptbBM124 9.450e-01 7.674e-01 1.231 0.21817
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22708.49) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 936.01 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3226.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22708
## Std. Err.: 141707
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2968.878m10a <- glm.nb(ptbBM ~ cb10.aveT + sp_ptbBM,data=week); summary(m10a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb10.aveT + sp_ptbBM, data = week, init.theta = 22332.14338,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5132 -0.7805 -0.1094 0.5463 2.6589
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.025e+01 1.493e+01 -1.356 0.17503
## cb10.aveTv1.l1 4.051e-01 3.235e-01 1.252 0.21054
## cb10.aveTv1.l2 -2.523e-01 2.396e-01 -1.053 0.29227
## cb10.aveTv2.l1 1.849e+00 1.184e+00 1.562 0.11838
## cb10.aveTv2.l2 -7.238e-01 9.349e-01 -0.774 0.43877
## cb10.aveTv3.l1 9.154e-01 4.986e-01 1.836 0.06639 .
## cb10.aveTv3.l2 1.515e-01 3.715e-01 0.408 0.68335
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.403e+06 2.229e+06 -0.629 0.52908
## sp_ptbBM5 1.666e+01 1.193e+01 1.397 0.16244
## sp_ptbBM6 -1.362e+00 2.024e+00 -0.673 0.50105
## sp_ptbBM7 2.175e+00 1.373e+00 1.584 0.11319
## sp_ptbBM8 -5.075e-01 1.075e+00 -0.472 0.63692
## sp_ptbBM9 1.555e+00 9.697e-01 1.603 0.10884
## sp_ptbBM10 -1.981e+00 1.067e+00 -1.857 0.06330 .
## sp_ptbBM11 1.885e+00 1.048e+00 1.799 0.07197 .
## sp_ptbBM12 1.831e-01 9.507e-01 0.193 0.84731
## sp_ptbBM13 1.543e+00 9.109e-01 1.694 0.09030 .
## sp_ptbBM14 -4.699e-01 1.076e+00 -0.437 0.66228
## sp_ptbBM15 8.201e-01 1.046e+00 0.784 0.43312
## sp_ptbBM16 -3.206e-01 9.095e-01 -0.353 0.72442
## sp_ptbBM17 9.263e-01 9.644e-01 0.960 0.33682
## sp_ptbBM18 5.282e-01 9.296e-01 0.568 0.56993
## sp_ptbBM19 6.391e-01 9.700e-01 0.659 0.50999
## sp_ptbBM20 4.744e-01 1.032e+00 0.460 0.64581
## sp_ptbBM21 9.069e-01 1.134e+00 0.800 0.42374
## sp_ptbBM22 8.142e-01 1.038e+00 0.785 0.43268
## sp_ptbBM23 1.426e-01 1.060e+00 0.135 0.89298
## sp_ptbBM24 1.109e+00 1.100e+00 1.008 0.31326
## sp_ptbBM25 1.010e+00 1.235e+00 0.818 0.41345
## sp_ptbBM26 1.338e+00 1.196e+00 1.118 0.26342
## sp_ptbBM27 -3.375e-01 1.320e+00 -0.256 0.79825
## sp_ptbBM28 2.923e-01 1.384e+00 0.211 0.83279
## sp_ptbBM29 1.613e+00 1.135e+00 1.422 0.15517
## sp_ptbBM30 8.321e-01 1.037e+00 0.803 0.42223
## sp_ptbBM31 1.760e+00 1.032e+00 1.706 0.08800 .
## sp_ptbBM32 2.952e-01 1.022e+00 0.289 0.77276
## sp_ptbBM33 1.185e-01 1.116e+00 0.106 0.91545
## sp_ptbBM34 1.421e+00 1.154e+00 1.231 0.21822
## sp_ptbBM35 7.580e-01 1.289e+00 0.588 0.55657
## sp_ptbBM36 1.779e+00 1.152e+00 1.544 0.12247
## sp_ptbBM37 8.721e-01 1.348e+00 0.647 0.51773
## sp_ptbBM38 2.075e+00 1.134e+00 1.829 0.06739 .
## sp_ptbBM39 8.258e-01 1.247e+00 0.662 0.50795
## sp_ptbBM40 1.998e+00 1.308e+00 1.528 0.12653
## sp_ptbBM41 8.656e-01 1.154e+00 0.750 0.45333
## sp_ptbBM42 7.088e-01 1.395e+00 0.508 0.61136
## sp_ptbBM43 1.213e+00 1.155e+00 1.051 0.29335
## sp_ptbBM44 9.452e-01 1.010e+00 0.936 0.34937
## sp_ptbBM45 1.632e+00 1.036e+00 1.576 0.11514
## sp_ptbBM46 6.796e-01 1.117e+00 0.609 0.54285
## sp_ptbBM47 1.581e+00 1.119e+00 1.413 0.15756
## sp_ptbBM48 1.070e+00 1.239e+00 0.864 0.38748
## sp_ptbBM49 2.382e+00 1.287e+00 1.851 0.06418 .
## sp_ptbBM50 1.465e+00 1.496e+00 0.979 0.32766
## sp_ptbBM51 3.023e+00 1.298e+00 2.328 0.01989 *
## sp_ptbBM52 1.193e+00 1.388e+00 0.860 0.38993
## sp_ptbBM53 2.064e+00 1.373e+00 1.503 0.13285
## sp_ptbBM54 1.320e+00 1.383e+00 0.955 0.33952
## sp_ptbBM55 5.783e-01 1.362e+00 0.425 0.67106
## sp_ptbBM56 2.113e+00 1.469e+00 1.439 0.15020
## sp_ptbBM57 1.655e+00 1.412e+00 1.173 0.24094
## sp_ptbBM58 1.701e+00 1.047e+00 1.626 0.10404
## sp_ptbBM59 5.446e-01 9.788e-01 0.556 0.57798
## sp_ptbBM60 5.680e-01 8.822e-01 0.644 0.51968
## sp_ptbBM61 4.136e-01 7.678e-01 0.539 0.59008
## sp_ptbBM62 -6.182e-01 8.819e-01 -0.701 0.48331
## sp_ptbBM63 -6.669e-01 9.167e-01 -0.728 0.46691
## sp_ptbBM64 -7.836e-01 1.055e+00 -0.743 0.45774
## sp_ptbBM65 2.075e-01 7.861e-01 0.264 0.79182
## sp_ptbBM66 -1.993e-02 7.496e-01 -0.027 0.97878
## sp_ptbBM67 1.185e+00 7.481e-01 1.583 0.11333
## sp_ptbBM68 4.322e-01 7.588e-01 0.570 0.56894
## sp_ptbBM69 6.696e-01 9.520e-01 0.703 0.48183
## sp_ptbBM70 2.976e-01 1.239e+00 0.240 0.81026
## sp_ptbBM71 6.485e-01 1.401e+00 0.463 0.64335
## sp_ptbBM72 1.444e+00 1.323e+00 1.091 0.27523
## sp_ptbBM73 8.786e-01 1.140e+00 0.771 0.44088
## sp_ptbBM74 2.174e+00 1.064e+00 2.044 0.04095 *
## sp_ptbBM75 9.807e-01 1.115e+00 0.880 0.37906
## sp_ptbBM76 1.158e+00 1.091e+00 1.061 0.28878
## sp_ptbBM77 8.070e-01 9.914e-01 0.814 0.41564
## sp_ptbBM78 1.320e+00 1.242e+00 1.063 0.28787
## sp_ptbBM79 1.051e+00 9.384e-01 1.120 0.26276
## sp_ptbBM80 7.441e-01 9.252e-01 0.804 0.42126
## sp_ptbBM81 1.185e+00 8.639e-01 1.372 0.17011
## sp_ptbBM82 8.388e-01 8.962e-01 0.936 0.34931
## sp_ptbBM83 2.697e-01 9.115e-01 0.296 0.76734
## sp_ptbBM84 8.179e-01 9.837e-01 0.831 0.40570
## sp_ptbBM85 -1.157e+00 1.061e+00 -1.090 0.27553
## sp_ptbBM86 1.294e+00 9.933e-01 1.302 0.19284
## sp_ptbBM87 8.031e-01 9.865e-01 0.814 0.41560
## sp_ptbBM88 9.591e-01 1.097e+00 0.874 0.38207
## sp_ptbBM89 1.723e-01 1.103e+00 0.156 0.87589
## sp_ptbBM90 7.300e-01 1.181e+00 0.618 0.53656
## sp_ptbBM91 2.328e+00 1.674e+00 1.391 0.16429
## sp_ptbBM92 2.260e+00 1.325e+00 1.706 0.08802 .
## sp_ptbBM93 1.802e+00 1.100e+00 1.638 0.10133
## sp_ptbBM94 1.148e+00 1.129e+00 1.017 0.30930
## sp_ptbBM95 1.472e+00 1.083e+00 1.359 0.17427
## sp_ptbBM96 1.726e+00 9.691e-01 1.781 0.07493 .
## sp_ptbBM97 8.238e-01 1.338e+00 0.616 0.53810
## sp_ptbBM98 7.412e-01 1.183e+00 0.626 0.53113
## sp_ptbBM99 8.849e-01 1.096e+00 0.807 0.41960
## sp_ptbBM100 1.525e+00 1.079e+00 1.413 0.15767
## sp_ptbBM101 1.629e+00 8.201e-01 1.986 0.04705 *
## sp_ptbBM102 1.523e+00 7.921e-01 1.922 0.05459 .
## sp_ptbBM103 7.846e-01 7.591e-01 1.034 0.30134
## sp_ptbBM104 5.740e-01 6.873e-01 0.835 0.40361
## sp_ptbBM105 3.525e-01 8.344e-01 0.422 0.67272
## sp_ptbBM106 -5.470e-01 8.674e-01 -0.631 0.52827
## sp_ptbBM107 -1.138e+00 1.347e+00 -0.844 0.39850
## sp_ptbBM108 1.147e-01 9.416e-01 0.122 0.90308
## sp_ptbBM109 -1.019e+00 1.207e+00 -0.844 0.39842
## sp_ptbBM110 -6.913e-01 1.076e+00 -0.643 0.52039
## sp_ptbBM111 -1.777e+00 1.225e+00 -1.450 0.14708
## sp_ptbBM112 -1.976e+00 1.226e+00 -1.611 0.10711
## sp_ptbBM113 -1.567e+00 1.526e+00 -1.027 0.30443
## sp_ptbBM114 1.017e+00 9.838e-01 1.034 0.30111
## sp_ptbBM115 -9.186e-01 8.940e-01 -1.027 0.30420
## sp_ptbBM116 2.852e+00 1.073e+00 2.658 0.00787 **
## sp_ptbBM117 -7.323e-01 1.037e+00 -0.706 0.48002
## sp_ptbBM118 2.758e+00 9.405e-01 2.933 0.00336 **
## sp_ptbBM119 -6.914e-01 1.156e+00 -0.598 0.54969
## sp_ptbBM120 1.891e+00 1.056e+00 1.791 0.07337 .
## sp_ptbBM121 3.492e-01 8.983e-01 0.389 0.69751
## sp_ptbBM122 1.551e+00 7.666e-01 2.023 0.04308 *
## sp_ptbBM123 1.076e+00 6.994e-01 1.539 0.12391
## sp_ptbBM124 7.855e-01 7.443e-01 1.055 0.29129
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22332.14) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 939.56 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3230.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22332
## Std. Err.: 143777
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2972.426m11a <- glm.nb(ptbBM ~ cb11.maxT + sp_ptbBM,data=week); summary(m11a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb11.maxT + sp_ptbBM, data = week, init.theta = 22990.3603,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5724 -0.7694 -0.1141 0.5305 2.6610
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.927e+01 1.661e+01 -2.364 0.01810 *
## cb11.maxTv1.l1 6.780e-01 3.675e-01 1.845 0.06506 .
## cb11.maxTv1.l2 1.270e-01 2.676e-01 0.475 0.63511
## cb11.maxTv2.l1 3.430e+00 1.298e+00 2.643 0.00823 **
## cb11.maxTv2.l2 -2.211e-01 9.834e-01 -0.225 0.82211
## cb11.maxTv3.l1 1.411e+00 5.124e-01 2.753 0.00591 **
## cb11.maxTv3.l2 1.290e-01 3.722e-01 0.347 0.72885
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.141e+06 2.254e+06 -0.506 0.61273
## sp_ptbBM5 1.444e+01 1.195e+01 1.209 0.22684
## sp_ptbBM6 -2.050e+00 2.011e+00 -1.020 0.30782
## sp_ptbBM7 7.078e-01 1.458e+00 0.485 0.62732
## sp_ptbBM8 -1.487e+00 1.165e+00 -1.277 0.20166
## sp_ptbBM9 1.337e+00 9.464e-01 1.412 0.15781
## sp_ptbBM10 -2.386e+00 9.973e-01 -2.393 0.01673 *
## sp_ptbBM11 1.927e+00 8.770e-01 2.197 0.02802 *
## sp_ptbBM12 2.034e-01 7.342e-01 0.277 0.78179
## sp_ptbBM13 1.050e+00 7.158e-01 1.467 0.14235
## sp_ptbBM14 -1.842e+00 1.159e+00 -1.589 0.11199
## sp_ptbBM15 -2.794e-01 1.131e+00 -0.247 0.80486
## sp_ptbBM16 -1.455e+00 1.052e+00 -1.383 0.16675
## sp_ptbBM17 -4.375e-01 9.817e-01 -0.446 0.65584
## sp_ptbBM18 -1.111e-01 9.820e-01 -0.113 0.90990
## sp_ptbBM19 -3.372e-01 9.850e-01 -0.342 0.73212
## sp_ptbBM20 -8.118e-01 1.008e+00 -0.805 0.42071
## sp_ptbBM21 -4.604e-01 1.211e+00 -0.380 0.70390
## sp_ptbBM22 -3.476e-01 1.184e+00 -0.294 0.76902
## sp_ptbBM23 -1.076e+00 1.077e+00 -0.998 0.31817
## sp_ptbBM24 -2.464e-02 9.187e-01 -0.027 0.97860
## sp_ptbBM25 3.023e-01 9.871e-01 0.306 0.75940
## sp_ptbBM26 4.731e-01 9.490e-01 0.499 0.61808
## sp_ptbBM27 -8.863e-01 1.080e+00 -0.821 0.41169
## sp_ptbBM28 -1.327e+00 1.218e+00 -1.089 0.27617
## sp_ptbBM29 2.521e-01 1.007e+00 0.250 0.80239
## sp_ptbBM30 -4.568e-01 8.495e-01 -0.538 0.59077
## sp_ptbBM31 1.325e+00 8.922e-01 1.485 0.13747
## sp_ptbBM32 -3.532e-01 8.605e-01 -0.411 0.68144
## sp_ptbBM33 -2.518e-01 9.112e-01 -0.276 0.78225
## sp_ptbBM34 1.250e-02 8.510e-01 0.015 0.98828
## sp_ptbBM35 -1.632e+00 1.273e+00 -1.282 0.19990
## sp_ptbBM36 -3.990e-02 1.010e+00 -0.040 0.96848
## sp_ptbBM37 -8.901e-01 1.078e+00 -0.826 0.40884
## sp_ptbBM38 7.230e-01 8.157e-01 0.886 0.37548
## sp_ptbBM39 -2.468e-01 9.104e-01 -0.271 0.78632
## sp_ptbBM40 7.597e-01 8.899e-01 0.854 0.39327
## sp_ptbBM41 -1.197e-01 9.101e-01 -0.131 0.89538
## sp_ptbBM42 -1.091e+00 1.261e+00 -0.865 0.38683
## sp_ptbBM43 -9.571e-02 1.022e+00 -0.094 0.92537
## sp_ptbBM44 1.462e-01 9.080e-01 0.161 0.87205
## sp_ptbBM45 7.944e-01 8.657e-01 0.918 0.35881
## sp_ptbBM46 -8.224e-02 8.277e-01 -0.099 0.92086
## sp_ptbBM47 5.978e-02 9.273e-01 0.064 0.94859
## sp_ptbBM48 -1.873e-02 8.600e-01 -0.022 0.98263
## sp_ptbBM49 1.351e-01 1.170e+00 0.115 0.90811
## sp_ptbBM50 -6.852e-01 1.167e+00 -0.587 0.55694
## sp_ptbBM51 1.453e+00 8.693e-01 1.671 0.09463 .
## sp_ptbBM52 1.239e-01 9.056e-01 0.137 0.89119
## sp_ptbBM53 1.695e+00 9.268e-01 1.829 0.06747 .
## sp_ptbBM54 8.481e-01 9.193e-01 0.923 0.35622
## sp_ptbBM55 2.127e-01 1.045e+00 0.204 0.83865
## sp_ptbBM56 3.324e-01 1.178e+00 0.282 0.77780
## sp_ptbBM57 -2.307e-01 1.211e+00 -0.191 0.84885
## sp_ptbBM58 3.165e-02 8.707e-01 0.036 0.97101
## sp_ptbBM59 -1.638e+00 1.096e+00 -1.494 0.13505
## sp_ptbBM60 -1.278e+00 1.198e+00 -1.067 0.28608
## sp_ptbBM61 -1.002e+00 1.065e+00 -0.941 0.34695
## sp_ptbBM62 -1.873e+00 1.101e+00 -1.701 0.08889 .
## sp_ptbBM63 -3.045e+00 1.324e+00 -2.299 0.02151 *
## sp_ptbBM64 -2.307e+00 1.489e+00 -1.549 0.12129
## sp_ptbBM65 -1.394e+00 1.114e+00 -1.251 0.21076
## sp_ptbBM66 -9.563e-01 9.182e-01 -1.042 0.29764
## sp_ptbBM67 6.262e-01 8.422e-01 0.744 0.45715
## sp_ptbBM68 7.719e-01 8.538e-01 0.904 0.36596
## sp_ptbBM69 2.421e-01 9.843e-01 0.246 0.80573
## sp_ptbBM70 -7.056e-01 1.361e+00 -0.518 0.60428
## sp_ptbBM71 -8.851e-01 1.296e+00 -0.683 0.49476
## sp_ptbBM72 2.684e-01 1.186e+00 0.226 0.82092
## sp_ptbBM73 -7.091e-02 9.866e-01 -0.072 0.94270
## sp_ptbBM74 1.759e+00 8.527e-01 2.062 0.03916 *
## sp_ptbBM75 9.081e-01 8.504e-01 1.068 0.28555
## sp_ptbBM76 1.353e+00 8.726e-01 1.551 0.12099
## sp_ptbBM77 -2.778e-01 9.236e-01 -0.301 0.76358
## sp_ptbBM78 9.471e-04 1.187e+00 0.001 0.99936
## sp_ptbBM79 4.206e-02 9.729e-01 0.043 0.96552
## sp_ptbBM80 -7.511e-01 9.118e-01 -0.824 0.41010
## sp_ptbBM81 2.261e-01 8.441e-01 0.268 0.78882
## sp_ptbBM82 -1.292e-01 8.069e-01 -0.160 0.87282
## sp_ptbBM83 -6.957e-01 8.615e-01 -0.807 0.41941
## sp_ptbBM84 -5.812e-01 1.107e+00 -0.525 0.59955
## sp_ptbBM85 -2.320e+00 1.170e+00 -1.982 0.04742 *
## sp_ptbBM86 4.992e-01 8.925e-01 0.559 0.57598
## sp_ptbBM87 1.305e-02 8.642e-01 0.015 0.98795
## sp_ptbBM88 4.482e-01 1.008e+00 0.445 0.65655
## sp_ptbBM89 -1.461e-01 9.804e-01 -0.149 0.88150
## sp_ptbBM90 -1.434e-01 1.078e+00 -0.133 0.89413
## sp_ptbBM91 2.105e+00 2.001e+00 1.052 0.29294
## sp_ptbBM92 1.730e+00 1.542e+00 1.122 0.26181
## sp_ptbBM93 2.005e+00 1.408e+00 1.424 0.15436
## sp_ptbBM94 1.641e+00 1.249e+00 1.313 0.18904
## sp_ptbBM95 2.674e+00 1.313e+00 2.036 0.04177 *
## sp_ptbBM96 2.673e+00 1.206e+00 2.216 0.02670 *
## sp_ptbBM97 2.817e+00 1.726e+00 1.632 0.10262
## sp_ptbBM98 5.097e-01 1.507e+00 0.338 0.73519
## sp_ptbBM99 1.199e+00 1.340e+00 0.894 0.37116
## sp_ptbBM100 1.588e+00 1.094e+00 1.452 0.14640
## sp_ptbBM101 2.171e+00 1.003e+00 2.166 0.03032 *
## sp_ptbBM102 2.457e+00 9.854e-01 2.493 0.01265 *
## sp_ptbBM103 1.804e+00 8.997e-01 2.005 0.04494 *
## sp_ptbBM104 1.930e+00 9.840e-01 1.961 0.04986 *
## sp_ptbBM105 2.359e-01 9.815e-01 0.240 0.81008
## sp_ptbBM106 -5.736e-01 1.164e+00 -0.493 0.62221
## sp_ptbBM107 -4.748e-01 8.228e-01 -0.577 0.56392
## sp_ptbBM108 -2.046e-03 9.031e-01 -0.002 0.99819
## sp_ptbBM109 -9.262e-01 8.734e-01 -1.060 0.28894
## sp_ptbBM110 1.591e-01 7.184e-01 0.221 0.82477
## sp_ptbBM111 -9.470e-01 8.020e-01 -1.181 0.23765
## sp_ptbBM112 -1.599e+00 9.273e-01 -1.724 0.08469 .
## sp_ptbBM113 -1.345e+00 1.208e+00 -1.113 0.26569
## sp_ptbBM114 1.504e-01 9.088e-01 0.166 0.86851
## sp_ptbBM115 -1.682e+00 9.551e-01 -1.761 0.07831 .
## sp_ptbBM116 2.015e+00 9.586e-01 2.102 0.03556 *
## sp_ptbBM117 -1.401e+00 1.074e+00 -1.305 0.19202
## sp_ptbBM118 2.130e+00 9.095e-01 2.342 0.01918 *
## sp_ptbBM119 -1.381e+00 1.189e+00 -1.162 0.24527
## sp_ptbBM120 9.602e-01 1.184e+00 0.811 0.41749
## sp_ptbBM121 -1.235e-01 9.603e-01 -0.129 0.89769
## sp_ptbBM122 9.804e-01 8.353e-01 1.174 0.24056
## sp_ptbBM123 4.186e-01 7.143e-01 0.586 0.55782
## sp_ptbBM124 6.543e-01 7.456e-01 0.878 0.38015
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22990.36) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 935.29 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3226.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22990
## Std. Err.: 147953
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2968.147##to check model diag for univariate models
options(na.action="na.exclude")
library(dplyr) ##make sure lags are dplyr lags
##for m1a avgWindSp ######
scatter.smooth(predict(m1a, type='response'), rstandard(m1a, type='deviance'), col='gray')
m1a.resid<-residuals(m1a, type="deviance")
m1a.pred<-predict(m1a, type="response")
length(m1a.resid); length(m1a.pred)## [1] 939## [1] 939pacf(m1a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 $ 25
library(dplyr)
#ensure that the lags are dplyr lags
m1a.ac<-update(m1a,.~.+lag(m1a.resid,1)+lag(m1a.resid,2)+lag(m1a.resid,3)+lag(m1a.resid,4)+
lag(m1a.resid,5)+lag(m1a.resid,6)+lag(m1a.resid,7)+lag(m1a.resid,8)+
lag(m1a.resid,9)+lag(m1a.resid,10)+lag(m1a.resid,11)+lag(m1a.resid,12)+
lag(m1a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedm1a.resid_ac<-residuals(m1a.ac, type="deviance")
m1a.pred_ac<-predict(m1a.ac, type="response")
pacf(m1a.resid_ac,na.action = na.omit) 
length(m1a.pred_ac)## [1] 939length(m1a.resid_ac)## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m1a.pred,lwd=1, col="blue")
plot(week$time,m1a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m1a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m1a.pred_ac,lwd=1, col="blue")
plot(week$time,m1a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m1a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m1a <- crosspred(cb1.avgWindSp, m1a.ac, cen = 4.5, by=0.1,cumul=TRUE)
##for m2a sun ######
summary(m2a)##
## Call:
## glm.nb(formula = ptbBM ~ cb2.sun + sp_ptbBM, data = week, init.theta = 22834.64112,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3950 -0.7776 -0.1304 0.5625 2.5851
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.546e+00 9.030e+00 -0.946 0.34393
## cb2.sunv1.l1 2.457e-01 1.946e-01 1.263 0.20665
## cb2.sunv1.l2 -3.919e-02 1.398e-01 -0.280 0.77914
## cb2.sunv2.l1 8.866e-01 7.914e-01 1.120 0.26258
## cb2.sunv2.l2 4.458e-01 5.431e-01 0.821 0.41176
## cb2.sunv3.l1 6.421e-01 3.004e-01 2.138 0.03252 *
## cb2.sunv3.l2 3.638e-01 2.067e-01 1.760 0.07835 .
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.457e+06 2.237e+06 -0.651 0.51483
## sp_ptbBM5 1.637e+01 1.203e+01 1.360 0.17381
## sp_ptbBM6 -2.509e+00 1.994e+00 -1.258 0.20829
## sp_ptbBM7 1.398e+00 1.056e+00 1.324 0.18551
## sp_ptbBM8 -1.246e+00 9.158e-01 -1.361 0.17351
## sp_ptbBM9 5.623e-01 8.071e-01 0.697 0.48594
## sp_ptbBM10 -3.464e+00 1.064e+00 -3.255 0.00113 **
## sp_ptbBM11 5.633e-01 7.594e-01 0.742 0.45826
## sp_ptbBM12 -1.134e+00 8.704e-01 -1.303 0.19265
## sp_ptbBM13 -7.862e-02 8.713e-01 -0.090 0.92810
## sp_ptbBM14 -1.852e+00 9.216e-01 -2.009 0.04453 *
## sp_ptbBM15 -7.575e-01 8.831e-01 -0.858 0.39106
## sp_ptbBM16 -1.463e+00 8.951e-01 -1.634 0.10220
## sp_ptbBM17 -1.044e+00 8.743e-01 -1.195 0.23227
## sp_ptbBM18 -1.006e+00 8.871e-01 -1.134 0.25667
## sp_ptbBM19 -1.196e+00 8.992e-01 -1.330 0.18353
## sp_ptbBM20 -6.762e-01 8.443e-01 -0.801 0.42323
## sp_ptbBM21 -2.532e-01 9.144e-01 -0.277 0.78190
## sp_ptbBM22 3.135e-01 8.292e-01 0.378 0.70540
## sp_ptbBM23 -8.043e-01 9.396e-01 -0.856 0.39203
## sp_ptbBM24 3.883e-02 8.417e-01 0.046 0.96320
## sp_ptbBM25 -3.668e-01 8.824e-01 -0.416 0.67763
## sp_ptbBM26 -3.520e-01 9.480e-01 -0.371 0.71040
## sp_ptbBM27 -1.912e+00 1.114e+00 -1.716 0.08615 .
## sp_ptbBM28 -1.796e+00 1.003e+00 -1.791 0.07333 .
## sp_ptbBM29 -7.802e-01 9.680e-01 -0.806 0.42024
## sp_ptbBM30 -1.072e+00 9.040e-01 -1.186 0.23548
## sp_ptbBM31 -2.243e-01 9.020e-01 -0.249 0.80357
## sp_ptbBM32 -1.667e+00 9.940e-01 -1.677 0.09348 .
## sp_ptbBM33 -1.202e+00 9.786e-01 -1.228 0.21937
## sp_ptbBM34 -5.696e-02 9.585e-01 -0.059 0.95261
## sp_ptbBM35 -1.568e-01 9.818e-01 -0.160 0.87315
## sp_ptbBM36 1.475e+00 9.497e-01 1.553 0.12035
## sp_ptbBM37 -9.219e-02 9.471e-01 -0.097 0.92246
## sp_ptbBM38 1.435e+00 9.198e-01 1.560 0.11881
## sp_ptbBM39 6.036e-02 9.284e-01 0.065 0.94816
## sp_ptbBM40 1.603e+00 1.009e+00 1.589 0.11202
## sp_ptbBM41 1.325e-01 9.037e-01 0.147 0.88343
## sp_ptbBM42 -1.545e-01 1.040e+00 -0.149 0.88184
## sp_ptbBM43 5.178e-02 8.486e-01 0.061 0.95134
## sp_ptbBM44 -1.632e-01 7.695e-01 -0.212 0.83200
## sp_ptbBM45 2.200e-01 7.278e-01 0.302 0.76247
## sp_ptbBM46 -1.177e+00 7.989e-01 -1.474 0.14049
## sp_ptbBM47 -6.994e-01 8.749e-01 -0.799 0.42405
## sp_ptbBM48 -3.204e-01 7.665e-01 -0.418 0.67595
## sp_ptbBM49 -6.950e-01 1.009e+00 -0.689 0.49110
## sp_ptbBM50 -6.352e-01 8.211e-01 -0.774 0.43913
## sp_ptbBM51 7.741e-01 8.400e-01 0.922 0.35676
## sp_ptbBM52 -6.493e-01 7.781e-01 -0.834 0.40403
## sp_ptbBM53 5.614e-01 7.589e-01 0.740 0.45951
## sp_ptbBM54 -4.564e-01 7.692e-01 -0.593 0.55301
## sp_ptbBM55 -6.105e-01 8.041e-01 -0.759 0.44775
## sp_ptbBM56 1.619e-01 9.410e-01 0.172 0.86342
## sp_ptbBM57 -8.778e-02 9.089e-01 -0.097 0.92306
## sp_ptbBM58 -2.205e-01 8.368e-01 -0.264 0.79213
## sp_ptbBM59 -5.513e-01 8.648e-01 -0.637 0.52381
## sp_ptbBM60 -4.463e-01 8.151e-01 -0.548 0.58400
## sp_ptbBM61 6.038e-02 8.739e-01 0.069 0.94492
## sp_ptbBM62 -7.509e-01 8.883e-01 -0.845 0.39796
## sp_ptbBM63 -1.652e+00 1.004e+00 -1.646 0.09983 .
## sp_ptbBM64 -9.097e-01 9.166e-01 -0.993 0.32093
## sp_ptbBM65 -3.562e-01 8.306e-01 -0.429 0.66807
## sp_ptbBM66 -1.210e+00 7.892e-01 -1.533 0.12535
## sp_ptbBM67 -6.703e-02 7.437e-01 -0.090 0.92819
## sp_ptbBM68 -7.037e-01 7.752e-01 -0.908 0.36399
## sp_ptbBM69 -4.101e-01 8.359e-01 -0.491 0.62374
## sp_ptbBM70 -1.026e+00 1.072e+00 -0.957 0.33873
## sp_ptbBM71 3.612e-01 1.025e+00 0.352 0.72451
## sp_ptbBM72 1.082e-02 1.025e+00 0.011 0.99158
## sp_ptbBM73 -1.701e-01 9.775e-01 -0.174 0.86183
## sp_ptbBM74 1.184e+00 9.730e-01 1.217 0.22352
## sp_ptbBM75 2.544e-01 9.061e-01 0.281 0.77887
## sp_ptbBM76 8.757e-01 9.301e-01 0.942 0.34643
## sp_ptbBM77 4.288e-01 9.377e-01 0.457 0.64747
## sp_ptbBM78 6.772e-01 8.961e-01 0.756 0.44982
## sp_ptbBM79 4.560e-01 8.409e-01 0.542 0.58758
## sp_ptbBM80 -4.086e-01 9.208e-01 -0.444 0.65724
## sp_ptbBM81 -4.644e-01 9.277e-01 -0.501 0.61665
## sp_ptbBM82 -4.128e-01 8.162e-01 -0.506 0.61299
## sp_ptbBM83 -9.705e-01 8.609e-01 -1.127 0.25959
## sp_ptbBM84 -6.024e-01 8.662e-01 -0.695 0.48682
## sp_ptbBM85 -2.166e+00 9.572e-01 -2.263 0.02362 *
## sp_ptbBM86 -2.240e-01 9.238e-01 -0.242 0.80844
## sp_ptbBM87 -2.014e-01 8.669e-01 -0.232 0.81630
## sp_ptbBM88 1.377e-01 9.314e-01 0.148 0.88244
## sp_ptbBM89 -9.040e-01 1.044e+00 -0.866 0.38653
## sp_ptbBM90 6.946e-01 1.012e+00 0.686 0.49252
## sp_ptbBM91 -1.368e-02 1.071e+00 -0.013 0.98981
## sp_ptbBM92 9.233e-01 8.623e-01 1.071 0.28427
## sp_ptbBM93 3.977e-01 8.857e-01 0.449 0.65338
## sp_ptbBM94 -3.136e-01 9.116e-01 -0.344 0.73084
## sp_ptbBM95 4.179e-01 8.186e-01 0.511 0.60964
## sp_ptbBM96 1.044e+00 8.015e-01 1.303 0.19270
## sp_ptbBM97 6.812e-01 8.018e-01 0.850 0.39559
## sp_ptbBM98 -1.207e-01 1.121e+00 -0.108 0.91427
## sp_ptbBM99 8.321e-01 8.213e-01 1.013 0.31099
## sp_ptbBM100 1.848e-01 8.628e-01 0.214 0.83037
## sp_ptbBM101 4.602e-01 7.562e-01 0.609 0.54281
## sp_ptbBM102 1.340e-01 7.808e-01 0.172 0.86371
## sp_ptbBM103 -1.872e-01 8.044e-01 -0.233 0.81596
## sp_ptbBM104 -2.805e-01 7.641e-01 -0.367 0.71354
## sp_ptbBM105 -1.058e+00 1.041e+00 -1.017 0.30933
## sp_ptbBM106 -1.323e+00 8.701e-01 -1.521 0.12834
## sp_ptbBM107 -1.375e+00 9.437e-01 -1.457 0.14502
## sp_ptbBM108 -1.208e+00 9.779e-01 -1.235 0.21689
## sp_ptbBM109 -1.722e+00 9.437e-01 -1.825 0.06797 .
## sp_ptbBM110 -8.229e-01 1.071e+00 -0.769 0.44215
## sp_ptbBM111 -1.224e+00 9.663e-01 -1.267 0.20512
## sp_ptbBM112 -1.357e-01 8.136e-01 -0.167 0.86754
## sp_ptbBM113 7.432e-01 8.201e-01 0.906 0.36481
## sp_ptbBM114 9.927e-01 7.565e-01 1.312 0.18944
## sp_ptbBM115 -5.699e-01 8.797e-01 -0.648 0.51705
## sp_ptbBM116 2.203e+00 7.393e-01 2.980 0.00288 **
## sp_ptbBM117 -1.338e+00 8.911e-01 -1.502 0.13317
## sp_ptbBM118 2.195e+00 8.312e-01 2.640 0.00829 **
## sp_ptbBM119 -1.257e+00 1.040e+00 -1.208 0.22707
## sp_ptbBM120 1.083e+00 8.267e-01 1.310 0.19008
## sp_ptbBM121 -2.234e-01 7.989e-01 -0.280 0.77979
## sp_ptbBM122 9.936e-01 7.035e-01 1.412 0.15787
## sp_ptbBM123 1.153e+00 6.239e-01 1.849 0.06450 .
## sp_ptbBM124 9.738e-01 7.797e-01 1.249 0.21168
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22834.64) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 936.71 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3227.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22835
## Std. Err.: 145868
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2969.576scatter.smooth(predict(m2a, type='response'), rstandard(m2a, type='deviance'), col='gray')
m2a.resid<-residuals(m2a, type="deviance")
m2a.pred<-predict(m2a, type="response")
length(m2a.resid); length(m2a.pred)## [1] 939## [1] 939pacf(m2a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m2a.ac<-update(m2a,.~.+lag(m2a.resid,1)+lag(m2a.resid,2)+lag(m2a.resid,3)+lag(m2a.resid,4)+
lag(m2a.resid,5)+lag(m2a.resid,6)+lag(m2a.resid,7)+lag(m2a.resid,8)+
lag(m2a.resid,9)+lag(m2a.resid,10)+lag(m2a.resid,11)+lag(m2a.resid,12)+
+lag(m2a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedm2a.resid_ac<-residuals(m2a.ac, type="deviance")
m2a.pred_ac<-predict(m2a.ac, type="response")
pacf(m2a.resid_ac,na.action = na.omit) 
length(m2a.pred_ac); length(m2a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m2a.pred,lwd=1, col="blue")
plot(week$time,m2a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m2a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m2a.pred_ac,lwd=1, col="blue")
plot(week$time,m2a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m2a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m2a <- crosspred(cb2.sun, m2a.ac, cen = 50.7, by=0.1,cumul=TRUE)
##for m3a RF ######
summary(m3a)##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + sp_ptbBM, data = week, init.theta = 21990.89142,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4107 -0.7737 -0.1194 0.5399 2.5834
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.752e+00 2.639e+00 1.422 0.15504
## cb3.RFv1.l1 1.915e-01 2.570e-01 0.745 0.45630
## cb3.RFv1.l2 1.224e-01 1.783e-01 0.687 0.49217
## cb3.RFv2.l1 -5.456e-01 3.757e-01 -1.452 0.14643
## cb3.RFv2.l2 5.501e-03 2.728e-01 0.020 0.98391
## cb3.RFv3.l1 -6.110e-01 6.007e-01 -1.017 0.30914
## cb3.RFv3.l2 -1.987e-01 4.648e-01 -0.428 0.66895
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.616e+06 2.229e+06 -0.725 0.46848
## sp_ptbBM5 1.612e+01 1.190e+01 1.355 0.17544
## sp_ptbBM6 -2.049e+00 1.982e+00 -1.033 0.30138
## sp_ptbBM7 9.356e-01 1.093e+00 0.856 0.39192
## sp_ptbBM8 -1.422e+00 9.968e-01 -1.426 0.15373
## sp_ptbBM9 9.562e-01 8.875e-01 1.077 0.28129
## sp_ptbBM10 -2.895e+00 1.142e+00 -2.534 0.01126 *
## sp_ptbBM11 7.994e-01 7.989e-01 1.001 0.31702
## sp_ptbBM12 -3.897e-01 8.838e-01 -0.441 0.65921
## sp_ptbBM13 1.382e+00 9.198e-01 1.502 0.13311
## sp_ptbBM14 -1.047e+00 9.248e-01 -1.132 0.25760
## sp_ptbBM15 5.983e-01 8.226e-01 0.727 0.46703
## sp_ptbBM16 -6.894e-01 9.636e-01 -0.715 0.47435
## sp_ptbBM17 3.788e-01 9.024e-01 0.420 0.67463
## sp_ptbBM18 -7.112e-02 8.988e-01 -0.079 0.93693
## sp_ptbBM19 1.107e-01 8.694e-01 0.127 0.89864
## sp_ptbBM20 4.349e-01 1.059e+00 0.411 0.68119
## sp_ptbBM21 7.659e-01 8.498e-01 0.901 0.36745
## sp_ptbBM22 8.331e-01 8.473e-01 0.983 0.32551
## sp_ptbBM23 -3.778e-01 8.960e-01 -0.422 0.67327
## sp_ptbBM24 4.352e-01 9.275e-01 0.469 0.63892
## sp_ptbBM25 2.050e-01 9.075e-01 0.226 0.82126
## sp_ptbBM26 8.037e-01 9.607e-01 0.837 0.40282
## sp_ptbBM27 -8.948e-01 1.155e+00 -0.775 0.43859
## sp_ptbBM28 -6.954e-01 1.052e+00 -0.661 0.50849
## sp_ptbBM29 5.247e-01 9.381e-01 0.559 0.57597
## sp_ptbBM30 -3.687e-01 9.401e-01 -0.392 0.69495
## sp_ptbBM31 8.586e-01 7.953e-01 1.080 0.28034
## sp_ptbBM32 -6.513e-01 8.960e-01 -0.727 0.46733
## sp_ptbBM33 -4.491e-01 9.428e-01 -0.476 0.63383
## sp_ptbBM34 4.711e-01 9.043e-01 0.521 0.60243
## sp_ptbBM35 9.325e-02 8.363e-01 0.112 0.91122
## sp_ptbBM36 1.127e+00 7.265e-01 1.551 0.12084
## sp_ptbBM37 1.013e-02 8.767e-01 0.012 0.99078
## sp_ptbBM38 5.836e-01 7.909e-01 0.738 0.46063
## sp_ptbBM39 -2.152e-01 8.350e-01 -0.258 0.79666
## sp_ptbBM40 5.584e-01 7.986e-01 0.699 0.48443
## sp_ptbBM41 1.921e-01 8.673e-01 0.221 0.82475
## sp_ptbBM42 -3.785e-01 9.546e-01 -0.396 0.69175
## sp_ptbBM43 1.249e-01 8.631e-01 0.145 0.88491
## sp_ptbBM44 4.531e-01 8.361e-01 0.542 0.58785
## sp_ptbBM45 9.719e-01 8.323e-01 1.168 0.24293
## sp_ptbBM46 -9.464e-02 8.405e-01 -0.113 0.91034
## sp_ptbBM47 6.331e-01 7.461e-01 0.848 0.39618
## sp_ptbBM48 3.450e-01 7.933e-01 0.435 0.66361
## sp_ptbBM49 1.191e+00 8.410e-01 1.417 0.15654
## sp_ptbBM50 -8.129e-02 8.628e-01 -0.094 0.92494
## sp_ptbBM51 1.840e+00 7.873e-01 2.337 0.01944 *
## sp_ptbBM52 1.842e-01 8.503e-01 0.217 0.82851
## sp_ptbBM53 8.987e-01 8.019e-01 1.121 0.26238
## sp_ptbBM54 5.042e-01 8.455e-01 0.596 0.55100
## sp_ptbBM55 1.710e-01 8.666e-01 0.197 0.84358
## sp_ptbBM56 9.194e-01 1.221e+00 0.753 0.45149
## sp_ptbBM57 6.278e-01 9.497e-01 0.661 0.50862
## sp_ptbBM58 2.778e-01 9.446e-01 0.294 0.76870
## sp_ptbBM59 -4.618e-01 9.132e-01 -0.506 0.61305
## sp_ptbBM60 -3.055e-02 9.455e-01 -0.032 0.97423
## sp_ptbBM61 4.372e-01 8.599e-01 0.508 0.61113
## sp_ptbBM62 5.010e-01 1.094e+00 0.458 0.64714
## sp_ptbBM63 -3.810e-01 1.238e+00 -0.308 0.75825
## sp_ptbBM64 6.193e-01 1.072e+00 0.578 0.56360
## sp_ptbBM65 7.559e-01 9.347e-01 0.809 0.41867
## sp_ptbBM66 4.820e-01 8.706e-01 0.554 0.57986
## sp_ptbBM67 1.596e+00 9.200e-01 1.735 0.08275 .
## sp_ptbBM68 8.460e-01 7.797e-01 1.085 0.27793
## sp_ptbBM69 1.581e+00 9.879e-01 1.600 0.10963
## sp_ptbBM70 -1.884e-01 1.003e+00 -0.188 0.85098
## sp_ptbBM71 2.578e-01 1.043e+00 0.247 0.80478
## sp_ptbBM72 7.082e-02 9.393e-01 0.075 0.93990
## sp_ptbBM73 -2.614e-01 8.686e-01 -0.301 0.76346
## sp_ptbBM74 8.836e-01 7.025e-01 1.258 0.20847
## sp_ptbBM75 2.695e-01 8.276e-01 0.326 0.74468
## sp_ptbBM76 7.926e-01 8.110e-01 0.977 0.32842
## sp_ptbBM77 5.305e-01 8.175e-01 0.649 0.51634
## sp_ptbBM78 1.336e+00 8.922e-01 1.497 0.13442
## sp_ptbBM79 1.125e+00 9.032e-01 1.245 0.21303
## sp_ptbBM80 4.905e-02 8.660e-01 0.057 0.95483
## sp_ptbBM81 7.175e-01 8.868e-01 0.809 0.41847
## sp_ptbBM82 1.242e-01 7.908e-01 0.157 0.87525
## sp_ptbBM83 1.891e-01 8.299e-01 0.228 0.81975
## sp_ptbBM84 6.184e-01 8.121e-01 0.761 0.44637
## sp_ptbBM85 -9.928e-01 8.856e-01 -1.121 0.26225
## sp_ptbBM86 1.285e+00 7.504e-01 1.712 0.08688 .
## sp_ptbBM87 6.764e-03 7.564e-01 0.009 0.99287
## sp_ptbBM88 4.594e-01 7.689e-01 0.598 0.55017
## sp_ptbBM89 -6.497e-01 8.417e-01 -0.772 0.44016
## sp_ptbBM90 9.597e-01 8.432e-01 1.138 0.25505
## sp_ptbBM91 9.982e-02 9.589e-01 0.104 0.91709
## sp_ptbBM92 5.419e-01 7.827e-01 0.692 0.48871
## sp_ptbBM93 5.026e-01 8.900e-01 0.565 0.57223
## sp_ptbBM94 -1.043e-01 9.093e-01 -0.115 0.90869
## sp_ptbBM95 5.599e-01 8.358e-01 0.670 0.50292
## sp_ptbBM96 8.995e-01 7.030e-01 1.280 0.20072
## sp_ptbBM97 8.022e-01 8.733e-01 0.919 0.35831
## sp_ptbBM98 1.085e-01 1.018e+00 0.107 0.91510
## sp_ptbBM99 7.395e-01 9.092e-01 0.813 0.41603
## sp_ptbBM100 2.023e-02 8.478e-01 0.024 0.98096
## sp_ptbBM101 5.643e-01 7.434e-01 0.759 0.44784
## sp_ptbBM102 1.577e-01 7.853e-01 0.201 0.84083
## sp_ptbBM103 7.282e-02 8.047e-01 0.091 0.92789
## sp_ptbBM104 4.079e-01 8.039e-01 0.507 0.61186
## sp_ptbBM105 1.008e-01 1.036e+00 0.097 0.92251
## sp_ptbBM106 -5.871e-01 9.315e-01 -0.630 0.52852
## sp_ptbBM107 -2.793e-01 8.419e-01 -0.332 0.74007
## sp_ptbBM108 2.943e-01 8.334e-01 0.353 0.72401
## sp_ptbBM109 -2.923e-01 9.176e-01 -0.319 0.75006
## sp_ptbBM110 6.300e-01 7.449e-01 0.846 0.39771
## sp_ptbBM111 -1.888e-03 1.036e+00 -0.002 0.99855
## sp_ptbBM112 -2.997e-01 9.151e-01 -0.327 0.74329
## sp_ptbBM113 8.883e-01 7.691e-01 1.155 0.24809
## sp_ptbBM114 8.528e-01 7.960e-01 1.071 0.28401
## sp_ptbBM115 -7.721e-01 8.698e-01 -0.888 0.37471
## sp_ptbBM116 2.046e+00 7.249e-01 2.822 0.00477 **
## sp_ptbBM117 -1.100e+00 8.341e-01 -1.319 0.18720
## sp_ptbBM118 2.252e+00 8.080e-01 2.787 0.00533 **
## sp_ptbBM119 -1.177e+00 1.044e+00 -1.127 0.25985
## sp_ptbBM120 1.256e+00 7.892e-01 1.591 0.11158
## sp_ptbBM121 -4.664e-01 7.812e-01 -0.597 0.55045
## sp_ptbBM122 8.489e-01 7.344e-01 1.156 0.24774
## sp_ptbBM123 3.753e-02 6.415e-01 0.059 0.95335
## sp_ptbBM124 6.630e-01 7.574e-01 0.875 0.38135
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21990.89) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 943.43 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3234.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21991
## Std. Err.: 143476
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2976.296scatter.smooth(predict(m3a, type='response'), rstandard(m3a, type='deviance'), col='gray')
m3a.resid<-residuals(m3a, type="deviance")
m3a.pred<-predict(m3a, type="response")
length(m3a.resid); length(m3a.pred)## [1] 939## [1] 939pacf(m3a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m3a.ac<-update(m3a,.~.+lag(m3a.resid,1)+lag(m3a.resid,2)+lag(m3a.resid,3)+lag(m3a.resid,4)+
lag(m3a.resid,5)+lag(m3a.resid,6)+ lag(m3a.resid,7)+lag(m3a.resid,8)+
lag(m3a.resid,9)+lag(m3a.resid,10)+lag(m3a.resid,11)+lag(m3a.resid,12)+
lag(m3a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedm3a.resid_ac<-residuals(m3a.ac, type="deviance")
m3a.pred_ac<-predict(m3a.ac, type="response")
pacf(m3a.resid_ac,na.action = na.omit) 
length(m3a.pred_ac); length(m3a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m3a.pred,lwd=1, col="blue")
plot(week$time,m3a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m3a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m3a.pred_ac,lwd=1, col="blue")
plot(week$time,m3a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m3a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices now
pred.m3a <- crosspred(cb3.RF, m3a.ac, cen = 44.9, by=0.1,cumul=TRUE)
##for m5a minRH ######
summary(m5a)##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBM, data = week, init.theta = 22284.16938,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4192 -0.7548 -0.1076 0.5433 2.6199
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.720e+01 1.055e+01 1.630 0.10321
## cb5.minRHv1.l1 -1.483e-01 2.389e-01 -0.621 0.53474
## cb5.minRHv1.l2 6.178e-02 1.595e-01 0.387 0.69851
## cb5.minRHv2.l1 -1.606e+00 9.187e-01 -1.748 0.08039 .
## cb5.minRHv2.l2 -6.450e-01 6.677e-01 -0.966 0.33402
## cb5.minRHv3.l1 -9.477e-01 5.807e-01 -1.632 0.10264
## cb5.minRHv3.l2 -5.744e-01 4.347e-01 -1.321 0.18641
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.475e+06 2.237e+06 -0.659 0.50973
## sp_ptbBM5 1.534e+01 1.190e+01 1.289 0.19734
## sp_ptbBM6 -2.922e+00 2.005e+00 -1.457 0.14499
## sp_ptbBM7 8.026e-01 1.073e+00 0.748 0.45428
## sp_ptbBM8 -1.369e+00 9.932e-01 -1.378 0.16810
## sp_ptbBM9 7.567e-01 9.445e-01 0.801 0.42304
## sp_ptbBM10 -3.122e+00 1.049e+00 -2.975 0.00293 **
## sp_ptbBM11 -2.315e-01 9.566e-01 -0.242 0.80878
## sp_ptbBM12 -1.127e+00 8.206e-01 -1.373 0.16980
## sp_ptbBM13 -3.965e-02 9.349e-01 -0.042 0.96617
## sp_ptbBM14 -1.387e+00 1.083e+00 -1.280 0.20041
## sp_ptbBM15 -1.608e-01 1.137e+00 -0.142 0.88747
## sp_ptbBM16 -1.310e+00 1.286e+00 -1.019 0.30813
## sp_ptbBM17 -6.231e-01 1.068e+00 -0.584 0.55954
## sp_ptbBM18 -5.816e-01 1.079e+00 -0.539 0.58989
## sp_ptbBM19 -4.891e-01 9.940e-01 -0.492 0.62266
## sp_ptbBM20 -3.470e-01 1.101e+00 -0.315 0.75256
## sp_ptbBM21 7.269e-02 1.019e+00 0.071 0.94314
## sp_ptbBM22 3.655e-01 9.295e-01 0.393 0.69415
## sp_ptbBM23 -1.017e+00 9.589e-01 -1.060 0.28905
## sp_ptbBM24 -1.587e-01 8.420e-01 -0.188 0.85051
## sp_ptbBM25 -5.421e-01 7.700e-01 -0.704 0.48142
## sp_ptbBM26 -3.071e-01 8.328e-01 -0.369 0.71228
## sp_ptbBM27 -1.350e+00 9.270e-01 -1.457 0.14525
## sp_ptbBM28 -1.587e+00 1.024e+00 -1.550 0.12115
## sp_ptbBM29 3.401e-01 8.739e-01 0.389 0.69714
## sp_ptbBM30 -1.175e+00 1.003e+00 -1.171 0.24143
## sp_ptbBM31 -1.910e-02 1.060e+00 -0.018 0.98563
## sp_ptbBM32 -2.070e+00 1.369e+00 -1.512 0.13046
## sp_ptbBM33 -1.612e+00 1.269e+00 -1.271 0.20389
## sp_ptbBM34 -1.109e+00 1.354e+00 -0.819 0.41259
## sp_ptbBM35 -1.404e+00 1.536e+00 -0.914 0.36078
## sp_ptbBM36 -1.918e-01 1.551e+00 -0.124 0.90155
## sp_ptbBM37 -1.270e+00 1.351e+00 -0.941 0.34695
## sp_ptbBM38 7.275e-02 1.131e+00 0.064 0.94870
## sp_ptbBM39 -1.053e+00 1.149e+00 -0.916 0.35942
## sp_ptbBM40 -3.307e-02 9.692e-01 -0.034 0.97278
## sp_ptbBM41 -7.771e-01 1.055e+00 -0.737 0.46120
## sp_ptbBM42 -1.345e+00 1.092e+00 -1.232 0.21796
## sp_ptbBM43 -7.441e-01 9.343e-01 -0.796 0.42580
## sp_ptbBM44 -5.176e-01 9.457e-01 -0.547 0.58420
## sp_ptbBM45 -2.657e-01 8.920e-01 -0.298 0.76579
## sp_ptbBM46 -1.256e+00 9.571e-01 -1.312 0.18953
## sp_ptbBM47 -5.985e-01 9.798e-01 -0.611 0.54134
## sp_ptbBM48 -7.957e-01 7.947e-01 -1.001 0.31674
## sp_ptbBM49 -1.857e-01 1.169e+00 -0.159 0.87385
## sp_ptbBM50 -1.433e+00 9.687e-01 -1.479 0.13906
## sp_ptbBM51 2.306e-01 1.067e+00 0.216 0.82893
## sp_ptbBM52 -1.031e+00 9.856e-01 -1.046 0.29577
## sp_ptbBM53 1.796e-01 9.212e-01 0.195 0.84545
## sp_ptbBM54 -4.590e-01 8.971e-01 -0.512 0.60889
## sp_ptbBM55 -4.741e-01 9.946e-01 -0.477 0.63362
## sp_ptbBM56 -2.438e-01 1.079e+00 -0.226 0.82129
## sp_ptbBM57 -1.713e-01 1.049e+00 -0.163 0.87033
## sp_ptbBM58 -3.096e-01 8.906e-01 -0.348 0.72810
## sp_ptbBM59 -1.505e+00 1.076e+00 -1.399 0.16192
## sp_ptbBM60 -8.917e-01 1.070e+00 -0.833 0.40482
## sp_ptbBM61 -7.324e-01 1.055e+00 -0.694 0.48751
## sp_ptbBM62 -6.589e-01 1.071e+00 -0.615 0.53844
## sp_ptbBM63 -1.827e+00 1.311e+00 -1.394 0.16329
## sp_ptbBM64 -9.561e-01 1.415e+00 -0.676 0.49934
## sp_ptbBM65 -9.820e-01 1.136e+00 -0.865 0.38723
## sp_ptbBM66 -7.167e-01 8.801e-01 -0.814 0.41549
## sp_ptbBM67 2.977e-01 7.096e-01 0.420 0.67481
## sp_ptbBM68 -2.656e-01 6.724e-01 -0.395 0.69282
## sp_ptbBM69 4.190e-01 7.596e-01 0.552 0.58120
## sp_ptbBM70 -1.028e+00 1.016e+00 -1.012 0.31151
## sp_ptbBM71 -5.361e-01 9.877e-01 -0.543 0.58730
## sp_ptbBM72 -4.652e-01 9.202e-01 -0.506 0.61316
## sp_ptbBM73 -9.096e-01 8.782e-01 -1.036 0.30034
## sp_ptbBM74 7.332e-01 7.852e-01 0.934 0.35042
## sp_ptbBM75 -5.019e-01 8.481e-01 -0.592 0.55401
## sp_ptbBM76 4.294e-01 7.019e-01 0.612 0.54069
## sp_ptbBM77 -3.389e-01 9.105e-01 -0.372 0.70973
## sp_ptbBM78 7.166e-01 1.030e+00 0.696 0.48666
## sp_ptbBM79 4.498e-01 8.976e-01 0.501 0.61630
## sp_ptbBM80 -8.378e-01 9.603e-01 -0.872 0.38298
## sp_ptbBM81 -2.868e-01 8.403e-01 -0.341 0.73284
## sp_ptbBM82 -5.488e-01 7.643e-01 -0.718 0.47268
## sp_ptbBM83 -9.749e-01 8.511e-01 -1.145 0.25202
## sp_ptbBM84 -3.080e-01 9.544e-01 -0.323 0.74687
## sp_ptbBM85 -1.693e+00 1.099e+00 -1.540 0.12348
## sp_ptbBM86 2.341e-01 1.025e+00 0.228 0.81943
## sp_ptbBM87 -2.274e-01 7.985e-01 -0.285 0.77577
## sp_ptbBM88 -3.292e-01 8.538e-01 -0.386 0.69985
## sp_ptbBM89 -1.217e+00 8.333e-01 -1.460 0.14433
## sp_ptbBM90 1.142e-01 8.765e-01 0.130 0.89638
## sp_ptbBM91 -6.856e-01 1.646e+00 -0.417 0.67694
## sp_ptbBM92 8.631e-01 1.192e+00 0.724 0.46915
## sp_ptbBM93 7.456e-01 1.262e+00 0.591 0.55453
## sp_ptbBM94 3.079e-01 1.090e+00 0.283 0.77747
## sp_ptbBM95 1.341e+00 1.100e+00 1.220 0.22259
## sp_ptbBM96 1.883e+00 1.126e+00 1.673 0.09441 .
## sp_ptbBM97 2.131e+00 1.397e+00 1.525 0.12726
## sp_ptbBM98 -6.762e-01 1.337e+00 -0.506 0.61289
## sp_ptbBM99 9.335e-01 1.155e+00 0.808 0.41905
## sp_ptbBM100 5.583e-01 1.065e+00 0.524 0.60026
## sp_ptbBM101 9.865e-01 9.961e-01 0.990 0.32201
## sp_ptbBM102 1.137e+00 9.564e-01 1.189 0.23459
## sp_ptbBM103 8.712e-01 9.361e-01 0.931 0.35198
## sp_ptbBM104 1.060e+00 8.751e-01 1.212 0.22563
## sp_ptbBM105 -2.148e-01 9.297e-01 -0.231 0.81725
## sp_ptbBM106 2.272e-01 1.001e+00 0.227 0.82039
## sp_ptbBM107 -1.506e-01 7.850e-01 -0.192 0.84787
## sp_ptbBM108 4.777e-01 8.535e-01 0.560 0.57564
## sp_ptbBM109 -1.516e-01 6.680e-01 -0.227 0.82049
## sp_ptbBM110 1.052e+00 6.793e-01 1.549 0.12132
## sp_ptbBM111 5.152e-01 6.970e-01 0.739 0.45980
## sp_ptbBM112 -2.536e-01 7.185e-01 -0.353 0.72410
## sp_ptbBM113 4.126e-01 9.235e-01 0.447 0.65507
## sp_ptbBM114 6.130e-01 8.730e-01 0.702 0.48261
## sp_ptbBM115 -9.407e-01 8.881e-01 -1.059 0.28950
## sp_ptbBM116 1.891e+00 7.500e-01 2.521 0.01171 *
## sp_ptbBM117 -1.613e+00 9.618e-01 -1.677 0.09353 .
## sp_ptbBM118 1.808e+00 7.696e-01 2.349 0.01882 *
## sp_ptbBM119 -1.658e+00 1.074e+00 -1.544 0.12248
## sp_ptbBM120 6.088e-01 9.977e-01 0.610 0.54174
## sp_ptbBM121 -5.461e-01 9.366e-01 -0.583 0.55982
## sp_ptbBM122 7.548e-01 8.127e-01 0.929 0.35305
## sp_ptbBM123 4.695e-02 6.455e-01 0.073 0.94201
## sp_ptbBM124 8.533e-01 7.540e-01 1.132 0.25778
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22284.17) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 941.58 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3232.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22284
## Std. Err.: 144435
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2974.443scatter.smooth(predict(m5a, type='response'), rstandard(m5a, type='deviance'), col='gray')
m5a.resid<-residuals(m5a, type="deviance")
m5a.pred<-predict(m5a, type="response")
length(m5a.resid); length(m5a.pred)## [1] 939## [1] 939pacf(m5a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
#ensure that the lags are dplyr lags
m5a.ac<-update(m5a,.~.+lag(m5a.resid,1)+lag(m5a.resid,2)+lag(m5a.resid,3)+lag(m5a.resid,4)+
lag(m5a.resid,5)+lag(m5a.resid,6)+lag(m5a.resid,7)+lag(m5a.resid,8)+
lag(m5a.resid,9)+lag(m5a.resid,10)+lag(m5a.resid,11)+lag(m5a.resid,12)+
lag(m5a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m5a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBM + lag(m5a.resid,
## 1) + lag(m5a.resid, 2) + lag(m5a.resid, 3) + lag(m5a.resid,
## 4) + lag(m5a.resid, 5) + lag(m5a.resid, 6) + lag(m5a.resid,
## 7) + lag(m5a.resid, 8) + lag(m5a.resid, 9) + lag(m5a.resid,
## 10) + lag(m5a.resid, 11) + lag(m5a.resid, 12) + lag(m5a.resid,
## 25), data = week, init.theta = 47356.28936, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.48248 -0.66160 -0.03787 0.44756 2.15902
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 8.81195 11.13123 0.792 0.428570
## cb5.minRHv1.l1 0.13802 0.24739 0.558 0.576905
## cb5.minRHv1.l2 0.01879 0.16567 0.113 0.909682
## cb5.minRHv2.l1 -1.10557 0.98274 -1.125 0.260591
## cb5.minRHv2.l2 -1.34375 0.69733 -1.927 0.053980 .
## cb5.minRHv3.l1 -0.91384 0.63426 -1.441 0.149643
## cb5.minRHv3.l2 -0.62593 0.46374 -1.350 0.177096
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -3.70388 43.87493 -0.084 0.932723
## sp_ptbBM9 1.69916 3.97987 0.427 0.669423
## sp_ptbBM10 -2.50660 1.66503 -1.505 0.132211
## sp_ptbBM11 -0.75285 1.04115 -0.723 0.469620
## sp_ptbBM12 -0.52478 0.91855 -0.571 0.567791
## sp_ptbBM13 1.21617 0.97014 1.254 0.209984
## sp_ptbBM14 -1.95224 1.10973 -1.759 0.078542 .
## sp_ptbBM15 2.50187 1.17950 2.121 0.033911 *
## sp_ptbBM16 -1.63335 1.31414 -1.243 0.213904
## sp_ptbBM17 1.14340 1.10301 1.037 0.299911
## sp_ptbBM18 -0.87188 1.12205 -0.777 0.437133
## sp_ptbBM19 1.78708 1.00760 1.774 0.076131 .
## sp_ptbBM20 -1.19640 1.13986 -1.050 0.293900
## sp_ptbBM21 1.48456 1.07823 1.377 0.168558
## sp_ptbBM22 0.75313 0.93743 0.803 0.421743
## sp_ptbBM23 -0.92808 0.95427 -0.973 0.330770
## sp_ptbBM24 0.38605 0.84193 0.459 0.646576
## sp_ptbBM25 -1.58941 0.79623 -1.996 0.045915 *
## sp_ptbBM26 0.24890 0.84859 0.293 0.769290
## sp_ptbBM27 -2.48665 0.86366 -2.879 0.003987 **
## sp_ptbBM28 -1.41449 1.04600 -1.352 0.176284
## sp_ptbBM29 -0.20796 0.90107 -0.231 0.817476
## sp_ptbBM30 0.79690 1.04586 0.762 0.446086
## sp_ptbBM31 0.11274 1.11160 0.101 0.919219
## sp_ptbBM32 0.68058 1.42458 0.478 0.632833
## sp_ptbBM33 -1.92778 1.30432 -1.478 0.139410
## sp_ptbBM34 1.76311 1.40908 1.251 0.210843
## sp_ptbBM35 -1.10882 1.59088 -0.697 0.485810
## sp_ptbBM36 2.02531 1.60464 1.262 0.206892
## sp_ptbBM37 -0.66552 1.41589 -0.470 0.638326
## sp_ptbBM38 0.66419 1.18721 0.559 0.575850
## sp_ptbBM39 0.94306 1.16825 0.807 0.419526
## sp_ptbBM40 -0.24222 1.01812 -0.238 0.811955
## sp_ptbBM41 0.72653 1.05966 0.686 0.492948
## sp_ptbBM42 -3.32323 1.16398 -2.855 0.004303 **
## sp_ptbBM43 1.43184 1.01950 1.404 0.160184
## sp_ptbBM44 -2.56774 0.95925 -2.677 0.007432 **
## sp_ptbBM45 0.99620 0.90691 1.098 0.272009
## sp_ptbBM46 -1.15885 0.97923 -1.183 0.236637
## sp_ptbBM47 0.39199 0.98436 0.398 0.690471
## sp_ptbBM48 -1.29220 0.82009 -1.576 0.115099
## sp_ptbBM49 1.15666 1.20393 0.961 0.336687
## sp_ptbBM50 -0.88039 0.99709 -0.883 0.377260
## sp_ptbBM51 0.72178 1.10279 0.655 0.512786
## sp_ptbBM52 0.06480 1.02375 0.063 0.949527
## sp_ptbBM53 0.96396 0.93048 1.036 0.300211
## sp_ptbBM54 0.89297 0.91229 0.979 0.327668
## sp_ptbBM55 -0.41815 1.03883 -0.403 0.687301
## sp_ptbBM56 0.84611 1.10080 0.769 0.442110
## sp_ptbBM57 1.14448 1.07894 1.061 0.288806
## sp_ptbBM58 0.30959 0.89687 0.345 0.729955
## sp_ptbBM59 -0.27840 1.09853 -0.253 0.799937
## sp_ptbBM60 0.47612 1.08822 0.438 0.661732
## sp_ptbBM61 -0.97452 1.09668 -0.889 0.374212
## sp_ptbBM62 1.51380 1.10503 1.370 0.170714
## sp_ptbBM63 -2.71015 1.36827 -1.981 0.047623 *
## sp_ptbBM64 2.78422 1.50410 1.851 0.064157 .
## sp_ptbBM65 -2.43667 1.15192 -2.115 0.034403 *
## sp_ptbBM66 2.11121 0.92987 2.270 0.023182 *
## sp_ptbBM67 -0.47324 0.71561 -0.661 0.508410
## sp_ptbBM68 0.36378 0.67251 0.541 0.588558
## sp_ptbBM69 0.36529 0.75050 0.487 0.626451
## sp_ptbBM70 0.24289 1.01209 0.240 0.810341
## sp_ptbBM71 -0.92128 1.04944 -0.878 0.380010
## sp_ptbBM72 0.74425 0.92968 0.801 0.423400
## sp_ptbBM73 -3.05036 0.97546 -3.127 0.001765 **
## sp_ptbBM74 2.31705 0.82908 2.795 0.005194 **
## sp_ptbBM75 -3.87424 0.89265 -4.340 1.42e-05 ***
## sp_ptbBM76 2.87181 0.78826 3.643 0.000269 ***
## sp_ptbBM77 -2.44284 0.93850 -2.603 0.009243 **
## sp_ptbBM78 3.08385 1.07028 2.881 0.003960 **
## sp_ptbBM79 0.74242 0.92039 0.807 0.419879
## sp_ptbBM80 1.37296 0.96546 1.422 0.155004
## sp_ptbBM81 -1.77308 0.89962 -1.971 0.048733 *
## sp_ptbBM82 1.82743 0.80554 2.269 0.023294 *
## sp_ptbBM83 -3.19962 0.88792 -3.604 0.000314 ***
## sp_ptbBM84 1.65409 1.01820 1.625 0.104263
## sp_ptbBM85 -2.08471 1.13367 -1.839 0.065929 .
## sp_ptbBM86 0.77550 1.04977 0.739 0.460071
## sp_ptbBM87 0.47275 0.79640 0.594 0.552780
## sp_ptbBM88 -1.01608 0.86739 -1.171 0.241428
## sp_ptbBM89 -1.62543 0.87504 -1.858 0.063231 .
## sp_ptbBM90 -1.83303 0.89153 -2.056 0.039778 *
## sp_ptbBM91 0.37320 1.68810 0.221 0.825031
## sp_ptbBM92 1.40224 1.22709 1.143 0.253149
## sp_ptbBM93 2.01777 1.30001 1.552 0.120632
## sp_ptbBM94 0.44403 1.15702 0.384 0.701148
## sp_ptbBM95 0.66362 1.12769 0.588 0.556210
## sp_ptbBM96 1.43942 1.26878 1.134 0.256589
## sp_ptbBM97 1.95762 1.45576 1.345 0.178710
## sp_ptbBM98 -4.03787 1.38960 -2.906 0.003663 **
## sp_ptbBM99 2.03400 1.23878 1.642 0.100602
## sp_ptbBM100 0.12563 1.08460 0.116 0.907785
## sp_ptbBM101 1.64965 1.05012 1.571 0.116201
## sp_ptbBM102 0.99114 0.95540 1.037 0.299545
## sp_ptbBM103 -0.69498 0.96389 -0.721 0.470901
## sp_ptbBM104 1.50499 0.91682 1.642 0.100685
## sp_ptbBM105 -1.11182 0.97108 -1.145 0.252240
## sp_ptbBM106 1.48426 1.12707 1.317 0.187866
## sp_ptbBM107 -2.03072 0.78671 -2.581 0.009844 **
## sp_ptbBM108 0.81067 0.90330 0.897 0.369477
## sp_ptbBM109 0.43339 0.67915 0.638 0.523389
## sp_ptbBM110 0.83479 0.68928 1.211 0.225852
## sp_ptbBM111 1.58816 0.72923 2.178 0.029417 *
## sp_ptbBM112 -1.02239 0.70243 -1.455 0.145531
## sp_ptbBM113 0.55766 0.93208 0.598 0.549642
## sp_ptbBM114 0.06613 0.88950 0.074 0.940735
## sp_ptbBM115 -0.49457 0.89722 -0.551 0.581483
## sp_ptbBM116 1.23424 0.77960 1.583 0.113380
## sp_ptbBM117 -0.81096 1.00730 -0.805 0.420769
## sp_ptbBM118 0.65055 0.78169 0.832 0.405274
## sp_ptbBM119 -0.58887 1.14102 -0.516 0.605792
## sp_ptbBM120 -1.16750 1.03968 -1.123 0.261463
## sp_ptbBM121 -1.01226 0.97107 -1.042 0.297214
## sp_ptbBM122 0.80515 0.82432 0.977 0.328695
## sp_ptbBM123 -0.54613 0.64971 -0.841 0.400585
## sp_ptbBM124 1.83948 0.77843 2.363 0.018124 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m5a.resid, 1) -0.43721 0.03058 -14.299 < 2e-16 ***
## lag(m5a.resid, 2) -0.48490 0.03548 -13.666 < 2e-16 ***
## lag(m5a.resid, 3) -0.56471 0.03714 -15.204 < 2e-16 ***
## lag(m5a.resid, 4) -0.57563 0.04022 -14.312 < 2e-16 ***
## lag(m5a.resid, 5) -0.58235 0.04054 -14.365 < 2e-16 ***
## lag(m5a.resid, 6) -0.53987 0.04142 -13.034 < 2e-16 ***
## lag(m5a.resid, 7) -0.51042 0.03913 -13.045 < 2e-16 ***
## lag(m5a.resid, 8) -0.45784 0.03764 -12.165 < 2e-16 ***
## lag(m5a.resid, 9) -0.36715 0.03517 -10.440 < 2e-16 ***
## lag(m5a.resid, 10) -0.31511 0.03190 -9.877 < 2e-16 ***
## lag(m5a.resid, 11) -0.21099 0.03103 -6.799 1.06e-11 ***
## lag(m5a.resid, 12) -0.17378 0.02885 -6.023 1.71e-09 ***
## lag(m5a.resid, 25) -0.04625 0.02447 -1.890 0.058744 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(47356.29) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 548.18 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2806.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 47356
## Std. Err.: 185936
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2530.107m5a.resid_ac<-residuals(m5a.ac, type="deviance")
m5a.pred_ac<-predict(m5a.ac, type="response")
pacf(m5a.resid_ac,na.action = na.omit) 
length(m5a.pred_ac); length(m5a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m5a.pred,lwd=1, col="blue")
plot(week$time,m5a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m5a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m5a.pred_ac,lwd=1, col="blue")
plot(week$time,m5a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m5a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m5a <- crosspred(cb5.minRH, m5a.ac, cen = 63, by=0.1,cumul=TRUE)
##for m6a meanRH ######
summary(m6a)##
## Call:
## glm.nb(formula = ptbBM ~ cb6.meanRH + sp_ptbBM, data = week,
## init.theta = 21891.10351, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4463 -0.7881 -0.1323 0.5381 2.6916
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.102e+01 1.250e+01 0.881 0.3782
## cb6.meanRHv1.l1 -2.221e-01 2.861e-01 -0.776 0.4375
## cb6.meanRHv1.l2 -8.627e-02 1.801e-01 -0.479 0.6320
## cb6.meanRHv2.l1 -8.449e-01 1.022e+00 -0.826 0.4086
## cb6.meanRHv2.l2 2.009e-01 6.840e-01 0.294 0.7690
## cb6.meanRHv3.l1 -3.194e-01 4.414e-01 -0.724 0.4693
## cb6.meanRHv3.l2 2.764e-01 3.224e-01 0.857 0.3912
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.338e+06 2.226e+06 -0.601 0.5478
## sp_ptbBM5 1.468e+01 1.182e+01 1.242 0.2143
## sp_ptbBM6 -2.040e+00 2.025e+00 -1.008 0.3136
## sp_ptbBM7 1.444e+00 1.067e+00 1.353 0.1761
## sp_ptbBM8 -1.016e+00 9.766e-01 -1.041 0.2980
## sp_ptbBM9 8.134e-01 9.457e-01 0.860 0.3897
## sp_ptbBM10 -2.611e+00 1.096e+00 -2.383 0.0172 *
## sp_ptbBM11 6.370e-01 8.965e-01 0.711 0.4774
## sp_ptbBM12 -6.359e-01 8.247e-01 -0.771 0.4407
## sp_ptbBM13 9.946e-01 1.071e+00 0.928 0.3532
## sp_ptbBM14 -8.158e-01 1.113e+00 -0.733 0.4637
## sp_ptbBM15 4.559e-01 1.058e+00 0.431 0.6665
## sp_ptbBM16 -9.322e-01 1.239e+00 -0.752 0.4519
## sp_ptbBM17 7.200e-03 1.060e+00 0.007 0.9946
## sp_ptbBM18 -6.874e-02 1.084e+00 -0.063 0.9494
## sp_ptbBM19 3.086e-02 1.051e+00 0.029 0.9766
## sp_ptbBM20 -2.315e-02 1.165e+00 -0.020 0.9841
## sp_ptbBM21 5.344e-01 9.587e-01 0.557 0.5772
## sp_ptbBM22 3.629e-01 1.082e+00 0.336 0.7372
## sp_ptbBM23 -1.443e+00 1.489e+00 -0.969 0.3325
## sp_ptbBM24 -9.378e-01 1.932e+00 -0.485 0.6275
## sp_ptbBM25 -1.867e+00 2.333e+00 -0.800 0.4237
## sp_ptbBM26 -1.606e+00 2.528e+00 -0.635 0.5252
## sp_ptbBM27 -3.267e+00 2.802e+00 -1.166 0.2435
## sp_ptbBM28 -2.738e+00 2.824e+00 -0.970 0.3323
## sp_ptbBM29 -1.317e+00 2.812e+00 -0.469 0.6394
## sp_ptbBM30 -2.278e+00 2.747e+00 -0.829 0.4070
## sp_ptbBM31 -1.257e+00 2.779e+00 -0.452 0.6510
## sp_ptbBM32 -2.636e+00 2.801e+00 -0.941 0.3467
## sp_ptbBM33 -2.544e+00 2.780e+00 -0.915 0.3601
## sp_ptbBM34 -1.812e+00 2.650e+00 -0.684 0.4940
## sp_ptbBM35 -2.420e+00 2.971e+00 -0.815 0.4154
## sp_ptbBM36 -1.017e+00 2.752e+00 -0.370 0.7117
## sp_ptbBM37 -2.062e+00 2.286e+00 -0.902 0.3671
## sp_ptbBM38 -4.145e-01 2.052e+00 -0.202 0.8399
## sp_ptbBM39 -1.486e+00 1.814e+00 -0.819 0.4127
## sp_ptbBM40 -3.092e-02 1.488e+00 -0.021 0.9834
## sp_ptbBM41 -8.306e-01 1.483e+00 -0.560 0.5753
## sp_ptbBM42 -1.078e+00 1.242e+00 -0.868 0.3854
## sp_ptbBM43 -2.583e-01 1.233e+00 -0.209 0.8341
## sp_ptbBM44 -3.630e-01 1.311e+00 -0.277 0.7819
## sp_ptbBM45 1.426e-01 1.393e+00 0.102 0.9185
## sp_ptbBM46 -9.680e-01 1.420e+00 -0.682 0.4955
## sp_ptbBM47 -3.028e-01 1.561e+00 -0.194 0.8461
## sp_ptbBM48 -8.081e-01 1.365e+00 -0.592 0.5538
## sp_ptbBM49 -1.169e-01 1.638e+00 -0.071 0.9431
## sp_ptbBM50 -1.050e+00 1.279e+00 -0.821 0.4116
## sp_ptbBM51 6.503e-01 1.295e+00 0.502 0.6157
## sp_ptbBM52 -6.944e-01 1.138e+00 -0.610 0.5416
## sp_ptbBM53 3.516e-01 1.100e+00 0.320 0.7492
## sp_ptbBM54 7.228e-02 9.999e-01 0.072 0.9424
## sp_ptbBM55 -6.655e-01 9.889e-01 -0.673 0.5010
## sp_ptbBM56 6.644e-01 1.008e+00 0.659 0.5099
## sp_ptbBM57 2.757e-01 1.076e+00 0.256 0.7978
## sp_ptbBM58 1.617e-01 9.816e-01 0.165 0.8692
## sp_ptbBM59 -1.053e+00 1.205e+00 -0.874 0.3823
## sp_ptbBM60 -7.594e-01 1.270e+00 -0.598 0.5500
## sp_ptbBM61 -3.452e-01 1.231e+00 -0.280 0.7792
## sp_ptbBM62 -1.326e+00 1.256e+00 -1.055 0.2912
## sp_ptbBM63 -9.390e-01 1.474e+00 -0.637 0.5242
## sp_ptbBM64 -4.911e-01 1.704e+00 -0.288 0.7732
## sp_ptbBM65 -3.319e-01 1.318e+00 -0.252 0.8011
## sp_ptbBM66 -3.435e-01 1.116e+00 -0.308 0.7583
## sp_ptbBM67 7.328e-01 9.347e-01 0.784 0.4331
## sp_ptbBM68 2.219e-01 8.770e-01 0.253 0.8002
## sp_ptbBM69 5.506e-01 9.250e-01 0.595 0.5517
## sp_ptbBM70 5.472e-02 1.089e+00 0.050 0.9599
## sp_ptbBM71 5.893e-03 1.054e+00 0.006 0.9955
## sp_ptbBM72 5.865e-03 9.392e-01 0.006 0.9950
## sp_ptbBM73 -4.587e-01 8.628e-01 -0.532 0.5950
## sp_ptbBM74 9.351e-01 7.895e-01 1.184 0.2363
## sp_ptbBM75 -1.558e-01 8.422e-01 -0.185 0.8533
## sp_ptbBM76 2.431e-01 7.036e-01 0.345 0.7297
## sp_ptbBM77 2.448e-01 8.831e-01 0.277 0.7816
## sp_ptbBM78 8.418e-01 1.037e+00 0.812 0.4169
## sp_ptbBM79 5.630e-01 9.166e-01 0.614 0.5391
## sp_ptbBM80 -5.060e-01 1.004e+00 -0.504 0.6142
## sp_ptbBM81 3.361e-01 9.307e-01 0.361 0.7180
## sp_ptbBM82 -1.240e-01 8.208e-01 -0.151 0.8800
## sp_ptbBM83 -3.755e-01 9.096e-01 -0.413 0.6797
## sp_ptbBM84 2.179e-01 9.565e-01 0.228 0.8198
## sp_ptbBM85 -1.418e+00 1.186e+00 -1.195 0.2321
## sp_ptbBM86 6.215e-01 1.084e+00 0.573 0.5664
## sp_ptbBM87 -6.570e-03 9.322e-01 -0.007 0.9944
## sp_ptbBM88 2.837e-01 9.546e-01 0.297 0.7663
## sp_ptbBM89 -7.076e-01 1.024e+00 -0.691 0.4897
## sp_ptbBM90 9.878e-01 9.908e-01 0.997 0.3188
## sp_ptbBM91 7.572e-01 1.279e+00 0.592 0.5539
## sp_ptbBM92 1.234e+00 9.508e-01 1.298 0.1944
## sp_ptbBM93 5.666e-01 9.435e-01 0.600 0.5482
## sp_ptbBM94 -3.433e-01 8.379e-01 -0.410 0.6820
## sp_ptbBM95 1.993e-01 7.658e-01 0.260 0.7947
## sp_ptbBM96 4.363e-01 8.482e-01 0.514 0.6069
## sp_ptbBM97 2.283e-01 8.673e-01 0.263 0.7924
## sp_ptbBM98 6.450e-01 1.231e+00 0.524 0.6002
## sp_ptbBM99 1.007e+00 9.029e-01 1.115 0.2649
## sp_ptbBM100 8.108e-01 8.785e-01 0.923 0.3561
## sp_ptbBM101 8.948e-01 7.492e-01 1.194 0.2323
## sp_ptbBM102 5.052e-01 7.204e-01 0.701 0.4831
## sp_ptbBM103 2.115e-02 7.645e-01 0.028 0.9779
## sp_ptbBM104 4.739e-01 7.934e-01 0.597 0.5503
## sp_ptbBM105 8.670e-01 9.342e-01 0.928 0.3534
## sp_ptbBM106 9.899e-02 7.580e-01 0.131 0.8961
## sp_ptbBM107 1.816e-02 7.635e-01 0.024 0.9810
## sp_ptbBM108 6.970e-01 7.478e-01 0.932 0.3513
## sp_ptbBM109 1.358e-01 7.072e-01 0.192 0.8477
## sp_ptbBM110 6.934e-01 6.424e-01 1.079 0.2804
## sp_ptbBM111 3.719e-01 8.912e-01 0.417 0.6765
## sp_ptbBM112 -7.663e-02 8.683e-01 -0.088 0.9297
## sp_ptbBM113 9.460e-01 9.131e-01 1.036 0.3002
## sp_ptbBM114 4.845e-01 8.476e-01 0.572 0.5676
## sp_ptbBM115 -9.617e-01 8.891e-01 -1.082 0.2794
## sp_ptbBM116 2.011e+00 8.082e-01 2.489 0.0128 *
## sp_ptbBM117 -1.222e+00 9.633e-01 -1.268 0.2047
## sp_ptbBM118 2.292e+00 9.014e-01 2.543 0.0110 *
## sp_ptbBM119 -9.940e-01 1.149e+00 -0.865 0.3869
## sp_ptbBM120 1.631e+00 9.790e-01 1.666 0.0957 .
## sp_ptbBM121 -6.391e-02 8.646e-01 -0.074 0.9411
## sp_ptbBM122 1.231e+00 7.862e-01 1.566 0.1173
## sp_ptbBM123 4.252e-01 6.606e-01 0.644 0.5198
## sp_ptbBM124 7.020e-01 7.556e-01 0.929 0.3529
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(21891.1) family taken to be 1)
##
## Null deviance: 1101.3 on 886 degrees of freedom
## Residual deviance: 944.4 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3235.3
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 21891
## Std. Err.: 143955
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2977.261scatter.smooth(predict(m6a, type='response'), rstandard(m6a, type='deviance'), col='gray')
m6a.resid<-residuals(m6a, type="deviance")
m6a.pred<-predict(m6a, type="response")
length(m6a.resid); length(m6a.pred)## [1] 939## [1] 939pacf(m6a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m6a.ac<-update(m6a,.~.+lag(m6a.resid,1)+lag(m6a.resid,2)+lag(m6a.resid,3)+lag(m6a.resid,4)+
lag(m6a.resid,5)+lag(m6a.resid,6)+lag(m6a.resid,7)+lag(m6a.resid,8)+
lag(m6a.resid,9)+lag(m6a.resid,10)+lag(m6a.resid,11)+lag(m6a.resid,12)+
lag(m6a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m6a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb6.meanRH + sp_ptbBM + lag(m6a.resid,
## 1) + lag(m6a.resid, 2) + lag(m6a.resid, 3) + lag(m6a.resid,
## 4) + lag(m6a.resid, 5) + lag(m6a.resid, 6) + lag(m6a.resid,
## 7) + lag(m6a.resid, 8) + lag(m6a.resid, 9) + lag(m6a.resid,
## 10) + lag(m6a.resid, 11) + lag(m6a.resid, 12) + lag(m6a.resid,
## 25), data = week, init.theta = 47537.46848, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.53961 -0.65177 -0.05496 0.45908 2.00834
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.259959 12.858939 0.487 0.626388
## cb6.meanRHv1.l1 -0.049546 0.293391 -0.169 0.865895
## cb6.meanRHv1.l2 -0.163845 0.185378 -0.884 0.376781
## cb6.meanRHv2.l1 -0.534028 1.060063 -0.504 0.614423
## cb6.meanRHv2.l2 -0.339399 0.705129 -0.481 0.630283
## cb6.meanRHv3.l1 -0.131428 0.474319 -0.277 0.781712
## cb6.meanRHv3.l2 0.191265 0.339309 0.564 0.572964
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -8.719498 43.680747 -0.200 0.841779
## sp_ptbBM9 2.736756 3.969018 0.690 0.490490
## sp_ptbBM10 -2.702944 1.691058 -1.598 0.109960
## sp_ptbBM11 0.417817 0.983384 0.425 0.670926
## sp_ptbBM12 -0.274909 0.919004 -0.299 0.764835
## sp_ptbBM13 2.264259 1.140339 1.986 0.047078 *
## sp_ptbBM14 -1.696510 1.143697 -1.483 0.137980
## sp_ptbBM15 2.950605 1.110133 2.658 0.007863 **
## sp_ptbBM16 -1.426058 1.259851 -1.132 0.257666
## sp_ptbBM17 1.717204 1.108133 1.550 0.121228
## sp_ptbBM18 -0.714577 1.133282 -0.631 0.528343
## sp_ptbBM19 2.199909 1.083284 2.031 0.042278 *
## sp_ptbBM20 -0.870327 1.227382 -0.709 0.478267
## sp_ptbBM21 1.402662 1.026662 1.366 0.171865
## sp_ptbBM22 1.251897 1.114657 1.123 0.261385
## sp_ptbBM23 -0.694347 1.506111 -0.461 0.644784
## sp_ptbBM24 0.980153 1.976915 0.496 0.620036
## sp_ptbBM25 -1.220497 2.392920 -0.510 0.610020
## sp_ptbBM26 1.258220 2.595230 0.485 0.627804
## sp_ptbBM27 -2.465957 2.862078 -0.862 0.388910
## sp_ptbBM28 -1.038101 2.911287 -0.357 0.721408
## sp_ptbBM29 -0.681292 2.900747 -0.235 0.814311
## sp_ptbBM30 0.671938 2.825456 0.238 0.812024
## sp_ptbBM31 -0.277608 2.872916 -0.097 0.923021
## sp_ptbBM32 0.851619 2.876955 0.296 0.767219
## sp_ptbBM33 -1.461998 2.862856 -0.511 0.609576
## sp_ptbBM34 1.459238 2.734810 0.534 0.593633
## sp_ptbBM35 -1.400906 3.062967 -0.457 0.647406
## sp_ptbBM36 1.331308 2.835128 0.470 0.638658
## sp_ptbBM37 -0.642517 2.369698 -0.271 0.786284
## sp_ptbBM38 0.490209 2.133848 0.230 0.818301
## sp_ptbBM39 0.769338 1.890248 0.407 0.684005
## sp_ptbBM40 0.191568 1.565483 0.122 0.902606
## sp_ptbBM41 1.093178 1.527414 0.716 0.474174
## sp_ptbBM42 -3.164515 1.333522 -2.373 0.017642 *
## sp_ptbBM43 2.293121 1.339590 1.712 0.086932 .
## sp_ptbBM44 -1.859541 1.358494 -1.369 0.171054
## sp_ptbBM45 1.766758 1.444788 1.223 0.221387
## sp_ptbBM46 -0.525719 1.479013 -0.355 0.722251
## sp_ptbBM47 0.910152 1.607135 0.566 0.571177
## sp_ptbBM48 -0.577219 1.437127 -0.402 0.687943
## sp_ptbBM49 1.106561 1.708850 0.648 0.517278
## sp_ptbBM50 -0.322368 1.331464 -0.242 0.808691
## sp_ptbBM51 1.196979 1.357786 0.882 0.378011
## sp_ptbBM52 0.310033 1.188604 0.261 0.794217
## sp_ptbBM53 1.105819 1.142576 0.968 0.333129
## sp_ptbBM54 1.049076 1.032220 1.016 0.309472
## sp_ptbBM55 -0.771523 1.044364 -0.739 0.460060
## sp_ptbBM56 1.161370 1.030422 1.127 0.259708
## sp_ptbBM57 1.130586 1.113356 1.015 0.309879
## sp_ptbBM58 0.763618 0.988859 0.772 0.439983
## sp_ptbBM59 -0.535905 1.243809 -0.431 0.666572
## sp_ptbBM60 0.670399 1.290509 0.519 0.603423
## sp_ptbBM61 -1.357080 1.280619 -1.060 0.289278
## sp_ptbBM62 1.078599 1.292438 0.835 0.403974
## sp_ptbBM63 -2.751696 1.534205 -1.794 0.072883 .
## sp_ptbBM64 2.719543 1.802569 1.509 0.131374
## sp_ptbBM65 -1.860032 1.356660 -1.371 0.170363
## sp_ptbBM66 2.491527 1.185370 2.102 0.035562 *
## sp_ptbBM67 0.065629 0.962998 0.068 0.945666
## sp_ptbBM68 1.054542 0.903343 1.167 0.243058
## sp_ptbBM69 0.753418 0.936402 0.805 0.421057
## sp_ptbBM70 1.230370 1.112819 1.106 0.268885
## sp_ptbBM71 -0.877874 1.118521 -0.785 0.432540
## sp_ptbBM72 1.214417 0.958765 1.267 0.205282
## sp_ptbBM73 -2.813490 0.962236 -2.924 0.003457 **
## sp_ptbBM74 2.600396 0.863045 3.013 0.002586 **
## sp_ptbBM75 -3.717945 0.892145 -4.167 3.08e-05 ***
## sp_ptbBM76 3.028559 0.803588 3.769 0.000164 ***
## sp_ptbBM77 -2.300638 0.912369 -2.522 0.011682 *
## sp_ptbBM78 3.205829 1.088382 2.945 0.003224 **
## sp_ptbBM79 0.637943 0.949560 0.672 0.501692
## sp_ptbBM80 1.427282 1.005117 1.420 0.155603
## sp_ptbBM81 -1.350437 0.993788 -1.359 0.174185
## sp_ptbBM82 2.226169 0.857884 2.595 0.009460 **
## sp_ptbBM83 -2.577309 0.947577 -2.720 0.006530 **
## sp_ptbBM84 1.633997 1.024812 1.594 0.110838
## sp_ptbBM85 -1.619084 1.233535 -1.313 0.189332
## sp_ptbBM86 0.872207 1.120507 0.778 0.436331
## sp_ptbBM87 1.082814 0.947744 1.143 0.253239
## sp_ptbBM88 -0.245460 0.981596 -0.250 0.802539
## sp_ptbBM89 -0.516207 1.089040 -0.474 0.635499
## sp_ptbBM90 -0.942340 1.006649 -0.936 0.349214
## sp_ptbBM91 1.414864 1.322586 1.070 0.284723
## sp_ptbBM92 0.928765 0.964445 0.963 0.335545
## sp_ptbBM93 1.415268 0.957116 1.479 0.139226
## sp_ptbBM94 -0.510205 0.864030 -0.590 0.554859
## sp_ptbBM95 -0.339527 0.757425 -0.448 0.653963
## sp_ptbBM96 0.133141 0.935587 0.142 0.886837
## sp_ptbBM97 0.002285 0.908642 0.003 0.997994
## sp_ptbBM98 -2.551941 1.272411 -2.006 0.044899 *
## sp_ptbBM99 1.465490 0.953354 1.537 0.124246
## sp_ptbBM100 0.346986 0.888058 0.391 0.696001
## sp_ptbBM101 1.365915 0.771550 1.770 0.076669 .
## sp_ptbBM102 0.747587 0.699959 1.068 0.285501
## sp_ptbBM103 -1.308694 0.768850 -1.702 0.088728 .
## sp_ptbBM104 1.022228 0.814812 1.255 0.209640
## sp_ptbBM105 0.273718 0.943474 0.290 0.771727
## sp_ptbBM106 0.818459 0.863270 0.948 0.343083
## sp_ptbBM107 -1.693387 0.761197 -2.225 0.026106 *
## sp_ptbBM108 0.790110 0.796317 0.992 0.321097
## sp_ptbBM109 0.950280 0.726582 1.308 0.190915
## sp_ptbBM110 0.561721 0.637945 0.881 0.378579
## sp_ptbBM111 1.628255 0.953005 1.709 0.087535 .
## sp_ptbBM112 -0.623681 0.868421 -0.718 0.472648
## sp_ptbBM113 0.859370 0.933053 0.921 0.357034
## sp_ptbBM114 -0.233945 0.862037 -0.271 0.786093
## sp_ptbBM115 -0.823152 0.896912 -0.918 0.358743
## sp_ptbBM116 1.446590 0.840523 1.721 0.085240 .
## sp_ptbBM117 -0.376331 1.005701 -0.374 0.708257
## sp_ptbBM118 1.367373 0.909575 1.503 0.132759
## sp_ptbBM119 -0.615640 1.228170 -0.501 0.616184
## sp_ptbBM120 0.174008 1.000336 0.174 0.861905
## sp_ptbBM121 -1.042413 0.897785 -1.161 0.245604
## sp_ptbBM122 1.244699 0.783849 1.588 0.112302
## sp_ptbBM123 -0.528922 0.668850 -0.791 0.429065
## sp_ptbBM124 1.514577 0.761699 1.988 0.046765 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m6a.resid, 1) -0.434767 0.030523 -14.244 < 2e-16 ***
## lag(m6a.resid, 2) -0.479153 0.035369 -13.547 < 2e-16 ***
## lag(m6a.resid, 3) -0.562685 0.037032 -15.194 < 2e-16 ***
## lag(m6a.resid, 4) -0.569543 0.040094 -14.205 < 2e-16 ***
## lag(m6a.resid, 5) -0.575420 0.040317 -14.272 < 2e-16 ***
## lag(m6a.resid, 6) -0.529231 0.040885 -12.944 < 2e-16 ***
## lag(m6a.resid, 7) -0.504068 0.038744 -13.010 < 2e-16 ***
## lag(m6a.resid, 8) -0.451528 0.037277 -12.113 < 2e-16 ***
## lag(m6a.resid, 9) -0.359530 0.034804 -10.330 < 2e-16 ***
## lag(m6a.resid, 10) -0.308238 0.031656 -9.737 < 2e-16 ***
## lag(m6a.resid, 11) -0.206678 0.030860 -6.697 2.12e-11 ***
## lag(m6a.resid, 12) -0.170457 0.028716 -5.936 2.92e-09 ***
## lag(m6a.resid, 25) -0.042222 0.024470 -1.725 0.084448 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(47537.47) family taken to be 1)
##
## Null deviance: 1055.0 on 861 degrees of freedom
## Residual deviance: 552.8 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2810.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 47537
## Std. Err.: 188073
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2534.727m6a.resid_ac<-residuals(m6a.ac, type="deviance")
m6a.pred_ac<-predict(m6a.ac, type="response")
pacf(m6a.resid_ac,na.action = na.omit) 
length(m6a.pred_ac); length(m6a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m6a.pred,lwd=1, col="blue")
plot(week$time,m6a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m6a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m6a.pred_ac,lwd=1, col="blue")
plot(week$time,m6a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m6a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.m6a <- crosspred(cb6.meanRH, m6a.ac, cen = 82.7, by=0.1,cumul=TRUE)
##for m7a maxRH ######
summary(m7a)##
## Call:
## glm.nb(formula = ptbBM ~ cb7.maxRH + sp_ptbBM, data = week, init.theta = 22024.84537,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4674 -0.7951 -0.1165 0.5319 2.5639
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.320e+00 1.250e+01 0.346 0.7296
## cb7.maxRHv1.l1 -2.357e-01 2.992e-01 -0.788 0.4309
## cb7.maxRHv1.l2 1.871e-02 2.040e-01 0.092 0.9269
## cb7.maxRHv2.l1 -1.392e-01 9.877e-01 -0.141 0.8879
## cb7.maxRHv2.l2 1.710e-01 6.943e-01 0.246 0.8055
## cb7.maxRHv3.l1 5.055e-02 2.824e-01 0.179 0.8579
## cb7.maxRHv3.l2 2.605e-01 1.898e-01 1.373 0.1699
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.327e+06 2.224e+06 -0.597 0.5507
## sp_ptbBM5 1.508e+01 1.204e+01 1.252 0.2105
## sp_ptbBM6 -7.682e-01 2.155e+00 -0.356 0.7215
## sp_ptbBM7 1.981e+00 1.341e+00 1.477 0.1397
## sp_ptbBM8 -2.493e-01 1.126e+00 -0.221 0.8248
## sp_ptbBM9 1.716e+00 9.949e-01 1.725 0.0845 .
## sp_ptbBM10 -2.259e+00 1.074e+00 -2.104 0.0353 *
## sp_ptbBM11 1.075e+00 7.960e-01 1.350 0.1769
## sp_ptbBM12 -7.990e-01 7.542e-01 -1.059 0.2894
## sp_ptbBM13 2.029e+00 9.767e-01 2.077 0.0378 *
## sp_ptbBM14 -6.962e-01 8.290e-01 -0.840 0.4010
## sp_ptbBM15 3.463e-01 7.202e-01 0.481 0.6307
## sp_ptbBM16 -2.908e-01 8.167e-01 -0.356 0.7218
## sp_ptbBM17 1.421e-02 7.504e-01 0.019 0.9849
## sp_ptbBM18 -6.563e-01 7.679e-01 -0.855 0.3927
## sp_ptbBM19 -5.617e-01 7.982e-01 -0.704 0.4816
## sp_ptbBM20 8.802e-01 1.152e+00 0.764 0.4448
## sp_ptbBM21 5.774e-01 7.416e-01 0.779 0.4363
## sp_ptbBM22 7.318e-01 7.586e-01 0.965 0.3347
## sp_ptbBM23 -9.483e-01 8.774e-01 -1.081 0.2798
## sp_ptbBM24 1.009e-01 7.708e-01 0.131 0.8959
## sp_ptbBM25 -7.471e-01 9.520e-01 -0.785 0.4326
## sp_ptbBM26 -4.297e-01 1.137e+00 -0.378 0.7054
## sp_ptbBM27 -6.022e-01 1.412e+00 -0.426 0.6698
## sp_ptbBM28 -1.531e+00 1.269e+00 -1.206 0.2276
## sp_ptbBM29 -1.156e+00 2.176e+00 -0.531 0.5953
## sp_ptbBM30 -2.758e+00 2.514e+00 -1.097 0.2726
## sp_ptbBM31 -2.924e+00 3.632e+00 -0.805 0.4209
## sp_ptbBM32 -4.804e+00 4.751e+00 -1.011 0.3120
## sp_ptbBM33 -4.585e+00 5.816e+00 -0.788 0.4305
## sp_ptbBM34 -2.688e+00 6.322e+00 -0.425 0.6707
## sp_ptbBM35 -3.371e+00 7.573e+00 -0.445 0.6562
## sp_ptbBM36 -1.766e+00 6.507e+00 -0.271 0.7861
## sp_ptbBM37 -2.352e+00 5.965e+00 -0.394 0.6934
## sp_ptbBM38 -8.224e-01 5.320e+00 -0.155 0.8772
## sp_ptbBM39 -1.712e+00 4.334e+00 -0.395 0.6929
## sp_ptbBM40 -2.702e-01 3.376e+00 -0.080 0.9362
## sp_ptbBM41 -6.058e-01 2.966e+00 -0.204 0.8382
## sp_ptbBM42 -7.989e-01 2.043e+00 -0.391 0.6958
## sp_ptbBM43 -2.441e-01 2.115e+00 -0.115 0.9081
## sp_ptbBM44 -5.178e-01 2.355e+00 -0.220 0.8260
## sp_ptbBM45 -1.501e-01 2.521e+00 -0.060 0.9525
## sp_ptbBM46 -1.364e+00 2.601e+00 -0.525 0.5998
## sp_ptbBM47 -9.843e-01 2.964e+00 -0.332 0.7398
## sp_ptbBM48 -1.076e+00 2.718e+00 -0.396 0.6922
## sp_ptbBM49 -8.903e-02 2.663e+00 -0.033 0.9733
## sp_ptbBM50 -7.152e-01 2.164e+00 -0.330 0.7411
## sp_ptbBM51 1.252e+00 2.032e+00 0.616 0.5379
## sp_ptbBM52 -1.818e-01 1.798e+00 -0.101 0.9195
## sp_ptbBM53 8.707e-01 1.597e+00 0.545 0.5857
## sp_ptbBM54 7.511e-01 1.351e+00 0.556 0.5784
## sp_ptbBM55 6.389e-01 1.332e+00 0.480 0.6314
## sp_ptbBM56 1.363e+00 1.207e+00 1.130 0.2586
## sp_ptbBM57 1.409e+00 1.291e+00 1.092 0.2750
## sp_ptbBM58 9.388e-01 1.266e+00 0.742 0.4583
## sp_ptbBM59 -1.234e-01 1.421e+00 -0.087 0.9308
## sp_ptbBM60 -1.015e-01 1.655e+00 -0.061 0.9511
## sp_ptbBM61 1.898e-02 1.654e+00 0.011 0.9908
## sp_ptbBM62 -5.046e-01 1.654e+00 -0.305 0.7603
## sp_ptbBM63 -2.652e-01 1.818e+00 -0.146 0.8840
## sp_ptbBM64 3.021e-01 2.112e+00 0.143 0.8863
## sp_ptbBM65 5.414e-01 1.777e+00 0.305 0.7606
## sp_ptbBM66 3.918e-01 1.636e+00 0.239 0.8108
## sp_ptbBM67 1.562e+00 1.373e+00 1.137 0.2555
## sp_ptbBM68 1.137e+00 1.301e+00 0.874 0.3823
## sp_ptbBM69 1.820e+00 1.179e+00 1.544 0.1226
## sp_ptbBM70 9.955e-01 1.197e+00 0.832 0.4057
## sp_ptbBM71 1.083e+00 1.184e+00 0.915 0.3604
## sp_ptbBM72 8.982e-01 1.125e+00 0.798 0.4248
## sp_ptbBM73 1.032e-01 1.061e+00 0.097 0.9226
## sp_ptbBM74 1.274e+00 1.097e+00 1.162 0.2454
## sp_ptbBM75 1.905e-01 1.073e+00 0.178 0.8590
## sp_ptbBM76 9.707e-01 1.078e+00 0.900 0.3681
## sp_ptbBM77 9.680e-01 1.038e+00 0.932 0.3512
## sp_ptbBM78 2.014e+00 1.093e+00 1.842 0.0655 .
## sp_ptbBM79 1.453e+00 1.027e+00 1.415 0.1571
## sp_ptbBM80 2.966e-01 1.074e+00 0.276 0.7824
## sp_ptbBM81 5.881e-01 1.143e+00 0.515 0.6068
## sp_ptbBM82 1.508e-01 1.063e+00 0.142 0.8872
## sp_ptbBM83 2.072e-01 9.644e-01 0.215 0.8299
## sp_ptbBM84 9.735e-01 1.105e+00 0.881 0.3783
## sp_ptbBM85 -3.314e-01 1.381e+00 -0.240 0.8103
## sp_ptbBM86 1.747e+00 1.205e+00 1.449 0.1472
## sp_ptbBM87 8.265e-01 1.194e+00 0.692 0.4887
## sp_ptbBM88 9.260e-01 1.163e+00 0.796 0.4258
## sp_ptbBM89 -1.709e-01 1.286e+00 -0.133 0.8943
## sp_ptbBM90 1.874e+00 1.150e+00 1.629 0.1032
## sp_ptbBM91 8.637e-01 1.056e+00 0.818 0.4136
## sp_ptbBM92 1.335e+00 9.308e-01 1.435 0.1514
## sp_ptbBM93 5.436e-01 9.141e-01 0.595 0.5520
## sp_ptbBM94 -4.983e-01 8.925e-01 -0.558 0.5767
## sp_ptbBM95 1.174e-02 8.592e-01 0.014 0.9891
## sp_ptbBM96 4.115e-02 8.178e-01 0.050 0.9599
## sp_ptbBM97 5.823e-01 8.043e-01 0.724 0.4690
## sp_ptbBM98 6.373e-01 1.097e+00 0.581 0.5613
## sp_ptbBM99 1.239e+00 8.238e-01 1.504 0.1327
## sp_ptbBM100 7.551e-01 7.732e-01 0.977 0.3288
## sp_ptbBM101 7.251e-01 6.832e-01 1.061 0.2886
## sp_ptbBM102 5.139e-01 7.154e-01 0.718 0.4725
## sp_ptbBM103 -2.059e-02 7.229e-01 -0.028 0.9773
## sp_ptbBM104 8.600e-01 7.525e-01 1.143 0.2531
## sp_ptbBM105 1.206e+00 9.129e-01 1.321 0.1865
## sp_ptbBM106 -3.331e-01 1.033e+00 -0.322 0.7472
## sp_ptbBM107 -4.476e-02 1.076e+00 -0.042 0.9668
## sp_ptbBM108 2.568e-01 8.848e-01 0.290 0.7716
## sp_ptbBM109 -4.236e-01 1.037e+00 -0.408 0.6830
## sp_ptbBM110 7.575e-03 9.991e-01 0.008 0.9940
## sp_ptbBM111 1.119e+00 1.319e+00 0.848 0.3964
## sp_ptbBM112 8.114e-02 1.129e+00 0.072 0.9427
## sp_ptbBM113 1.392e+00 8.542e-01 1.630 0.1031
## sp_ptbBM114 5.754e-01 8.336e-01 0.690 0.4900
## sp_ptbBM115 -8.360e-01 8.228e-01 -1.016 0.3096
## sp_ptbBM116 1.941e+00 7.784e-01 2.494 0.0126 *
## sp_ptbBM117 -1.767e+00 9.749e-01 -1.812 0.0699 .
## sp_ptbBM118 2.093e+00 1.071e+00 1.955 0.0506 .
## sp_ptbBM119 -1.811e+00 1.186e+00 -1.527 0.1268
## sp_ptbBM120 9.408e-01 9.017e-01 1.043 0.2968
## sp_ptbBM121 -5.242e-01 8.081e-01 -0.649 0.5166
## sp_ptbBM122 3.643e-01 8.314e-01 0.438 0.6613
## sp_ptbBM123 -6.105e-01 7.559e-01 -0.808 0.4193
## sp_ptbBM124 2.255e-01 8.244e-01 0.274 0.7844
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22024.85) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 942.52 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3233.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22025
## Std. Err.: 143520
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2975.382scatter.smooth(predict(m7a, type='response'), rstandard(m7a, type='deviance'), col='gray')
m7a.resid<-residuals(m7a, type="deviance")
m7a.pred<-predict(m7a, type="response")
length(m7a.resid); length(m7a.pred)## [1] 939## [1] 939pacf(m7a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m7a.ac<-update(m7a,.~.+lag(m7a.resid,1)+lag(m7a.resid,2)+lag(m7a.resid,3)+lag(m7a.resid,4)+
lag(m7a.resid,5)+lag(m7a.resid,6)+lag(m7a.resid,7)+lag(m7a.resid,8)+
lag(m7a.resid,9)+lag(m7a.resid,10)+lag(m7a.resid,11)+lag(m7a.resid,12)+
lag(m7a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m7a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb7.maxRH + sp_ptbBM + lag(m7a.resid,
## 1) + lag(m7a.resid, 2) + lag(m7a.resid, 3) + lag(m7a.resid,
## 4) + lag(m7a.resid, 5) + lag(m7a.resid, 6) + lag(m7a.resid,
## 7) + lag(m7a.resid, 8) + lag(m7a.resid, 9) + lag(m7a.resid,
## 10) + lag(m7a.resid, 11) + lag(m7a.resid, 12) + lag(m7a.resid,
## 25), data = week, init.theta = 47259.8113, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.49476 -0.65518 -0.04479 0.45393 2.14079
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -12.51529 13.09002 -0.956 0.339025
## cb7.maxRHv1.l1 0.18798 0.31361 0.599 0.548895
## cb7.maxRHv1.l2 -0.13590 0.21280 -0.639 0.523046
## cb7.maxRHv2.l1 1.03226 1.03263 1.000 0.317484
## cb7.maxRHv2.l2 -0.05396 0.72763 -0.074 0.940888
## cb7.maxRHv3.l1 0.44725 0.29750 1.503 0.132741
## cb7.maxRHv3.l2 0.39213 0.20318 1.930 0.053609 .
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -3.71088 43.71198 -0.085 0.932346
## sp_ptbBM9 3.86801 4.02450 0.961 0.336493
## sp_ptbBM10 -1.54305 1.64019 -0.941 0.346820
## sp_ptbBM11 0.46886 0.90373 0.519 0.603894
## sp_ptbBM12 -0.71585 0.82231 -0.871 0.384007
## sp_ptbBM13 3.28715 1.00333 3.276 0.001052 **
## sp_ptbBM14 -1.24724 0.83329 -1.497 0.134454
## sp_ptbBM15 2.35196 0.72115 3.261 0.001109 **
## sp_ptbBM16 -0.78034 0.77587 -1.006 0.314534
## sp_ptbBM17 1.46890 0.75157 1.954 0.050649 .
## sp_ptbBM18 -1.96051 0.78930 -2.484 0.012997 *
## sp_ptbBM19 0.97436 0.79595 1.224 0.220898
## sp_ptbBM20 0.23343 1.27496 0.183 0.854731
## sp_ptbBM21 1.57371 0.80643 1.951 0.051003 .
## sp_ptbBM22 0.65951 0.76357 0.864 0.387740
## sp_ptbBM23 -1.49153 0.88527 -1.685 0.092021 .
## sp_ptbBM24 -0.11866 0.75260 -0.158 0.874720
## sp_ptbBM25 -2.82470 1.02239 -2.763 0.005730 **
## sp_ptbBM26 0.26475 1.11678 0.237 0.812604
## sp_ptbBM27 -1.67089 1.44906 -1.153 0.248874
## sp_ptbBM28 -0.40261 1.29223 -0.312 0.755370
## sp_ptbBM29 0.27994 2.26529 0.124 0.901651
## sp_ptbBM30 1.77656 2.58224 0.688 0.491456
## sp_ptbBM31 0.54529 3.79124 0.144 0.885635
## sp_ptbBM32 2.20056 4.93900 0.446 0.655924
## sp_ptbBM33 2.75838 6.05301 0.456 0.648603
## sp_ptbBM34 7.91200 6.62867 1.194 0.232634
## sp_ptbBM35 7.25192 7.93814 0.914 0.360951
## sp_ptbBM36 8.26837 6.82638 1.211 0.225804
## sp_ptbBM37 5.52729 6.29202 0.878 0.379694
## sp_ptbBM38 5.02804 5.60778 0.897 0.369923
## sp_ptbBM39 4.11371 4.58742 0.897 0.369858
## sp_ptbBM40 2.67244 3.58192 0.746 0.455612
## sp_ptbBM41 3.30851 3.10880 1.064 0.287221
## sp_ptbBM42 -1.38840 2.14226 -0.648 0.516919
## sp_ptbBM43 4.26225 2.24941 1.895 0.058115 .
## sp_ptbBM44 0.22814 2.46073 0.093 0.926131
## sp_ptbBM45 3.88156 2.63152 1.475 0.140206
## sp_ptbBM46 1.65001 2.74286 0.602 0.547463
## sp_ptbBM47 3.61579 3.09048 1.170 0.242011
## sp_ptbBM48 1.91283 2.85504 0.670 0.502868
## sp_ptbBM49 3.41748 2.80852 1.217 0.223670
## sp_ptbBM50 1.96011 2.27699 0.861 0.389330
## sp_ptbBM51 3.32737 2.14713 1.550 0.121217
## sp_ptbBM52 2.27451 1.89870 1.198 0.230945
## sp_ptbBM53 2.46306 1.67657 1.469 0.141804
## sp_ptbBM54 2.57050 1.40392 1.831 0.067109 .
## sp_ptbBM55 1.20982 1.39943 0.865 0.387307
## sp_ptbBM56 2.47281 1.25982 1.963 0.049666 *
## sp_ptbBM57 2.99964 1.34913 2.223 0.026189 *
## sp_ptbBM58 2.22298 1.31602 1.689 0.091187 .
## sp_ptbBM59 1.35822 1.49304 0.910 0.362981
## sp_ptbBM60 2.74739 1.72560 1.592 0.111354
## sp_ptbBM61 0.40873 1.74575 0.234 0.814887
## sp_ptbBM62 3.22968 1.74630 1.849 0.064394 .
## sp_ptbBM63 -0.59539 1.93059 -0.308 0.757782
## sp_ptbBM64 5.15189 2.25196 2.288 0.022153 *
## sp_ptbBM65 0.36814 1.86322 0.198 0.843373
## sp_ptbBM66 4.22590 1.74628 2.420 0.015523 *
## sp_ptbBM67 1.79312 1.42407 1.259 0.207974
## sp_ptbBM68 3.00451 1.36558 2.200 0.027794 *
## sp_ptbBM69 2.68173 1.20658 2.223 0.026244 *
## sp_ptbBM70 2.82197 1.24621 2.264 0.023547 *
## sp_ptbBM71 0.88074 1.25576 0.701 0.483078
## sp_ptbBM72 2.45920 1.17055 2.101 0.035651 *
## sp_ptbBM73 -1.58764 1.14619 -1.385 0.166007
## sp_ptbBM74 3.41735 1.16746 2.927 0.003421 **
## sp_ptbBM75 -2.71286 1.10875 -2.447 0.014414 *
## sp_ptbBM76 4.12938 1.18734 3.478 0.000505 ***
## sp_ptbBM77 -0.55647 1.07058 -0.520 0.603212
## sp_ptbBM78 4.72599 1.15654 4.086 4.38e-05 ***
## sp_ptbBM79 2.21371 1.07572 2.058 0.039602 *
## sp_ptbBM80 2.69580 1.10003 2.451 0.014259 *
## sp_ptbBM81 -0.47297 1.20351 -0.393 0.694324
## sp_ptbBM82 3.01496 1.13056 2.667 0.007658 **
## sp_ptbBM83 -1.58165 0.99272 -1.593 0.111105
## sp_ptbBM84 3.25961 1.18859 2.742 0.006099 **
## sp_ptbBM85 0.23565 1.44245 0.163 0.870229
## sp_ptbBM86 2.89005 1.25761 2.298 0.021559 *
## sp_ptbBM87 2.33842 1.22635 1.907 0.056546 .
## sp_ptbBM88 1.39558 1.20113 1.162 0.245283
## sp_ptbBM89 0.37234 1.34889 0.276 0.782524
## sp_ptbBM90 1.39092 1.18999 1.169 0.242466
## sp_ptbBM91 2.44301 1.10573 2.209 0.027146 *
## sp_ptbBM92 2.22564 0.94941 2.344 0.019067 *
## sp_ptbBM93 2.02691 0.93908 2.158 0.030896 *
## sp_ptbBM94 0.02560 0.90325 0.028 0.977392
## sp_ptbBM95 -0.05951 0.87028 -0.068 0.945479
## sp_ptbBM96 -0.10781 0.89402 -0.121 0.904018
## sp_ptbBM97 0.92390 0.85951 1.075 0.282413
## sp_ptbBM98 -1.38584 1.16978 -1.185 0.236134
## sp_ptbBM99 2.53330 0.89492 2.831 0.004644 **
## sp_ptbBM100 0.97823 0.78319 1.249 0.211654
## sp_ptbBM101 1.81512 0.70033 2.592 0.009547 **
## sp_ptbBM102 1.03697 0.68855 1.506 0.132063
## sp_ptbBM103 -1.12714 0.71463 -1.577 0.114739
## sp_ptbBM104 1.93460 0.76009 2.545 0.010920 *
## sp_ptbBM105 1.73360 0.95297 1.819 0.068887 .
## sp_ptbBM106 1.55867 1.10940 1.405 0.160029
## sp_ptbBM107 -0.68735 1.09862 -0.626 0.531546
## sp_ptbBM108 1.30267 0.94581 1.377 0.168419
## sp_ptbBM109 1.41042 1.08943 1.295 0.195445
## sp_ptbBM110 0.29388 1.02662 0.286 0.774678
## sp_ptbBM111 3.94065 1.42852 2.759 0.005806 **
## sp_ptbBM112 0.47680 1.14229 0.417 0.676381
## sp_ptbBM113 2.08882 0.88678 2.356 0.018497 *
## sp_ptbBM114 0.21469 0.84599 0.254 0.799674
## sp_ptbBM115 -0.70766 0.81625 -0.867 0.385962
## sp_ptbBM116 1.62682 0.80181 2.029 0.042466 *
## sp_ptbBM117 -1.36660 1.00696 -1.357 0.174732
## sp_ptbBM118 1.57916 1.10661 1.427 0.153572
## sp_ptbBM119 -1.33146 1.26278 -1.054 0.291705
## sp_ptbBM120 -0.59105 0.92279 -0.640 0.521848
## sp_ptbBM121 -1.71528 0.84172 -2.038 0.041569 *
## sp_ptbBM122 0.09223 0.82378 0.112 0.910853
## sp_ptbBM123 -2.13871 0.77874 -2.746 0.006025 **
## sp_ptbBM124 0.73939 0.84045 0.880 0.378990
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m7a.resid, 1) -0.43613 0.03063 -14.237 < 2e-16 ***
## lag(m7a.resid, 2) -0.48282 0.03572 -13.517 < 2e-16 ***
## lag(m7a.resid, 3) -0.56703 0.03724 -15.225 < 2e-16 ***
## lag(m7a.resid, 4) -0.57478 0.04015 -14.317 < 2e-16 ***
## lag(m7a.resid, 5) -0.58603 0.04073 -14.389 < 2e-16 ***
## lag(m7a.resid, 6) -0.53738 0.04144 -12.967 < 2e-16 ***
## lag(m7a.resid, 7) -0.50932 0.03945 -12.911 < 2e-16 ***
## lag(m7a.resid, 8) -0.45605 0.03792 -12.026 < 2e-16 ***
## lag(m7a.resid, 9) -0.36331 0.03527 -10.300 < 2e-16 ***
## lag(m7a.resid, 10) -0.30853 0.03184 -9.691 < 2e-16 ***
## lag(m7a.resid, 11) -0.20650 0.03102 -6.657 2.79e-11 ***
## lag(m7a.resid, 12) -0.17206 0.02883 -5.968 2.40e-09 ***
## lag(m7a.resid, 25) -0.04419 0.02436 -1.814 0.069686 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(47259.81) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 549.98 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2807.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 47260
## Std. Err.: 186013
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2531.902m7a.resid_ac<-residuals(m7a.ac, type="deviance")
m7a.pred_ac<-predict(m7a.ac, type="response")
pacf(m7a.resid_ac,na.action = na.omit) 
length(m7a.pred_ac); length(m7a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m7a.pred,lwd=1, col="blue")
plot(week$time,m7a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m7a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m7a.pred_ac,lwd=1, col="blue")
plot(week$time,m7a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m7a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m7a <- crosspred(cb7.maxRH, m7a.ac, cen = 97, by=0.1,cumul=TRUE)
##for m8a AH ######
summary(m8a)##
## Call:
## glm.nb(formula = ptbBM ~ cb8.AH + sp_ptbBM, data = week, init.theta = 22297.04219,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5092 -0.7695 -0.1027 0.5323 2.7731
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.850e+00 1.168e+01 -0.586 0.55760
## cb8.AHv1.l1 3.183e-01 2.924e-01 1.089 0.27632
## cb8.AHv1.l2 -5.072e-02 2.057e-01 -0.247 0.80523
## cb8.AHv2.l1 5.157e-01 9.564e-01 0.539 0.58972
## cb8.AHv2.l2 -2.815e-01 6.913e-01 -0.407 0.68386
## cb8.AHv3.l1 4.144e-01 5.457e-01 0.759 0.44768
## cb8.AHv3.l2 5.219e-01 3.866e-01 1.350 0.17700
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.306e+06 2.243e+06 -0.582 0.56055
## sp_ptbBM5 1.655e+01 1.213e+01 1.364 0.17247
## sp_ptbBM6 -1.267e+00 2.340e+00 -0.542 0.58809
## sp_ptbBM7 2.352e+00 1.902e+00 1.236 0.21636
## sp_ptbBM8 1.551e-01 1.629e+00 0.095 0.92416
## sp_ptbBM9 3.541e+00 1.650e+00 2.146 0.03189 *
## sp_ptbBM10 -8.370e-01 1.638e+00 -0.511 0.60931
## sp_ptbBM11 2.753e+00 1.587e+00 1.735 0.08272 .
## sp_ptbBM12 9.150e-01 1.595e+00 0.574 0.56611
## sp_ptbBM13 2.436e+00 1.364e+00 1.786 0.07409 .
## sp_ptbBM14 -2.857e-03 1.450e+00 -0.002 0.99843
## sp_ptbBM15 1.165e+00 1.453e+00 0.802 0.42268
## sp_ptbBM16 1.899e+00 1.569e+00 1.210 0.22623
## sp_ptbBM17 1.873e+00 1.539e+00 1.218 0.22338
## sp_ptbBM18 1.337e+00 1.504e+00 0.889 0.37410
## sp_ptbBM19 8.508e-01 1.509e+00 0.564 0.57280
## sp_ptbBM20 1.187e+00 1.586e+00 0.748 0.45424
## sp_ptbBM21 1.900e+00 1.790e+00 1.061 0.28854
## sp_ptbBM22 1.898e+00 1.970e+00 0.963 0.33537
## sp_ptbBM23 2.787e+00 2.564e+00 1.087 0.27693
## sp_ptbBM24 4.314e+00 2.881e+00 1.497 0.13427
## sp_ptbBM25 4.828e+00 3.491e+00 1.383 0.16663
## sp_ptbBM26 5.182e+00 3.718e+00 1.394 0.16338
## sp_ptbBM27 3.752e+00 3.942e+00 0.952 0.34116
## sp_ptbBM28 4.047e+00 3.975e+00 1.018 0.30857
## sp_ptbBM29 4.948e+00 3.766e+00 1.314 0.18887
## sp_ptbBM30 3.410e+00 3.787e+00 0.900 0.36797
## sp_ptbBM31 4.990e+00 3.719e+00 1.342 0.17962
## sp_ptbBM32 3.696e+00 3.533e+00 1.046 0.29556
## sp_ptbBM33 4.483e+00 3.678e+00 1.219 0.22289
## sp_ptbBM34 5.297e+00 3.873e+00 1.368 0.17146
## sp_ptbBM35 5.533e+00 4.096e+00 1.351 0.17681
## sp_ptbBM36 6.179e+00 3.905e+00 1.582 0.11355
## sp_ptbBM37 3.947e+00 3.685e+00 1.071 0.28414
## sp_ptbBM38 4.739e+00 3.363e+00 1.409 0.15877
## sp_ptbBM39 3.180e+00 3.231e+00 0.984 0.32503
## sp_ptbBM40 3.903e+00 3.018e+00 1.293 0.19587
## sp_ptbBM41 2.885e+00 2.627e+00 1.098 0.27207
## sp_ptbBM42 2.173e+00 2.508e+00 0.866 0.38624
## sp_ptbBM43 2.969e+00 2.548e+00 1.165 0.24396
## sp_ptbBM44 2.841e+00 2.437e+00 1.166 0.24372
## sp_ptbBM45 3.840e+00 2.610e+00 1.471 0.14125
## sp_ptbBM46 2.858e+00 2.684e+00 1.065 0.28679
## sp_ptbBM47 3.839e+00 2.816e+00 1.363 0.17287
## sp_ptbBM48 3.643e+00 2.890e+00 1.260 0.20755
## sp_ptbBM49 4.419e+00 2.883e+00 1.533 0.12525
## sp_ptbBM50 2.799e+00 2.769e+00 1.011 0.31220
## sp_ptbBM51 4.790e+00 2.902e+00 1.650 0.09888 .
## sp_ptbBM52 3.029e+00 2.844e+00 1.065 0.28699
## sp_ptbBM53 3.807e+00 2.770e+00 1.374 0.16929
## sp_ptbBM54 3.016e+00 2.565e+00 1.175 0.23980
## sp_ptbBM55 2.501e+00 2.403e+00 1.041 0.29810
## sp_ptbBM56 3.058e+00 2.330e+00 1.312 0.18942
## sp_ptbBM57 2.722e+00 2.158e+00 1.261 0.20716
## sp_ptbBM58 2.758e+00 2.027e+00 1.361 0.17364
## sp_ptbBM59 1.688e+00 2.051e+00 0.823 0.41072
## sp_ptbBM60 1.846e+00 1.977e+00 0.934 0.35032
## sp_ptbBM61 2.113e+00 1.944e+00 1.087 0.27712
## sp_ptbBM62 1.696e+00 1.994e+00 0.850 0.39511
## sp_ptbBM63 1.572e+00 1.931e+00 0.814 0.41549
## sp_ptbBM64 2.430e+00 1.770e+00 1.372 0.16991
## sp_ptbBM65 3.176e+00 1.542e+00 2.060 0.03945 *
## sp_ptbBM66 1.672e+00 1.246e+00 1.341 0.17976
## sp_ptbBM67 2.029e+00 1.033e+00 1.965 0.04939 *
## sp_ptbBM68 9.479e-01 1.037e+00 0.914 0.36080
## sp_ptbBM69 9.102e-01 1.128e+00 0.807 0.41958
## sp_ptbBM70 -4.613e-01 1.425e+00 -0.324 0.74616
## sp_ptbBM71 -4.909e-01 1.685e+00 -0.291 0.77078
## sp_ptbBM72 1.770e+00 1.926e+00 0.919 0.35812
## sp_ptbBM73 1.487e+00 1.885e+00 0.789 0.42997
## sp_ptbBM74 2.886e+00 1.949e+00 1.481 0.13867
## sp_ptbBM75 1.981e+00 1.902e+00 1.042 0.29759
## sp_ptbBM76 2.228e+00 1.719e+00 1.296 0.19499
## sp_ptbBM77 1.631e+00 1.612e+00 1.012 0.31150
## sp_ptbBM78 1.670e+00 1.497e+00 1.115 0.26468
## sp_ptbBM79 1.846e+00 1.517e+00 1.217 0.22357
## sp_ptbBM80 1.421e+00 1.623e+00 0.876 0.38121
## sp_ptbBM81 1.427e+00 1.609e+00 0.887 0.37515
## sp_ptbBM82 1.132e+00 1.662e+00 0.681 0.49569
## sp_ptbBM83 1.274e+00 1.604e+00 0.794 0.42708
## sp_ptbBM84 1.740e+00 1.612e+00 1.079 0.28046
## sp_ptbBM85 2.759e-01 1.459e+00 0.189 0.84995
## sp_ptbBM86 2.834e+00 1.327e+00 2.136 0.03270 *
## sp_ptbBM87 1.193e+00 1.215e+00 0.982 0.32624
## sp_ptbBM88 7.450e-01 1.193e+00 0.624 0.53241
## sp_ptbBM89 -7.846e-01 1.351e+00 -0.581 0.56142
## sp_ptbBM90 9.863e-01 1.098e+00 0.898 0.36894
## sp_ptbBM91 4.725e-02 1.351e+00 0.035 0.97211
## sp_ptbBM92 1.109e+00 1.344e+00 0.825 0.40923
## sp_ptbBM93 2.323e+00 1.368e+00 1.698 0.08944 .
## sp_ptbBM94 1.041e+00 1.226e+00 0.849 0.39604
## sp_ptbBM95 1.159e+00 1.099e+00 1.054 0.29179
## sp_ptbBM96 6.652e-01 9.514e-01 0.699 0.48441
## sp_ptbBM97 1.016e+00 1.008e+00 1.007 0.31379
## sp_ptbBM98 -5.431e-01 9.675e-01 -0.561 0.57454
## sp_ptbBM99 -2.960e-01 9.926e-01 -0.298 0.76556
## sp_ptbBM100 8.452e-01 1.028e+00 0.822 0.41121
## sp_ptbBM101 1.650e+00 8.908e-01 1.852 0.06396 .
## sp_ptbBM102 8.980e-01 7.777e-01 1.155 0.24823
## sp_ptbBM103 1.189e-01 7.449e-01 0.160 0.87315
## sp_ptbBM104 1.246e+00 7.899e-01 1.578 0.11463
## sp_ptbBM105 7.432e-01 8.418e-01 0.883 0.37731
## sp_ptbBM106 -7.517e-01 1.117e+00 -0.673 0.50088
## sp_ptbBM107 6.143e-01 2.314e+00 0.265 0.79066
## sp_ptbBM108 -1.414e-01 1.697e+00 -0.083 0.93357
## sp_ptbBM109 -1.755e+00 1.922e+00 -0.913 0.36101
## sp_ptbBM110 -2.185e+00 2.046e+00 -1.068 0.28552
## sp_ptbBM111 -2.549e+00 1.879e+00 -1.356 0.17500
## sp_ptbBM112 -2.549e+00 1.507e+00 -1.692 0.09068 .
## sp_ptbBM113 -1.995e+00 1.795e+00 -1.111 0.26653
## sp_ptbBM114 1.383e+00 9.734e-01 1.421 0.15531
## sp_ptbBM115 -7.118e-01 8.234e-01 -0.864 0.38732
## sp_ptbBM116 2.296e+00 8.302e-01 2.766 0.00567 **
## sp_ptbBM117 -1.880e+00 7.918e-01 -2.375 0.01757 *
## sp_ptbBM118 2.008e+00 7.564e-01 2.655 0.00794 **
## sp_ptbBM119 -2.215e+00 8.661e-01 -2.557 0.01056 *
## sp_ptbBM120 -2.270e-01 8.858e-01 -0.256 0.79778
## sp_ptbBM121 1.533e-01 8.212e-01 0.187 0.85189
## sp_ptbBM122 9.081e-01 6.757e-01 1.344 0.17897
## sp_ptbBM123 3.315e-01 5.787e-01 0.573 0.56678
## sp_ptbBM124 3.568e-01 7.566e-01 0.472 0.63722
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22297.04) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 939.93 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3230.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22297
## Std. Err.: 142938
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2972.79scatter.smooth(predict(m8a, type='response'), rstandard(m8a, type='deviance'), col='gray')
m8a.resid<-residuals(m8a, type="deviance")
m8a.pred<-predict(m8a, type="response")
length(m8a.resid); length(m8a.pred)## [1] 939## [1] 939pacf(m8a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m8a.ac<-update(m8a,.~.+lag(m8a.resid,1)+lag(m8a.resid,2)+lag(m8a.resid,3)+lag(m8a.resid,4)+
lag(m8a.resid,5)+lag(m8a.resid,6)+lag(m8a.resid,7)+lag(m8a.resid,8)+
lag(m8a.resid,9)+lag(m8a.resid,10)+lag(m8a.resid,11)+lag(m8a.resid,12)+
lag(m8a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m8a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb8.AH + sp_ptbBM + lag(m8a.resid, 1) +
## lag(m8a.resid, 2) + lag(m8a.resid, 3) + lag(m8a.resid, 4) +
## lag(m8a.resid, 5) + lag(m8a.resid, 6) + lag(m8a.resid, 7) +
## lag(m8a.resid, 8) + lag(m8a.resid, 9) + lag(m8a.resid, 10) +
## lag(m8a.resid, 11) + lag(m8a.resid, 12) + lag(m8a.resid,
## 25), data = week, init.theta = 47802.64921, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.50652 -0.66361 -0.02604 0.46941 2.10555
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.15574 12.35106 -1.389 0.164830
## cb8.AHv1.l1 0.35712 0.30908 1.155 0.247908
## cb8.AHv1.l2 -0.41483 0.21555 -1.925 0.054288 .
## cb8.AHv2.l1 1.47872 1.01216 1.461 0.144029
## cb8.AHv2.l2 -0.19375 0.73327 -0.264 0.791609
## cb8.AHv3.l1 0.84988 0.56301 1.510 0.131162
## cb8.AHv3.l2 0.65115 0.41081 1.585 0.112962
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -3.45844 44.05097 -0.079 0.937422
## sp_ptbBM9 4.66555 4.19575 1.112 0.266151
## sp_ptbBM10 -1.44788 2.12662 -0.681 0.495975
## sp_ptbBM11 2.75904 1.67330 1.649 0.099176 .
## sp_ptbBM12 1.26738 1.66400 0.762 0.446270
## sp_ptbBM13 2.78192 1.40823 1.975 0.048215 *
## sp_ptbBM14 -0.66649 1.45923 -0.457 0.647857
## sp_ptbBM15 3.12709 1.48725 2.103 0.035500 *
## sp_ptbBM16 0.55172 1.59685 0.346 0.729716
## sp_ptbBM17 2.33508 1.58019 1.478 0.139483
## sp_ptbBM18 0.15600 1.56063 0.100 0.920379
## sp_ptbBM19 2.94281 1.54389 1.906 0.056637 .
## sp_ptbBM20 -1.41874 1.64561 -0.862 0.388615
## sp_ptbBM21 3.14067 1.86900 1.680 0.092880 .
## sp_ptbBM22 2.61164 2.02389 1.290 0.196911
## sp_ptbBM23 2.98754 2.66250 1.122 0.261829
## sp_ptbBM24 5.78354 3.02657 1.911 0.056015 .
## sp_ptbBM25 5.32643 3.71203 1.435 0.151313
## sp_ptbBM26 8.63012 3.91531 2.204 0.027510 *
## sp_ptbBM27 3.13449 4.13203 0.759 0.448102
## sp_ptbBM28 5.66202 4.17731 1.355 0.175283
## sp_ptbBM29 4.90408 3.93339 1.247 0.212477
## sp_ptbBM30 5.07205 3.95898 1.281 0.200140
## sp_ptbBM31 4.58259 3.87591 1.182 0.237076
## sp_ptbBM32 5.05478 3.69988 1.366 0.171875
## sp_ptbBM33 3.78064 3.86289 0.979 0.327724
## sp_ptbBM34 7.41980 4.07936 1.819 0.068932 .
## sp_ptbBM35 5.48322 4.30156 1.275 0.202414
## sp_ptbBM36 7.24660 4.10115 1.767 0.077234 .
## sp_ptbBM37 3.83268 3.85963 0.993 0.320701
## sp_ptbBM38 3.62712 3.51192 1.033 0.301695
## sp_ptbBM39 3.50649 3.37130 1.040 0.298292
## sp_ptbBM40 2.43780 3.13812 0.777 0.437257
## sp_ptbBM41 2.89942 2.73294 1.061 0.288729
## sp_ptbBM42 -0.54313 2.60248 -0.209 0.834685
## sp_ptbBM43 4.44894 2.68148 1.659 0.097089 .
## sp_ptbBM44 -0.09049 2.52186 -0.036 0.971378
## sp_ptbBM45 3.68936 2.71397 1.359 0.174022
## sp_ptbBM46 2.05407 2.79705 0.734 0.462725
## sp_ptbBM47 4.28859 2.92911 1.464 0.143158
## sp_ptbBM48 2.77140 3.01906 0.918 0.358636
## sp_ptbBM49 4.04033 3.01810 1.339 0.180668
## sp_ptbBM50 2.85105 2.87666 0.991 0.321639
## sp_ptbBM51 3.27815 3.04745 1.076 0.282060
## sp_ptbBM52 2.69967 2.96360 0.911 0.362325
## sp_ptbBM53 2.78521 2.89270 0.963 0.335628
## sp_ptbBM54 3.15198 2.65740 1.186 0.235577
## sp_ptbBM55 0.81494 2.51033 0.325 0.745459
## sp_ptbBM56 2.51495 2.41156 1.043 0.297009
## sp_ptbBM57 2.23354 2.23389 1.000 0.317384
## sp_ptbBM58 1.68285 2.09688 0.803 0.422235
## sp_ptbBM59 0.76842 2.11479 0.363 0.716338
## sp_ptbBM60 2.60500 2.03737 1.279 0.201034
## sp_ptbBM61 0.66531 2.01592 0.330 0.741378
## sp_ptbBM62 3.31508 2.07226 1.600 0.109655
## sp_ptbBM63 -1.45718 2.01827 -0.722 0.470298
## sp_ptbBM64 4.50646 1.83143 2.461 0.013870 *
## sp_ptbBM65 -0.18103 1.63025 -0.111 0.911582
## sp_ptbBM66 3.12772 1.29738 2.411 0.015917 *
## sp_ptbBM67 0.82570 1.05011 0.786 0.431696
## sp_ptbBM68 1.53547 1.04122 1.475 0.140298
## sp_ptbBM69 1.20634 1.12961 1.068 0.285556
## sp_ptbBM70 0.98463 1.44102 0.683 0.494427
## sp_ptbBM71 -1.06223 1.75147 -0.606 0.544195
## sp_ptbBM72 2.73497 1.97570 1.384 0.166265
## sp_ptbBM73 -1.36391 1.97706 -0.690 0.490280
## sp_ptbBM74 3.99969 2.01689 1.983 0.047356 *
## sp_ptbBM75 -1.97443 1.95018 -1.012 0.311332
## sp_ptbBM76 4.26082 1.82890 2.330 0.019821 *
## sp_ptbBM77 -1.14546 1.64282 -0.697 0.485645
## sp_ptbBM78 3.88528 1.52598 2.546 0.010894 *
## sp_ptbBM79 1.39151 1.54847 0.899 0.368848
## sp_ptbBM80 2.79879 1.64237 1.704 0.088358 .
## sp_ptbBM81 -0.19933 1.65598 -0.120 0.904189
## sp_ptbBM82 4.06586 1.70153 2.390 0.016869 *
## sp_ptbBM83 -1.23424 1.63007 -0.757 0.448950
## sp_ptbBM84 3.39476 1.67962 2.021 0.043264 *
## sp_ptbBM85 -0.40054 1.49368 -0.268 0.788578
## sp_ptbBM86 2.45603 1.35856 1.808 0.070634 .
## sp_ptbBM87 1.79143 1.22217 1.466 0.142709
## sp_ptbBM88 1.13556 1.20674 0.941 0.346696
## sp_ptbBM89 0.42180 1.38337 0.305 0.760435
## sp_ptbBM90 -0.74652 1.09270 -0.683 0.494486
## sp_ptbBM91 1.61066 1.37213 1.174 0.240458
## sp_ptbBM92 1.60802 1.35408 1.188 0.235014
## sp_ptbBM93 2.79823 1.41486 1.978 0.047958 *
## sp_ptbBM94 0.82614 1.26884 0.651 0.514985
## sp_ptbBM95 0.48544 1.10401 0.440 0.660152
## sp_ptbBM96 1.25915 1.01168 1.245 0.213275
## sp_ptbBM97 0.32722 1.03096 0.317 0.750944
## sp_ptbBM98 -3.59065 1.00931 -3.558 0.000374 ***
## sp_ptbBM99 1.06502 1.05568 1.009 0.313048
## sp_ptbBM100 0.63578 1.04693 0.607 0.543664
## sp_ptbBM101 2.74341 0.92105 2.979 0.002896 **
## sp_ptbBM102 1.31140 0.74940 1.750 0.080127 .
## sp_ptbBM103 -0.54208 0.74839 -0.724 0.468864
## sp_ptbBM104 1.57814 0.79603 1.983 0.047421 *
## sp_ptbBM105 0.03393 0.83187 0.041 0.967463
## sp_ptbBM106 0.11400 1.23926 0.092 0.926708
## sp_ptbBM107 -1.68632 2.34382 -0.719 0.471847
## sp_ptbBM108 -0.13190 1.73541 -0.076 0.939416
## sp_ptbBM109 -1.39225 1.99230 -0.699 0.484666
## sp_ptbBM110 -3.35984 2.16833 -1.550 0.121260
## sp_ptbBM111 -2.68947 1.93947 -1.387 0.165532
## sp_ptbBM112 -4.26337 1.60017 -2.664 0.007714 **
## sp_ptbBM113 -3.92581 1.89436 -2.072 0.038232 *
## sp_ptbBM114 1.30914 0.97650 1.341 0.180035
## sp_ptbBM115 -0.83724 0.81722 -1.024 0.305600
## sp_ptbBM116 2.54213 0.83794 3.034 0.002415 **
## sp_ptbBM117 -0.80240 0.81386 -0.986 0.324171
## sp_ptbBM118 1.66113 0.75748 2.193 0.028309 *
## sp_ptbBM119 -1.23068 0.92333 -1.333 0.182574
## sp_ptbBM120 -0.72028 0.91728 -0.785 0.432314
## sp_ptbBM121 -0.45026 0.85096 -0.529 0.596722
## sp_ptbBM122 1.64656 0.66655 2.470 0.013501 *
## sp_ptbBM123 -0.14508 0.57564 -0.252 0.801019
## sp_ptbBM124 1.43400 0.78430 1.828 0.067493 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m8a.resid, 1) -0.44115 0.03061 -14.410 < 2e-16 ***
## lag(m8a.resid, 2) -0.49075 0.03589 -13.675 < 2e-16 ***
## lag(m8a.resid, 3) -0.57415 0.03760 -15.269 < 2e-16 ***
## lag(m8a.resid, 4) -0.58671 0.04080 -14.380 < 2e-16 ***
## lag(m8a.resid, 5) -0.58830 0.04110 -14.313 < 2e-16 ***
## lag(m8a.resid, 6) -0.54004 0.04168 -12.957 < 2e-16 ***
## lag(m8a.resid, 7) -0.51438 0.03954 -13.008 < 2e-16 ***
## lag(m8a.resid, 8) -0.46039 0.03798 -12.123 < 2e-16 ***
## lag(m8a.resid, 9) -0.36457 0.03541 -10.296 < 2e-16 ***
## lag(m8a.resid, 10) -0.31370 0.03229 -9.714 < 2e-16 ***
## lag(m8a.resid, 11) -0.21136 0.03129 -6.755 1.43e-11 ***
## lag(m8a.resid, 12) -0.17335 0.02911 -5.956 2.59e-09 ***
## lag(m8a.resid, 25) -0.04805 0.02443 -1.967 0.049142 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(47802.65) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 545.03 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2802.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 47803
## Std. Err.: 188182
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2526.95m8a.resid_ac<-residuals(m8a.ac, type="deviance")
m8a.pred_ac<-predict(m8a.ac, type="response")
pacf(m8a.resid_ac,na.action = na.omit) 
length(m8a.pred_ac); length(m8a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m8a.pred,lwd=1, col="blue")
plot(week$time,m8a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m8a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m8a.pred_ac,lwd=1, col="blue")
plot(week$time,m8a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m8a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m8a <- crosspred(cb8.AH, m8a.ac, cen = 31.2, by=0.1,cumul=TRUE)
##for m9a minT ######
summary(m9a)##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBM, data = week, init.theta = 22708.48563,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.44984 -0.78014 -0.08793 0.55896 2.66282
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.322e+00 1.044e+01 -0.318 0.75034
## cb9.minTv1.l1 1.134e-01 2.555e-01 0.444 0.65721
## cb9.minTv1.l2 -1.702e-01 1.800e-01 -0.945 0.34455
## cb9.minTv2.l1 8.252e-01 8.811e-01 0.937 0.34897
## cb9.minTv2.l2 -5.810e-01 6.655e-01 -0.873 0.38262
## cb9.minTv3.l1 9.618e-01 4.965e-01 1.937 0.05275 .
## cb9.minTv3.l2 3.776e-01 3.596e-01 1.050 0.29368
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.435e+06 2.224e+06 -0.645 0.51866
## sp_ptbBM5 1.492e+01 1.183e+01 1.262 0.20704
## sp_ptbBM6 -2.680e+00 1.973e+00 -1.358 0.17446
## sp_ptbBM7 6.651e-01 1.121e+00 0.594 0.55284
## sp_ptbBM8 -1.772e+00 1.063e+00 -1.667 0.09549 .
## sp_ptbBM9 6.644e-01 9.941e-01 0.668 0.50392
## sp_ptbBM10 -3.213e+00 1.268e+00 -2.534 0.01128 *
## sp_ptbBM11 6.634e-01 8.838e-01 0.751 0.45286
## sp_ptbBM12 -1.021e+00 9.257e-01 -1.103 0.27023
## sp_ptbBM13 1.666e-01 8.768e-01 0.190 0.84928
## sp_ptbBM14 -1.553e+00 9.743e-01 -1.594 0.11101
## sp_ptbBM15 -1.366e-01 9.026e-01 -0.151 0.87967
## sp_ptbBM16 -1.149e+00 1.099e+00 -1.045 0.29607
## sp_ptbBM17 -5.154e-01 9.746e-01 -0.529 0.59689
## sp_ptbBM18 -1.138e+00 1.057e+00 -1.078 0.28124
## sp_ptbBM19 -1.443e+00 1.099e+00 -1.313 0.18919
## sp_ptbBM20 -1.782e+00 1.066e+00 -1.671 0.09474 .
## sp_ptbBM21 -6.449e-01 9.245e-01 -0.698 0.48547
## sp_ptbBM22 -1.005e+00 9.002e-01 -1.117 0.26416
## sp_ptbBM23 -1.045e+00 9.345e-01 -1.118 0.26355
## sp_ptbBM24 -1.940e-01 8.407e-01 -0.231 0.81748
## sp_ptbBM25 -5.120e-01 7.572e-01 -0.676 0.49889
## sp_ptbBM26 -2.764e-01 8.161e-01 -0.339 0.73490
## sp_ptbBM27 -2.206e+00 1.058e+00 -2.085 0.03709 *
## sp_ptbBM28 -4.163e-01 9.248e-01 -0.450 0.65262
## sp_ptbBM29 4.159e-01 7.380e-01 0.564 0.57304
## sp_ptbBM30 -1.018e-01 8.372e-01 -0.122 0.90317
## sp_ptbBM31 9.503e-01 8.883e-01 1.070 0.28470
## sp_ptbBM32 -3.709e-01 9.229e-01 -0.402 0.68779
## sp_ptbBM33 -1.055e-01 1.014e+00 -0.104 0.91716
## sp_ptbBM34 8.521e-01 1.394e+00 0.611 0.54107
## sp_ptbBM35 8.474e-01 1.417e+00 0.598 0.54986
## sp_ptbBM36 2.490e+00 1.624e+00 1.533 0.12517
## sp_ptbBM37 1.004e+00 1.983e+00 0.506 0.61261
## sp_ptbBM38 1.911e+00 1.878e+00 1.018 0.30883
## sp_ptbBM39 2.122e-01 1.784e+00 0.119 0.90532
## sp_ptbBM40 9.172e-01 1.787e+00 0.513 0.60775
## sp_ptbBM41 -1.716e-01 1.355e+00 -0.127 0.89922
## sp_ptbBM42 -3.244e-01 1.209e+00 -0.268 0.78840
## sp_ptbBM43 7.194e-02 1.121e+00 0.064 0.94885
## sp_ptbBM44 -4.041e-02 7.925e-01 -0.051 0.95934
## sp_ptbBM45 7.014e-01 7.960e-01 0.881 0.37825
## sp_ptbBM46 -1.302e-01 8.519e-01 -0.153 0.87858
## sp_ptbBM47 6.420e-01 7.985e-01 0.804 0.42141
## sp_ptbBM48 -6.717e-02 9.677e-01 -0.069 0.94466
## sp_ptbBM49 1.500e+00 8.535e-01 1.758 0.07877 .
## sp_ptbBM50 1.413e-01 1.007e+00 0.140 0.88837
## sp_ptbBM51 1.944e+00 9.530e-01 2.040 0.04133 *
## sp_ptbBM52 4.737e-01 1.195e+00 0.396 0.69180
## sp_ptbBM53 1.486e+00 1.167e+00 1.273 0.20291
## sp_ptbBM54 6.195e-01 1.194e+00 0.519 0.60397
## sp_ptbBM55 -3.935e-01 1.113e+00 -0.354 0.72353
## sp_ptbBM56 8.898e-01 1.152e+00 0.773 0.43967
## sp_ptbBM57 5.793e-01 9.635e-01 0.601 0.54766
## sp_ptbBM58 5.807e-01 9.589e-01 0.606 0.54479
## sp_ptbBM59 -5.273e-01 8.295e-01 -0.636 0.52500
## sp_ptbBM60 -1.004e-02 7.749e-01 -0.013 0.98967
## sp_ptbBM61 -1.825e-02 7.456e-01 -0.024 0.98047
## sp_ptbBM62 -9.422e-01 8.362e-01 -1.127 0.25982
## sp_ptbBM63 -7.932e-01 8.777e-01 -0.904 0.36617
## sp_ptbBM64 -1.101e+00 1.238e+00 -0.889 0.37386
## sp_ptbBM65 -5.541e-01 1.316e+00 -0.421 0.67375
## sp_ptbBM66 -1.589e+00 1.256e+00 -1.265 0.20574
## sp_ptbBM67 -1.098e+00 1.177e+00 -0.932 0.35117
## sp_ptbBM68 -2.007e+00 1.230e+00 -1.632 0.10276
## sp_ptbBM69 -2.327e+00 1.253e+00 -1.858 0.06317 .
## sp_ptbBM70 -3.010e+00 1.256e+00 -2.396 0.01657 *
## sp_ptbBM71 -1.360e+00 9.716e-01 -1.400 0.16147
## sp_ptbBM72 -3.090e-01 8.510e-01 -0.363 0.71653
## sp_ptbBM73 -4.264e-01 8.201e-01 -0.520 0.60308
## sp_ptbBM74 8.071e-01 7.428e-01 1.087 0.27723
## sp_ptbBM75 9.959e-02 8.366e-01 0.119 0.90524
## sp_ptbBM76 9.570e-02 7.864e-01 0.122 0.90314
## sp_ptbBM77 -1.083e-01 8.202e-01 -0.132 0.89498
## sp_ptbBM78 3.518e-01 8.962e-01 0.393 0.69464
## sp_ptbBM79 1.076e-01 9.084e-01 0.118 0.90573
## sp_ptbBM80 -3.359e-01 8.887e-01 -0.378 0.70549
## sp_ptbBM81 -2.242e-01 8.860e-01 -0.253 0.80021
## sp_ptbBM82 -4.763e-01 9.014e-01 -0.528 0.59725
## sp_ptbBM83 -9.418e-01 9.605e-01 -0.980 0.32687
## sp_ptbBM84 -2.210e-01 1.078e+00 -0.205 0.83758
## sp_ptbBM85 -2.798e+00 1.328e+00 -2.108 0.03504 *
## sp_ptbBM86 1.034e-03 1.326e+00 0.001 0.99938
## sp_ptbBM87 -1.030e+00 1.328e+00 -0.776 0.43794
## sp_ptbBM88 -1.461e+00 1.297e+00 -1.126 0.26005
## sp_ptbBM89 -2.816e+00 1.325e+00 -2.125 0.03355 *
## sp_ptbBM90 -2.019e+00 1.399e+00 -1.443 0.14907
## sp_ptbBM91 -1.832e+00 1.071e+00 -1.711 0.08711 .
## sp_ptbBM92 -3.685e-01 1.001e+00 -0.368 0.71286
## sp_ptbBM93 -6.993e-01 1.146e+00 -0.610 0.54170
## sp_ptbBM94 -8.515e-01 1.197e+00 -0.712 0.47672
## sp_ptbBM95 -1.155e+00 1.185e+00 -0.975 0.32974
## sp_ptbBM96 -8.478e-01 1.157e+00 -0.733 0.46358
## sp_ptbBM97 -2.077e+00 1.328e+00 -1.564 0.11781
## sp_ptbBM98 -2.802e+00 1.392e+00 -2.013 0.04408 *
## sp_ptbBM99 -2.033e+00 1.378e+00 -1.476 0.13995
## sp_ptbBM100 -7.991e-01 1.388e+00 -0.576 0.56484
## sp_ptbBM101 -8.634e-01 1.511e+00 -0.571 0.56778
## sp_ptbBM102 -1.342e+00 1.392e+00 -0.964 0.33526
## sp_ptbBM103 -2.453e+00 1.568e+00 -1.565 0.11769
## sp_ptbBM104 -2.161e+00 1.513e+00 -1.428 0.15327
## sp_ptbBM105 -3.735e+00 2.333e+00 -1.601 0.10939
## sp_ptbBM106 -5.452e+00 3.255e+00 -1.675 0.09400 .
## sp_ptbBM107 -5.587e+00 3.962e+00 -1.410 0.15853
## sp_ptbBM108 -5.357e+00 3.465e+00 -1.546 0.12212
## sp_ptbBM109 -7.613e+00 3.713e+00 -2.050 0.04035 *
## sp_ptbBM110 -8.008e+00 3.537e+00 -2.264 0.02355 *
## sp_ptbBM111 -1.019e+01 3.956e+00 -2.575 0.01001 *
## sp_ptbBM112 -8.838e+00 3.343e+00 -2.644 0.00819 **
## sp_ptbBM113 -5.066e+00 2.196e+00 -2.307 0.02104 *
## sp_ptbBM114 -1.927e+00 1.466e+00 -1.314 0.18881
## sp_ptbBM115 -3.225e+00 1.376e+00 -2.345 0.01903 *
## sp_ptbBM116 4.348e-01 1.312e+00 0.332 0.74022
## sp_ptbBM117 -2.960e+00 1.098e+00 -2.696 0.00703 **
## sp_ptbBM118 7.208e-01 8.860e-01 0.814 0.41591
## sp_ptbBM119 -2.337e+00 9.264e-01 -2.522 0.01166 *
## sp_ptbBM120 1.540e-02 8.160e-01 0.019 0.98494
## sp_ptbBM121 -6.600e-01 7.197e-01 -0.917 0.35913
## sp_ptbBM122 7.531e-01 6.602e-01 1.141 0.25401
## sp_ptbBM123 5.647e-01 7.057e-01 0.800 0.42361
## sp_ptbBM124 9.450e-01 7.674e-01 1.231 0.21817
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22708.49) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 936.01 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3226.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22708
## Std. Err.: 141707
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2968.878scatter.smooth(predict(m9a, type='response'), rstandard(m9a, type='deviance'), col='gray')
m9a.resid<-residuals(m9a, type="deviance")
m9a.pred<-predict(m9a, type="response")
length(m9a.resid); length(m9a.pred)## [1] 939## [1] 939pacf(m9a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
#ensure that the lags are dplyr lags
m9a.ac<-update(m9a,.~.+lag(m9a.resid,1)+lag(m9a.resid,2)+lag(m9a.resid,3)+lag(m9a.resid,4)+
lag(m9a.resid,5)+lag(m9a.resid,6)+lag(m9a.resid,7)+lag(m9a.resid,8)+
lag(m9a.resid,9)+lag(m9a.resid,10)+lag(m9a.resid,11)+lag(m9a.resid,12)+
lag(m9a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m9a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBM + lag(m9a.resid,
## 1) + lag(m9a.resid, 2) + lag(m9a.resid, 3) + lag(m9a.resid,
## 4) + lag(m9a.resid, 5) + lag(m9a.resid, 6) + lag(m9a.resid,
## 7) + lag(m9a.resid, 8) + lag(m9a.resid, 9) + lag(m9a.resid,
## 10) + lag(m9a.resid, 11) + lag(m9a.resid, 12) + lag(m9a.resid,
## 25), data = week, init.theta = 48584.26047, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.54963 -0.64029 -0.04615 0.45954 2.13333
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -17.92999 11.27505 -1.590 0.111782
## cb9.minTv1.l1 0.12948 0.26962 0.480 0.631055
## cb9.minTv1.l2 -0.10206 0.18890 -0.540 0.589012
## cb9.minTv2.l1 1.88918 0.94196 2.006 0.044902 *
## cb9.minTv2.l2 0.32040 0.70765 0.453 0.650713
## cb9.minTv3.l1 1.13135 0.50905 2.222 0.026250 *
## cb9.minTv3.l2 0.72744 0.37896 1.920 0.054909 .
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -11.77554 43.69902 -0.269 0.787569
## sp_ptbBM9 2.33562 3.98798 0.586 0.558101
## sp_ptbBM10 -3.32554 1.79050 -1.857 0.063264 .
## sp_ptbBM11 -0.06724 0.99621 -0.067 0.946190
## sp_ptbBM12 -0.88199 1.01510 -0.869 0.384920
## sp_ptbBM13 0.80486 0.89665 0.898 0.369385
## sp_ptbBM14 -2.65752 0.99122 -2.681 0.007339 **
## sp_ptbBM15 1.78597 0.91170 1.959 0.050120 .
## sp_ptbBM16 -2.05426 1.09376 -1.878 0.060358 .
## sp_ptbBM17 0.39258 0.98271 0.399 0.689535
## sp_ptbBM18 -2.05266 1.08432 -1.893 0.058353 .
## sp_ptbBM19 0.32505 1.11795 0.291 0.771238
## sp_ptbBM20 -2.53126 1.10168 -2.298 0.021582 *
## sp_ptbBM21 -0.34768 0.96473 -0.360 0.718553
## sp_ptbBM22 -0.62475 0.92212 -0.678 0.498077
## sp_ptbBM23 -1.73651 0.93552 -1.856 0.063424 .
## sp_ptbBM24 0.87481 0.83970 1.042 0.297501
## sp_ptbBM25 -2.15393 0.76900 -2.801 0.005095 **
## sp_ptbBM26 1.47036 0.83299 1.765 0.077538 .
## sp_ptbBM27 -3.90057 1.04019 -3.750 0.000177 ***
## sp_ptbBM28 -0.02953 0.92918 -0.032 0.974649
## sp_ptbBM29 -0.86284 0.74480 -1.158 0.246666
## sp_ptbBM30 0.80400 0.86771 0.927 0.354146
## sp_ptbBM31 -0.73244 0.92590 -0.791 0.428912
## sp_ptbBM32 0.41544 0.97044 0.428 0.668581
## sp_ptbBM33 -1.88689 1.04103 -1.813 0.069906 .
## sp_ptbBM34 2.44455 1.42843 1.711 0.087016 .
## sp_ptbBM35 -0.41659 1.45006 -0.287 0.773892
## sp_ptbBM36 3.44275 1.68323 2.045 0.040823 *
## sp_ptbBM37 1.34201 2.12123 0.633 0.526957
## sp_ptbBM38 2.45477 2.01118 1.221 0.222252
## sp_ptbBM39 1.66633 1.91050 0.872 0.383101
## sp_ptbBM40 1.22413 1.91224 0.640 0.522073
## sp_ptbBM41 1.32042 1.47152 0.897 0.369549
## sp_ptbBM42 -2.59301 1.29432 -2.003 0.045137 *
## sp_ptbBM43 1.68479 1.22624 1.374 0.169457
## sp_ptbBM44 -2.03927 0.77449 -2.633 0.008462 **
## sp_ptbBM45 0.59186 0.82112 0.721 0.471031
## sp_ptbBM46 -1.08517 0.85059 -1.276 0.202032
## sp_ptbBM47 -0.17857 0.79354 -0.225 0.821952
## sp_ptbBM48 -1.07144 0.98633 -1.086 0.277351
## sp_ptbBM49 0.61061 0.86714 0.704 0.481329
## sp_ptbBM50 -0.33008 1.00862 -0.327 0.743470
## sp_ptbBM51 1.14973 0.99312 1.158 0.246990
## sp_ptbBM52 0.41895 1.22447 0.342 0.732239
## sp_ptbBM53 0.83425 1.19708 0.697 0.485860
## sp_ptbBM54 0.65737 1.21890 0.539 0.589673
## sp_ptbBM55 -0.70899 1.18041 -0.601 0.548088
## sp_ptbBM56 0.39366 1.18353 0.333 0.739424
## sp_ptbBM57 0.58938 0.98285 0.600 0.548729
## sp_ptbBM58 0.27043 0.97406 0.278 0.781292
## sp_ptbBM59 -1.05849 0.84666 -1.250 0.211227
## sp_ptbBM60 0.35508 0.77583 0.458 0.647185
## sp_ptbBM61 -1.72680 0.80615 -2.142 0.032191 *
## sp_ptbBM62 0.93795 0.86085 1.090 0.275902
## sp_ptbBM63 -3.49969 0.95441 -3.667 0.000246 ***
## sp_ptbBM64 2.35921 1.29931 1.816 0.069411 .
## sp_ptbBM65 -2.21232 1.36294 -1.623 0.104547
## sp_ptbBM66 1.66722 1.29960 1.283 0.199539
## sp_ptbBM67 -1.56366 1.18580 -1.319 0.187287
## sp_ptbBM68 -0.62535 1.25206 -0.499 0.617458
## sp_ptbBM69 -1.65085 1.25479 -1.316 0.188295
## sp_ptbBM70 -2.31515 1.26592 -1.829 0.067426 .
## sp_ptbBM71 -1.90788 1.04884 -1.819 0.068906 .
## sp_ptbBM72 -0.16156 0.84591 -0.191 0.848530
## sp_ptbBM73 -3.32981 0.92101 -3.615 0.000300 ***
## sp_ptbBM74 1.64542 0.78590 2.094 0.036289 *
## sp_ptbBM75 -4.08949 0.87803 -4.658 3.20e-06 ***
## sp_ptbBM76 2.66330 0.85774 3.105 0.001903 **
## sp_ptbBM77 -3.19538 0.83469 -3.828 0.000129 ***
## sp_ptbBM78 2.10358 0.92507 2.274 0.022968 *
## sp_ptbBM79 -0.19103 0.93692 -0.204 0.838434
## sp_ptbBM80 0.71416 0.88703 0.805 0.420753
## sp_ptbBM81 -2.32497 0.93734 -2.480 0.013123 *
## sp_ptbBM82 1.53281 0.93386 1.641 0.100721
## sp_ptbBM83 -3.45508 0.99846 -3.460 0.000539 ***
## sp_ptbBM84 0.85256 1.14193 0.747 0.455308
## sp_ptbBM85 -2.32934 1.36798 -1.703 0.088614 .
## sp_ptbBM86 0.72009 1.34167 0.537 0.591465
## sp_ptbBM87 0.70681 1.35479 0.522 0.601872
## sp_ptbBM88 -1.20688 1.33761 -0.902 0.366913
## sp_ptbBM89 -1.59501 1.37293 -1.162 0.245335
## sp_ptbBM90 -3.15265 1.43116 -2.203 0.027604 *
## sp_ptbBM91 -0.59525 1.09584 -0.543 0.587000
## sp_ptbBM92 0.16381 0.99724 0.164 0.869521
## sp_ptbBM93 0.82416 1.16478 0.708 0.479217
## sp_ptbBM94 -0.07233 1.21258 -0.060 0.952434
## sp_ptbBM95 -0.51077 1.20035 -0.426 0.670458
## sp_ptbBM96 0.30389 1.22404 0.248 0.803926
## sp_ptbBM97 -0.60019 1.40101 -0.428 0.668362
## sp_ptbBM98 -5.26841 1.46483 -3.597 0.000322 ***
## sp_ptbBM99 -0.25961 1.44278 -0.180 0.857201
## sp_ptbBM100 -0.10166 1.40432 -0.072 0.942292
## sp_ptbBM101 0.87415 1.54252 0.567 0.570916
## sp_ptbBM102 0.01507 1.41036 0.011 0.991475
## sp_ptbBM103 -2.21250 1.60557 -1.378 0.168198
## sp_ptbBM104 -0.70619 1.54163 -0.458 0.646896
## sp_ptbBM105 -2.30641 2.38525 -0.967 0.333571
## sp_ptbBM106 -2.76368 3.28265 -0.842 0.399840
## sp_ptbBM107 -5.09035 3.96558 -1.284 0.199271
## sp_ptbBM108 -3.21843 3.51979 -0.914 0.360516
## sp_ptbBM109 -5.24264 3.77158 -1.390 0.164518
## sp_ptbBM110 -7.19909 3.60788 -1.995 0.046001 *
## sp_ptbBM111 -8.85142 4.08401 -2.167 0.030209 *
## sp_ptbBM112 -9.60468 3.45458 -2.780 0.005431 **
## sp_ptbBM113 -5.17311 2.29520 -2.254 0.024204 *
## sp_ptbBM114 -1.45785 1.48133 -0.984 0.325041
## sp_ptbBM115 -1.92993 1.41593 -1.363 0.172879
## sp_ptbBM116 1.04082 1.35417 0.769 0.442129
## sp_ptbBM117 -1.16061 1.12689 -1.030 0.303049
## sp_ptbBM118 0.77808 0.89890 0.866 0.386712
## sp_ptbBM119 -1.87284 0.98055 -1.910 0.056136 .
## sp_ptbBM120 -0.91022 0.83211 -1.094 0.274008
## sp_ptbBM121 -1.70145 0.73630 -2.311 0.020844 *
## sp_ptbBM122 0.95680 0.64191 1.491 0.136081
## sp_ptbBM123 -0.42738 0.72151 -0.592 0.553621
## sp_ptbBM124 1.64192 0.75771 2.167 0.030240 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m9a.resid, 1) -0.44140 0.03073 -14.365 < 2e-16 ***
## lag(m9a.resid, 2) -0.49172 0.03617 -13.596 < 2e-16 ***
## lag(m9a.resid, 3) -0.57739 0.03820 -15.114 < 2e-16 ***
## lag(m9a.resid, 4) -0.58592 0.04113 -14.245 < 2e-16 ***
## lag(m9a.resid, 5) -0.58758 0.04143 -14.182 < 2e-16 ***
## lag(m9a.resid, 6) -0.54030 0.04204 -12.852 < 2e-16 ***
## lag(m9a.resid, 7) -0.50980 0.03975 -12.824 < 2e-16 ***
## lag(m9a.resid, 8) -0.45365 0.03772 -12.027 < 2e-16 ***
## lag(m9a.resid, 9) -0.36065 0.03526 -10.228 < 2e-16 ***
## lag(m9a.resid, 10) -0.31006 0.03235 -9.585 < 2e-16 ***
## lag(m9a.resid, 11) -0.20263 0.03127 -6.480 9.17e-11 ***
## lag(m9a.resid, 12) -0.16662 0.02898 -5.750 8.91e-09 ***
## lag(m9a.resid, 25) -0.04316 0.02439 -1.770 0.076745 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(48584.26) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 546.04 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2804
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 48584
## Std. Err.: 193420
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2527.967m9a.resid_ac<-residuals(m9a.ac, type="deviance")
m9a.pred_ac<-predict(m9a.ac, type="response")
pacf(m9a.resid_ac,na.action = na.omit) 
length(m9a.pred_ac); length(m9a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m9a.pred,lwd=1, col="blue")
plot(week$time,m9a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m9a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m9a.pred_ac,lwd=1, col="blue")
plot(week$time,m9a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m9a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m9a <- crosspred(cb9.minT, m9a.ac, cen = 24.1, by=0.1,cumul=TRUE)
##for m10a aveT ######
summary(m10a)##
## Call:
## glm.nb(formula = ptbBM ~ cb10.aveT + sp_ptbBM, data = week, init.theta = 22332.14338,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5132 -0.7805 -0.1094 0.5463 2.6589
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.025e+01 1.493e+01 -1.356 0.17503
## cb10.aveTv1.l1 4.051e-01 3.235e-01 1.252 0.21054
## cb10.aveTv1.l2 -2.523e-01 2.396e-01 -1.053 0.29227
## cb10.aveTv2.l1 1.849e+00 1.184e+00 1.562 0.11838
## cb10.aveTv2.l2 -7.238e-01 9.349e-01 -0.774 0.43877
## cb10.aveTv3.l1 9.154e-01 4.986e-01 1.836 0.06639 .
## cb10.aveTv3.l2 1.515e-01 3.715e-01 0.408 0.68335
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.403e+06 2.229e+06 -0.629 0.52908
## sp_ptbBM5 1.666e+01 1.193e+01 1.397 0.16244
## sp_ptbBM6 -1.362e+00 2.024e+00 -0.673 0.50105
## sp_ptbBM7 2.175e+00 1.373e+00 1.584 0.11319
## sp_ptbBM8 -5.075e-01 1.075e+00 -0.472 0.63692
## sp_ptbBM9 1.555e+00 9.697e-01 1.603 0.10884
## sp_ptbBM10 -1.981e+00 1.067e+00 -1.857 0.06330 .
## sp_ptbBM11 1.885e+00 1.048e+00 1.799 0.07197 .
## sp_ptbBM12 1.831e-01 9.507e-01 0.193 0.84731
## sp_ptbBM13 1.543e+00 9.109e-01 1.694 0.09030 .
## sp_ptbBM14 -4.699e-01 1.076e+00 -0.437 0.66228
## sp_ptbBM15 8.201e-01 1.046e+00 0.784 0.43312
## sp_ptbBM16 -3.206e-01 9.095e-01 -0.353 0.72442
## sp_ptbBM17 9.263e-01 9.644e-01 0.960 0.33682
## sp_ptbBM18 5.282e-01 9.296e-01 0.568 0.56993
## sp_ptbBM19 6.391e-01 9.700e-01 0.659 0.50999
## sp_ptbBM20 4.744e-01 1.032e+00 0.460 0.64581
## sp_ptbBM21 9.069e-01 1.134e+00 0.800 0.42374
## sp_ptbBM22 8.142e-01 1.038e+00 0.785 0.43268
## sp_ptbBM23 1.426e-01 1.060e+00 0.135 0.89298
## sp_ptbBM24 1.109e+00 1.100e+00 1.008 0.31326
## sp_ptbBM25 1.010e+00 1.235e+00 0.818 0.41345
## sp_ptbBM26 1.338e+00 1.196e+00 1.118 0.26342
## sp_ptbBM27 -3.375e-01 1.320e+00 -0.256 0.79825
## sp_ptbBM28 2.923e-01 1.384e+00 0.211 0.83279
## sp_ptbBM29 1.613e+00 1.135e+00 1.422 0.15517
## sp_ptbBM30 8.321e-01 1.037e+00 0.803 0.42223
## sp_ptbBM31 1.760e+00 1.032e+00 1.706 0.08800 .
## sp_ptbBM32 2.952e-01 1.022e+00 0.289 0.77276
## sp_ptbBM33 1.185e-01 1.116e+00 0.106 0.91545
## sp_ptbBM34 1.421e+00 1.154e+00 1.231 0.21822
## sp_ptbBM35 7.580e-01 1.289e+00 0.588 0.55657
## sp_ptbBM36 1.779e+00 1.152e+00 1.544 0.12247
## sp_ptbBM37 8.721e-01 1.348e+00 0.647 0.51773
## sp_ptbBM38 2.075e+00 1.134e+00 1.829 0.06739 .
## sp_ptbBM39 8.258e-01 1.247e+00 0.662 0.50795
## sp_ptbBM40 1.998e+00 1.308e+00 1.528 0.12653
## sp_ptbBM41 8.656e-01 1.154e+00 0.750 0.45333
## sp_ptbBM42 7.088e-01 1.395e+00 0.508 0.61136
## sp_ptbBM43 1.213e+00 1.155e+00 1.051 0.29335
## sp_ptbBM44 9.452e-01 1.010e+00 0.936 0.34937
## sp_ptbBM45 1.632e+00 1.036e+00 1.576 0.11514
## sp_ptbBM46 6.796e-01 1.117e+00 0.609 0.54285
## sp_ptbBM47 1.581e+00 1.119e+00 1.413 0.15756
## sp_ptbBM48 1.070e+00 1.239e+00 0.864 0.38748
## sp_ptbBM49 2.382e+00 1.287e+00 1.851 0.06418 .
## sp_ptbBM50 1.465e+00 1.496e+00 0.979 0.32766
## sp_ptbBM51 3.023e+00 1.298e+00 2.328 0.01989 *
## sp_ptbBM52 1.193e+00 1.388e+00 0.860 0.38993
## sp_ptbBM53 2.064e+00 1.373e+00 1.503 0.13285
## sp_ptbBM54 1.320e+00 1.383e+00 0.955 0.33952
## sp_ptbBM55 5.783e-01 1.362e+00 0.425 0.67106
## sp_ptbBM56 2.113e+00 1.469e+00 1.439 0.15020
## sp_ptbBM57 1.655e+00 1.412e+00 1.173 0.24094
## sp_ptbBM58 1.701e+00 1.047e+00 1.626 0.10404
## sp_ptbBM59 5.446e-01 9.788e-01 0.556 0.57798
## sp_ptbBM60 5.680e-01 8.822e-01 0.644 0.51968
## sp_ptbBM61 4.136e-01 7.678e-01 0.539 0.59008
## sp_ptbBM62 -6.182e-01 8.819e-01 -0.701 0.48331
## sp_ptbBM63 -6.669e-01 9.167e-01 -0.728 0.46691
## sp_ptbBM64 -7.836e-01 1.055e+00 -0.743 0.45774
## sp_ptbBM65 2.075e-01 7.861e-01 0.264 0.79182
## sp_ptbBM66 -1.993e-02 7.496e-01 -0.027 0.97878
## sp_ptbBM67 1.185e+00 7.481e-01 1.583 0.11333
## sp_ptbBM68 4.322e-01 7.588e-01 0.570 0.56894
## sp_ptbBM69 6.696e-01 9.520e-01 0.703 0.48183
## sp_ptbBM70 2.976e-01 1.239e+00 0.240 0.81026
## sp_ptbBM71 6.485e-01 1.401e+00 0.463 0.64335
## sp_ptbBM72 1.444e+00 1.323e+00 1.091 0.27523
## sp_ptbBM73 8.786e-01 1.140e+00 0.771 0.44088
## sp_ptbBM74 2.174e+00 1.064e+00 2.044 0.04095 *
## sp_ptbBM75 9.807e-01 1.115e+00 0.880 0.37906
## sp_ptbBM76 1.158e+00 1.091e+00 1.061 0.28878
## sp_ptbBM77 8.070e-01 9.914e-01 0.814 0.41564
## sp_ptbBM78 1.320e+00 1.242e+00 1.063 0.28787
## sp_ptbBM79 1.051e+00 9.384e-01 1.120 0.26276
## sp_ptbBM80 7.441e-01 9.252e-01 0.804 0.42126
## sp_ptbBM81 1.185e+00 8.639e-01 1.372 0.17011
## sp_ptbBM82 8.388e-01 8.962e-01 0.936 0.34931
## sp_ptbBM83 2.697e-01 9.115e-01 0.296 0.76734
## sp_ptbBM84 8.179e-01 9.837e-01 0.831 0.40570
## sp_ptbBM85 -1.157e+00 1.061e+00 -1.090 0.27553
## sp_ptbBM86 1.294e+00 9.933e-01 1.302 0.19284
## sp_ptbBM87 8.031e-01 9.865e-01 0.814 0.41560
## sp_ptbBM88 9.591e-01 1.097e+00 0.874 0.38207
## sp_ptbBM89 1.723e-01 1.103e+00 0.156 0.87589
## sp_ptbBM90 7.300e-01 1.181e+00 0.618 0.53656
## sp_ptbBM91 2.328e+00 1.674e+00 1.391 0.16429
## sp_ptbBM92 2.260e+00 1.325e+00 1.706 0.08802 .
## sp_ptbBM93 1.802e+00 1.100e+00 1.638 0.10133
## sp_ptbBM94 1.148e+00 1.129e+00 1.017 0.30930
## sp_ptbBM95 1.472e+00 1.083e+00 1.359 0.17427
## sp_ptbBM96 1.726e+00 9.691e-01 1.781 0.07493 .
## sp_ptbBM97 8.238e-01 1.338e+00 0.616 0.53810
## sp_ptbBM98 7.412e-01 1.183e+00 0.626 0.53113
## sp_ptbBM99 8.849e-01 1.096e+00 0.807 0.41960
## sp_ptbBM100 1.525e+00 1.079e+00 1.413 0.15767
## sp_ptbBM101 1.629e+00 8.201e-01 1.986 0.04705 *
## sp_ptbBM102 1.523e+00 7.921e-01 1.922 0.05459 .
## sp_ptbBM103 7.846e-01 7.591e-01 1.034 0.30134
## sp_ptbBM104 5.740e-01 6.873e-01 0.835 0.40361
## sp_ptbBM105 3.525e-01 8.344e-01 0.422 0.67272
## sp_ptbBM106 -5.470e-01 8.674e-01 -0.631 0.52827
## sp_ptbBM107 -1.138e+00 1.347e+00 -0.844 0.39850
## sp_ptbBM108 1.147e-01 9.416e-01 0.122 0.90308
## sp_ptbBM109 -1.019e+00 1.207e+00 -0.844 0.39842
## sp_ptbBM110 -6.913e-01 1.076e+00 -0.643 0.52039
## sp_ptbBM111 -1.777e+00 1.225e+00 -1.450 0.14708
## sp_ptbBM112 -1.976e+00 1.226e+00 -1.611 0.10711
## sp_ptbBM113 -1.567e+00 1.526e+00 -1.027 0.30443
## sp_ptbBM114 1.017e+00 9.838e-01 1.034 0.30111
## sp_ptbBM115 -9.186e-01 8.940e-01 -1.027 0.30420
## sp_ptbBM116 2.852e+00 1.073e+00 2.658 0.00787 **
## sp_ptbBM117 -7.323e-01 1.037e+00 -0.706 0.48002
## sp_ptbBM118 2.758e+00 9.405e-01 2.933 0.00336 **
## sp_ptbBM119 -6.914e-01 1.156e+00 -0.598 0.54969
## sp_ptbBM120 1.891e+00 1.056e+00 1.791 0.07337 .
## sp_ptbBM121 3.492e-01 8.983e-01 0.389 0.69751
## sp_ptbBM122 1.551e+00 7.666e-01 2.023 0.04308 *
## sp_ptbBM123 1.076e+00 6.994e-01 1.539 0.12391
## sp_ptbBM124 7.855e-01 7.443e-01 1.055 0.29129
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22332.14) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 939.56 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3230.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22332
## Std. Err.: 143777
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2972.426scatter.smooth(predict(m10a, type='response'), rstandard(m10a, type='deviance'), col='gray')
m10a.resid<-residuals(m10a, type="deviance")
m10a.pred<-predict(m10a, type="response")
length(m10a.resid); length(m10a.pred)## [1] 939## [1] 939pacf(m10a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m10a.ac<-update(m10a,.~.+lag(m10a.resid,1)+lag(m10a.resid,2)+lag(m10a.resid,3)+lag(m10a.resid,4)+
lag(m10a.resid,5)+lag(m10a.resid,6)+lag(m10a.resid,7)+lag(m10a.resid,8)+
lag(m10a.resid,9)+lag(m10a.resid,10)+lag(m10a.resid,11)+lag(m10a.resid,12)+
lag(m10a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m10a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb10.aveT + sp_ptbBM + lag(m10a.resid,
## 1) + lag(m10a.resid, 2) + lag(m10a.resid, 3) + lag(m10a.resid,
## 4) + lag(m10a.resid, 5) + lag(m10a.resid, 6) + lag(m10a.resid,
## 7) + lag(m10a.resid, 8) + lag(m10a.resid, 9) + lag(m10a.resid,
## 10) + lag(m10a.resid, 11) + lag(m10a.resid, 12) + lag(m10a.resid,
## 25), data = week, init.theta = 47653.68221, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.56065 -0.65474 -0.03847 0.46928 2.14758
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -25.42500 16.77721 -1.515 0.129659
## cb10.aveTv1.l1 0.33804 0.35343 0.956 0.338851
## cb10.aveTv1.l2 -0.22724 0.25454 -0.893 0.372002
## cb10.aveTv2.l1 2.23750 1.32246 1.692 0.090660 .
## cb10.aveTv2.l2 0.05770 1.01073 0.057 0.954478
## cb10.aveTv3.l1 1.03419 0.53061 1.949 0.051288 .
## cb10.aveTv3.l2 0.22216 0.38835 0.572 0.567284
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -6.00090 43.50337 -0.138 0.890287
## sp_ptbBM9 3.00723 3.90790 0.770 0.441581
## sp_ptbBM10 -2.67421 1.66986 -1.601 0.109277
## sp_ptbBM11 0.77990 1.13070 0.690 0.490353
## sp_ptbBM12 0.26012 1.02913 0.253 0.800455
## sp_ptbBM13 2.21403 0.93164 2.376 0.017478 *
## sp_ptbBM14 -1.13628 1.08758 -1.045 0.296124
## sp_ptbBM15 3.31503 1.07327 3.089 0.002010 **
## sp_ptbBM16 -1.32789 0.88250 -1.505 0.132404
## sp_ptbBM17 2.14451 0.97597 2.197 0.027998 *
## sp_ptbBM18 -1.00584 0.96939 -1.038 0.299457
## sp_ptbBM19 2.92287 0.98457 2.969 0.002991 **
## sp_ptbBM20 -0.82734 1.03738 -0.798 0.425144
## sp_ptbBM21 1.86243 1.20981 1.539 0.123696
## sp_ptbBM22 1.13629 1.04277 1.090 0.275849
## sp_ptbBM23 -0.71595 1.07775 -0.664 0.506500
## sp_ptbBM24 1.34895 1.11147 1.214 0.224875
## sp_ptbBM25 -0.82251 1.27994 -0.643 0.520475
## sp_ptbBM26 1.97810 1.21787 1.624 0.104328
## sp_ptbBM27 -1.87183 1.31521 -1.423 0.154672
## sp_ptbBM28 0.75393 1.41601 0.532 0.594425
## sp_ptbBM29 0.54686 1.15035 0.475 0.634516
## sp_ptbBM30 1.34772 1.07668 1.252 0.210665
## sp_ptbBM31 0.23185 1.06660 0.217 0.827921
## sp_ptbBM32 0.77332 1.06993 0.723 0.469817
## sp_ptbBM33 -1.18524 1.13008 -1.049 0.294263
## sp_ptbBM34 2.81060 1.18649 2.369 0.017844 *
## sp_ptbBM35 0.50565 1.29866 0.389 0.697009
## sp_ptbBM36 2.83389 1.17152 2.419 0.015564 *
## sp_ptbBM37 1.13295 1.38827 0.816 0.414450
## sp_ptbBM38 1.25952 1.18173 1.066 0.286500
## sp_ptbBM39 0.94060 1.26704 0.742 0.457869
## sp_ptbBM40 0.52205 1.34448 0.388 0.697801
## sp_ptbBM41 1.42716 1.16578 1.224 0.220876
## sp_ptbBM42 -1.69231 1.43575 -1.179 0.238518
## sp_ptbBM43 2.34951 1.23323 1.905 0.056758 .
## sp_ptbBM44 -0.87669 0.98998 -0.886 0.375854
## sp_ptbBM45 1.35646 1.06176 1.278 0.201407
## sp_ptbBM46 0.22770 1.12432 0.203 0.839509
## sp_ptbBM47 0.98671 1.13457 0.870 0.384474
## sp_ptbBM48 0.96488 1.26065 0.765 0.444044
## sp_ptbBM49 2.05768 1.31749 1.562 0.118330
## sp_ptbBM50 2.05670 1.53038 1.344 0.178975
## sp_ptbBM51 2.21049 1.33759 1.653 0.098415 .
## sp_ptbBM52 1.49596 1.42436 1.050 0.293596
## sp_ptbBM53 1.48324 1.41855 1.046 0.295742
## sp_ptbBM54 1.70321 1.40799 1.210 0.226404
## sp_ptbBM55 -0.03437 1.41293 -0.024 0.980595
## sp_ptbBM56 2.00108 1.50646 1.328 0.184067
## sp_ptbBM57 1.72696 1.44189 1.198 0.231031
## sp_ptbBM58 0.86263 1.07035 0.806 0.420282
## sp_ptbBM59 -0.63614 1.00348 -0.634 0.526122
## sp_ptbBM60 0.50957 0.89613 0.569 0.569605
## sp_ptbBM61 -1.71675 0.83483 -2.056 0.039742 *
## sp_ptbBM62 1.17538 0.90988 1.292 0.196429
## sp_ptbBM63 -3.26206 0.96678 -3.374 0.000740 ***
## sp_ptbBM64 2.26313 1.12922 2.004 0.045055 *
## sp_ptbBM65 -2.13069 0.79480 -2.681 0.007345 **
## sp_ptbBM66 2.83942 0.81043 3.504 0.000459 ***
## sp_ptbBM67 0.12328 0.73538 0.168 0.866861
## sp_ptbBM68 1.39296 0.76874 1.812 0.069986 .
## sp_ptbBM69 1.39305 0.94834 1.469 0.141852
## sp_ptbBM70 1.72590 1.26091 1.369 0.171072
## sp_ptbBM71 0.20751 1.46856 0.141 0.887632
## sp_ptbBM72 2.18219 1.33968 1.629 0.103337
## sp_ptbBM73 -2.01398 1.21372 -1.659 0.097048 .
## sp_ptbBM74 3.21522 1.12650 2.854 0.004315 **
## sp_ptbBM75 -3.20174 1.13666 -2.817 0.004850 **
## sp_ptbBM76 3.84417 1.19668 3.212 0.001317 **
## sp_ptbBM77 -2.01139 0.99586 -2.020 0.043408 *
## sp_ptbBM78 3.99409 1.29530 3.084 0.002046 **
## sp_ptbBM79 0.92960 0.95091 0.978 0.328276
## sp_ptbBM80 1.89127 0.93730 2.018 0.043613 *
## sp_ptbBM81 -1.19648 0.90813 -1.318 0.187665
## sp_ptbBM82 2.67653 0.92819 2.884 0.003931 **
## sp_ptbBM83 -2.30395 0.91675 -2.513 0.011965 *
## sp_ptbBM84 2.03895 1.05631 1.930 0.053575 .
## sp_ptbBM85 -1.14082 1.07978 -1.057 0.290728
## sp_ptbBM86 1.68704 1.00326 1.682 0.092654 .
## sp_ptbBM87 1.64207 0.99326 1.653 0.098288 .
## sp_ptbBM88 0.61222 1.11479 0.549 0.582884
## sp_ptbBM89 0.45788 1.14054 0.401 0.688080
## sp_ptbBM90 -0.79973 1.20930 -0.661 0.508410
## sp_ptbBM91 3.41857 1.74302 1.961 0.049845 *
## sp_ptbBM92 2.24313 1.34043 1.673 0.094240 .
## sp_ptbBM93 2.23867 1.12940 1.982 0.047459 *
## sp_ptbBM94 0.69875 1.16981 0.597 0.550295
## sp_ptbBM95 1.01857 1.09826 0.927 0.353697
## sp_ptbBM96 1.67799 1.08851 1.542 0.123182
## sp_ptbBM97 1.41040 1.36420 1.034 0.301200
## sp_ptbBM98 -2.37903 1.22003 -1.950 0.051178 .
## sp_ptbBM99 2.15784 1.15923 1.861 0.062682 .
## sp_ptbBM100 1.52170 1.08481 1.403 0.160696
## sp_ptbBM101 1.80721 0.84379 2.142 0.032212 *
## sp_ptbBM102 1.86561 0.77664 2.402 0.016299 *
## sp_ptbBM103 -0.54790 0.76238 -0.719 0.472339
## sp_ptbBM104 1.28537 0.68357 1.880 0.060055 .
## sp_ptbBM105 -0.49173 0.87116 -0.564 0.572450
## sp_ptbBM106 1.04693 0.96853 1.081 0.279720
## sp_ptbBM107 -3.23561 1.36684 -2.367 0.017922 *
## sp_ptbBM108 1.08582 0.97854 1.110 0.267159
## sp_ptbBM109 -0.48331 1.26036 -0.383 0.701373
## sp_ptbBM110 -0.02269 1.09385 -0.021 0.983452
## sp_ptbBM111 -0.31430 1.27988 -0.246 0.806014
## sp_ptbBM112 -1.80756 1.24049 -1.457 0.145080
## sp_ptbBM113 -1.81592 1.56467 -1.161 0.245814
## sp_ptbBM114 0.91969 1.00666 0.914 0.360926
## sp_ptbBM115 -1.02041 0.88957 -1.147 0.251348
## sp_ptbBM116 2.95698 1.10619 2.673 0.007515 **
## sp_ptbBM117 0.31351 1.06902 0.293 0.769313
## sp_ptbBM118 2.44990 0.94636 2.589 0.009632 **
## sp_ptbBM119 0.14088 1.20587 0.117 0.906994
## sp_ptbBM120 1.46995 1.07632 1.366 0.172028
## sp_ptbBM121 -0.48899 0.92219 -0.530 0.595940
## sp_ptbBM122 1.85367 0.76355 2.428 0.015195 *
## sp_ptbBM123 0.11327 0.69830 0.162 0.871148
## sp_ptbBM124 1.65555 0.76368 2.168 0.030169 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m10a.resid, 1) -0.43949 0.03062 -14.351 < 2e-16 ***
## lag(m10a.resid, 2) -0.48659 0.03576 -13.607 < 2e-16 ***
## lag(m10a.resid, 3) -0.56879 0.03760 -15.129 < 2e-16 ***
## lag(m10a.resid, 4) -0.57829 0.04082 -14.168 < 2e-16 ***
## lag(m10a.resid, 5) -0.58595 0.04113 -14.245 < 2e-16 ***
## lag(m10a.resid, 6) -0.54216 0.04173 -12.991 < 2e-16 ***
## lag(m10a.resid, 7) -0.51437 0.03949 -13.024 < 2e-16 ***
## lag(m10a.resid, 8) -0.45874 0.03778 -12.143 < 2e-16 ***
## lag(m10a.resid, 9) -0.36611 0.03535 -10.356 < 2e-16 ***
## lag(m10a.resid, 10) -0.31281 0.03214 -9.734 < 2e-16 ***
## lag(m10a.resid, 11) -0.20733 0.03121 -6.643 3.07e-11 ***
## lag(m10a.resid, 12) -0.16523 0.02901 -5.695 1.23e-08 ***
## lag(m10a.resid, 25) -0.04048 0.02431 -1.665 0.095853 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(47653.68) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 548.13 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2806.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 47654
## Std. Err.: 187938
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2530.054m10a.resid_ac<-residuals(m10a.ac, type="deviance")
m10a.pred_ac<-predict(m10a.ac, type="response")
pacf(m10a.resid_ac,na.action = na.omit) 
length(m10a.pred_ac); length(m10a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m10a.pred,lwd=1, col="blue")
plot(week$time,m10a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m10a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m10a.pred_ac,lwd=1, col="blue")
plot(week$time,m10a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m10a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m10a <- crosspred(cb10.aveT, m10a.ac, cen = 27.6, by=0.1,cumul=TRUE)
##for m11a maxT ######
summary(m11a)##
## Call:
## glm.nb(formula = ptbBM ~ cb11.maxT + sp_ptbBM, data = week, init.theta = 22990.3603,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5724 -0.7694 -0.1141 0.5305 2.6610
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.927e+01 1.661e+01 -2.364 0.01810 *
## cb11.maxTv1.l1 6.780e-01 3.675e-01 1.845 0.06506 .
## cb11.maxTv1.l2 1.270e-01 2.676e-01 0.475 0.63511
## cb11.maxTv2.l1 3.430e+00 1.298e+00 2.643 0.00823 **
## cb11.maxTv2.l2 -2.211e-01 9.834e-01 -0.225 0.82211
## cb11.maxTv3.l1 1.411e+00 5.124e-01 2.753 0.00591 **
## cb11.maxTv3.l2 1.290e-01 3.722e-01 0.347 0.72885
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.141e+06 2.254e+06 -0.506 0.61273
## sp_ptbBM5 1.444e+01 1.195e+01 1.209 0.22684
## sp_ptbBM6 -2.050e+00 2.011e+00 -1.020 0.30782
## sp_ptbBM7 7.078e-01 1.458e+00 0.485 0.62732
## sp_ptbBM8 -1.487e+00 1.165e+00 -1.277 0.20166
## sp_ptbBM9 1.337e+00 9.464e-01 1.412 0.15781
## sp_ptbBM10 -2.386e+00 9.973e-01 -2.393 0.01673 *
## sp_ptbBM11 1.927e+00 8.770e-01 2.197 0.02802 *
## sp_ptbBM12 2.034e-01 7.342e-01 0.277 0.78179
## sp_ptbBM13 1.050e+00 7.158e-01 1.467 0.14235
## sp_ptbBM14 -1.842e+00 1.159e+00 -1.589 0.11199
## sp_ptbBM15 -2.794e-01 1.131e+00 -0.247 0.80486
## sp_ptbBM16 -1.455e+00 1.052e+00 -1.383 0.16675
## sp_ptbBM17 -4.375e-01 9.817e-01 -0.446 0.65584
## sp_ptbBM18 -1.111e-01 9.820e-01 -0.113 0.90990
## sp_ptbBM19 -3.372e-01 9.850e-01 -0.342 0.73212
## sp_ptbBM20 -8.118e-01 1.008e+00 -0.805 0.42071
## sp_ptbBM21 -4.604e-01 1.211e+00 -0.380 0.70390
## sp_ptbBM22 -3.476e-01 1.184e+00 -0.294 0.76902
## sp_ptbBM23 -1.076e+00 1.077e+00 -0.998 0.31817
## sp_ptbBM24 -2.464e-02 9.187e-01 -0.027 0.97860
## sp_ptbBM25 3.023e-01 9.871e-01 0.306 0.75940
## sp_ptbBM26 4.731e-01 9.490e-01 0.499 0.61808
## sp_ptbBM27 -8.863e-01 1.080e+00 -0.821 0.41169
## sp_ptbBM28 -1.327e+00 1.218e+00 -1.089 0.27617
## sp_ptbBM29 2.521e-01 1.007e+00 0.250 0.80239
## sp_ptbBM30 -4.568e-01 8.495e-01 -0.538 0.59077
## sp_ptbBM31 1.325e+00 8.922e-01 1.485 0.13747
## sp_ptbBM32 -3.532e-01 8.605e-01 -0.411 0.68144
## sp_ptbBM33 -2.518e-01 9.112e-01 -0.276 0.78225
## sp_ptbBM34 1.250e-02 8.510e-01 0.015 0.98828
## sp_ptbBM35 -1.632e+00 1.273e+00 -1.282 0.19990
## sp_ptbBM36 -3.990e-02 1.010e+00 -0.040 0.96848
## sp_ptbBM37 -8.901e-01 1.078e+00 -0.826 0.40884
## sp_ptbBM38 7.230e-01 8.157e-01 0.886 0.37548
## sp_ptbBM39 -2.468e-01 9.104e-01 -0.271 0.78632
## sp_ptbBM40 7.597e-01 8.899e-01 0.854 0.39327
## sp_ptbBM41 -1.197e-01 9.101e-01 -0.131 0.89538
## sp_ptbBM42 -1.091e+00 1.261e+00 -0.865 0.38683
## sp_ptbBM43 -9.571e-02 1.022e+00 -0.094 0.92537
## sp_ptbBM44 1.462e-01 9.080e-01 0.161 0.87205
## sp_ptbBM45 7.944e-01 8.657e-01 0.918 0.35881
## sp_ptbBM46 -8.224e-02 8.277e-01 -0.099 0.92086
## sp_ptbBM47 5.978e-02 9.273e-01 0.064 0.94859
## sp_ptbBM48 -1.873e-02 8.600e-01 -0.022 0.98263
## sp_ptbBM49 1.351e-01 1.170e+00 0.115 0.90811
## sp_ptbBM50 -6.852e-01 1.167e+00 -0.587 0.55694
## sp_ptbBM51 1.453e+00 8.693e-01 1.671 0.09463 .
## sp_ptbBM52 1.239e-01 9.056e-01 0.137 0.89119
## sp_ptbBM53 1.695e+00 9.268e-01 1.829 0.06747 .
## sp_ptbBM54 8.481e-01 9.193e-01 0.923 0.35622
## sp_ptbBM55 2.127e-01 1.045e+00 0.204 0.83865
## sp_ptbBM56 3.324e-01 1.178e+00 0.282 0.77780
## sp_ptbBM57 -2.307e-01 1.211e+00 -0.191 0.84885
## sp_ptbBM58 3.165e-02 8.707e-01 0.036 0.97101
## sp_ptbBM59 -1.638e+00 1.096e+00 -1.494 0.13505
## sp_ptbBM60 -1.278e+00 1.198e+00 -1.067 0.28608
## sp_ptbBM61 -1.002e+00 1.065e+00 -0.941 0.34695
## sp_ptbBM62 -1.873e+00 1.101e+00 -1.701 0.08889 .
## sp_ptbBM63 -3.045e+00 1.324e+00 -2.299 0.02151 *
## sp_ptbBM64 -2.307e+00 1.489e+00 -1.549 0.12129
## sp_ptbBM65 -1.394e+00 1.114e+00 -1.251 0.21076
## sp_ptbBM66 -9.563e-01 9.182e-01 -1.042 0.29764
## sp_ptbBM67 6.262e-01 8.422e-01 0.744 0.45715
## sp_ptbBM68 7.719e-01 8.538e-01 0.904 0.36596
## sp_ptbBM69 2.421e-01 9.843e-01 0.246 0.80573
## sp_ptbBM70 -7.056e-01 1.361e+00 -0.518 0.60428
## sp_ptbBM71 -8.851e-01 1.296e+00 -0.683 0.49476
## sp_ptbBM72 2.684e-01 1.186e+00 0.226 0.82092
## sp_ptbBM73 -7.091e-02 9.866e-01 -0.072 0.94270
## sp_ptbBM74 1.759e+00 8.527e-01 2.062 0.03916 *
## sp_ptbBM75 9.081e-01 8.504e-01 1.068 0.28555
## sp_ptbBM76 1.353e+00 8.726e-01 1.551 0.12099
## sp_ptbBM77 -2.778e-01 9.236e-01 -0.301 0.76358
## sp_ptbBM78 9.471e-04 1.187e+00 0.001 0.99936
## sp_ptbBM79 4.206e-02 9.729e-01 0.043 0.96552
## sp_ptbBM80 -7.511e-01 9.118e-01 -0.824 0.41010
## sp_ptbBM81 2.261e-01 8.441e-01 0.268 0.78882
## sp_ptbBM82 -1.292e-01 8.069e-01 -0.160 0.87282
## sp_ptbBM83 -6.957e-01 8.615e-01 -0.807 0.41941
## sp_ptbBM84 -5.812e-01 1.107e+00 -0.525 0.59955
## sp_ptbBM85 -2.320e+00 1.170e+00 -1.982 0.04742 *
## sp_ptbBM86 4.992e-01 8.925e-01 0.559 0.57598
## sp_ptbBM87 1.305e-02 8.642e-01 0.015 0.98795
## sp_ptbBM88 4.482e-01 1.008e+00 0.445 0.65655
## sp_ptbBM89 -1.461e-01 9.804e-01 -0.149 0.88150
## sp_ptbBM90 -1.434e-01 1.078e+00 -0.133 0.89413
## sp_ptbBM91 2.105e+00 2.001e+00 1.052 0.29294
## sp_ptbBM92 1.730e+00 1.542e+00 1.122 0.26181
## sp_ptbBM93 2.005e+00 1.408e+00 1.424 0.15436
## sp_ptbBM94 1.641e+00 1.249e+00 1.313 0.18904
## sp_ptbBM95 2.674e+00 1.313e+00 2.036 0.04177 *
## sp_ptbBM96 2.673e+00 1.206e+00 2.216 0.02670 *
## sp_ptbBM97 2.817e+00 1.726e+00 1.632 0.10262
## sp_ptbBM98 5.097e-01 1.507e+00 0.338 0.73519
## sp_ptbBM99 1.199e+00 1.340e+00 0.894 0.37116
## sp_ptbBM100 1.588e+00 1.094e+00 1.452 0.14640
## sp_ptbBM101 2.171e+00 1.003e+00 2.166 0.03032 *
## sp_ptbBM102 2.457e+00 9.854e-01 2.493 0.01265 *
## sp_ptbBM103 1.804e+00 8.997e-01 2.005 0.04494 *
## sp_ptbBM104 1.930e+00 9.840e-01 1.961 0.04986 *
## sp_ptbBM105 2.359e-01 9.815e-01 0.240 0.81008
## sp_ptbBM106 -5.736e-01 1.164e+00 -0.493 0.62221
## sp_ptbBM107 -4.748e-01 8.228e-01 -0.577 0.56392
## sp_ptbBM108 -2.046e-03 9.031e-01 -0.002 0.99819
## sp_ptbBM109 -9.262e-01 8.734e-01 -1.060 0.28894
## sp_ptbBM110 1.591e-01 7.184e-01 0.221 0.82477
## sp_ptbBM111 -9.470e-01 8.020e-01 -1.181 0.23765
## sp_ptbBM112 -1.599e+00 9.273e-01 -1.724 0.08469 .
## sp_ptbBM113 -1.345e+00 1.208e+00 -1.113 0.26569
## sp_ptbBM114 1.504e-01 9.088e-01 0.166 0.86851
## sp_ptbBM115 -1.682e+00 9.551e-01 -1.761 0.07831 .
## sp_ptbBM116 2.015e+00 9.586e-01 2.102 0.03556 *
## sp_ptbBM117 -1.401e+00 1.074e+00 -1.305 0.19202
## sp_ptbBM118 2.130e+00 9.095e-01 2.342 0.01918 *
## sp_ptbBM119 -1.381e+00 1.189e+00 -1.162 0.24527
## sp_ptbBM120 9.602e-01 1.184e+00 0.811 0.41749
## sp_ptbBM121 -1.235e-01 9.603e-01 -0.129 0.89769
## sp_ptbBM122 9.804e-01 8.353e-01 1.174 0.24056
## sp_ptbBM123 4.186e-01 7.143e-01 0.586 0.55782
## sp_ptbBM124 6.543e-01 7.456e-01 0.878 0.38015
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(22990.36) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 935.29 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3226.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 22990
## Std. Err.: 147953
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2968.147scatter.smooth(predict(m11a, type='response'), rstandard(m11a, type='deviance'), col='gray')
m11a.resid<-residuals(m11a, type="deviance")
m11a.pred<-predict(m11a, type="response")
length(m11a.resid); length(m11a.pred)## [1] 939## [1] 939pacf(m11a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-12 & 25
m11a.ac<-update(m11a,.~.+lag(m11a.resid,1)+lag(m11a.resid,2)+lag(m11a.resid,3)+lag(m11a.resid,4)+
lag(m11a.resid,5)+lag(m11a.resid,6)+lag(m11a.resid,7)+lag(m11a.resid,8)+
lag(m11a.resid,9)+lag(m11a.resid,10)+lag(m11a.resid,11)+lag(m11a.resid,12)+
lag(m11a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(m11a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb11.maxT + sp_ptbBM + lag(m11a.resid,
## 1) + lag(m11a.resid, 2) + lag(m11a.resid, 3) + lag(m11a.resid,
## 4) + lag(m11a.resid, 5) + lag(m11a.resid, 6) + lag(m11a.resid,
## 7) + lag(m11a.resid, 8) + lag(m11a.resid, 9) + lag(m11a.resid,
## 10) + lag(m11a.resid, 11) + lag(m11a.resid, 12) + lag(m11a.resid,
## 25), data = week, init.theta = 47615.60569, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.56056 -0.63618 -0.04906 0.45134 2.24414
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -36.64490 18.68097 -1.962 0.049807 *
## cb11.maxTv1.l1 0.60347 0.40047 1.507 0.131835
## cb11.maxTv1.l2 0.01914 0.28112 0.068 0.945720
## cb11.maxTv2.l1 3.02473 1.45546 2.078 0.037691 *
## cb11.maxTv2.l2 0.18715 1.06110 0.176 0.860003
## cb11.maxTv3.l1 1.06078 0.55160 1.923 0.054469 .
## cb11.maxTv3.l2 0.24479 0.39210 0.624 0.532422
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -10.01237 43.48939 -0.230 0.817916
## sp_ptbBM9 2.98475 3.95200 0.755 0.450100
## sp_ptbBM10 -2.90626 1.63400 -1.779 0.075303 .
## sp_ptbBM11 0.78985 0.97654 0.809 0.418615
## sp_ptbBM12 -0.06279 0.81131 -0.077 0.938309
## sp_ptbBM13 1.67028 0.73281 2.279 0.022650 *
## sp_ptbBM14 -2.65239 1.19998 -2.210 0.027080 *
## sp_ptbBM15 2.21791 1.17712 1.884 0.059540 .
## sp_ptbBM16 -2.29036 1.06207 -2.157 0.031044 *
## sp_ptbBM17 1.12468 1.00999 1.114 0.265467
## sp_ptbBM18 -1.39492 1.02550 -1.360 0.173754
## sp_ptbBM19 1.64652 1.00417 1.640 0.101073
## sp_ptbBM20 -2.24215 1.04556 -2.144 0.031997 *
## sp_ptbBM21 0.80032 1.27371 0.628 0.529785
## sp_ptbBM22 0.19122 1.20972 0.158 0.874405
## sp_ptbBM23 -1.38763 1.08180 -1.283 0.199597
## sp_ptbBM24 0.27268 0.93448 0.292 0.770439
## sp_ptbBM25 -1.25929 1.02905 -1.224 0.221048
## sp_ptbBM26 0.82229 0.96106 0.856 0.392212
## sp_ptbBM27 -2.37536 1.04890 -2.265 0.023536 *
## sp_ptbBM28 -1.35370 1.25249 -1.081 0.279781
## sp_ptbBM29 -0.41417 1.03881 -0.399 0.690114
## sp_ptbBM30 0.40539 0.88336 0.459 0.646290
## sp_ptbBM31 0.11287 0.94331 0.120 0.904756
## sp_ptbBM32 0.27265 0.90733 0.300 0.763797
## sp_ptbBM33 -1.59162 0.92982 -1.712 0.086943 .
## sp_ptbBM34 1.27812 0.88038 1.452 0.146560
## sp_ptbBM35 -1.59148 1.30388 -1.221 0.222248
## sp_ptbBM36 1.14254 1.02920 1.110 0.266947
## sp_ptbBM37 -0.32845 1.11878 -0.294 0.769078
## sp_ptbBM38 -0.15272 0.87041 -0.175 0.860718
## sp_ptbBM39 0.57894 0.92043 0.629 0.529357
## sp_ptbBM40 -0.80482 0.92849 -0.867 0.386053
## sp_ptbBM41 0.80487 0.91061 0.884 0.376762
## sp_ptbBM42 -3.24633 1.32546 -2.449 0.014317 *
## sp_ptbBM43 1.76444 1.10648 1.595 0.110791
## sp_ptbBM44 -1.98889 0.91039 -2.185 0.028914 *
## sp_ptbBM45 1.19478 0.88640 1.348 0.177693
## sp_ptbBM46 -0.99395 0.83102 -1.196 0.231672
## sp_ptbBM47 0.35126 0.93461 0.376 0.707037
## sp_ptbBM48 -0.86743 0.87966 -0.986 0.324081
## sp_ptbBM49 0.73184 1.19805 0.611 0.541295
## sp_ptbBM50 -0.35574 1.19467 -0.298 0.765879
## sp_ptbBM51 1.16538 0.88911 1.311 0.189947
## sp_ptbBM52 0.43822 0.91646 0.478 0.632536
## sp_ptbBM53 1.01849 0.94561 1.077 0.281449
## sp_ptbBM54 1.39511 0.93066 1.499 0.133860
## sp_ptbBM55 -1.12655 1.11180 -1.013 0.310933
## sp_ptbBM56 1.34596 1.20855 1.114 0.265410
## sp_ptbBM57 0.14941 1.24947 0.120 0.904818
## sp_ptbBM58 0.51579 0.88119 0.585 0.558325
## sp_ptbBM59 -1.24162 1.11139 -1.117 0.263921
## sp_ptbBM60 -0.08130 1.21353 -0.067 0.946585
## sp_ptbBM61 -2.01499 1.12239 -1.795 0.072611 .
## sp_ptbBM62 0.25302 1.13324 0.223 0.823322
## sp_ptbBM63 -4.48523 1.38836 -3.231 0.001235 **
## sp_ptbBM64 0.98660 1.57261 0.627 0.530421
## sp_ptbBM65 -3.08541 1.12446 -2.744 0.006071 **
## sp_ptbBM66 1.64599 0.97499 1.688 0.091370 .
## sp_ptbBM67 -0.36177 0.84913 -0.426 0.670078
## sp_ptbBM68 0.77186 0.87714 0.880 0.378875
## sp_ptbBM69 0.47271 0.99654 0.474 0.635247
## sp_ptbBM70 0.45224 1.38653 0.326 0.744298
## sp_ptbBM71 -0.80070 1.37305 -0.583 0.559790
## sp_ptbBM72 1.05688 1.20574 0.877 0.380734
## sp_ptbBM73 -2.31359 1.06818 -2.166 0.030317 *
## sp_ptbBM74 2.79471 0.90357 3.093 0.001982 **
## sp_ptbBM75 -3.10240 0.87673 -3.539 0.000402 ***
## sp_ptbBM76 3.51416 0.96958 3.624 0.000290 ***
## sp_ptbBM77 -2.86729 0.95356 -3.007 0.002639 **
## sp_ptbBM78 2.28556 1.24941 1.829 0.067354 .
## sp_ptbBM79 0.04463 1.01262 0.044 0.964845
## sp_ptbBM80 0.84340 0.93017 0.907 0.364558
## sp_ptbBM81 -1.77799 0.89765 -1.981 0.047622 *
## sp_ptbBM82 1.94494 0.85057 2.287 0.022218 *
## sp_ptbBM83 -3.22692 0.90003 -3.585 0.000337 ***
## sp_ptbBM84 1.18001 1.17241 1.006 0.314187
## sp_ptbBM85 -2.42488 1.21037 -2.003 0.045132 *
## sp_ptbBM86 0.70670 0.90850 0.778 0.436637
## sp_ptbBM87 0.58113 0.87304 0.666 0.505638
## sp_ptbBM88 -0.34141 1.02373 -0.333 0.738762
## sp_ptbBM89 -0.55571 1.01715 -0.546 0.584832
## sp_ptbBM90 -2.19391 1.11649 -1.965 0.049414 *
## sp_ptbBM91 2.80336 2.08783 1.343 0.179365
## sp_ptbBM92 1.76337 1.58654 1.111 0.266372
## sp_ptbBM93 2.54365 1.46180 1.740 0.081844 .
## sp_ptbBM94 1.40345 1.31193 1.070 0.284729
## sp_ptbBM95 1.31548 1.36937 0.961 0.336727
## sp_ptbBM96 2.52985 1.35879 1.862 0.062627 .
## sp_ptbBM97 2.14737 1.78708 1.202 0.229515
## sp_ptbBM98 -2.69441 1.56293 -1.724 0.084718 .
## sp_ptbBM99 1.80287 1.40688 1.281 0.200030
## sp_ptbBM100 1.20827 1.12825 1.071 0.284203
## sp_ptbBM101 2.32910 1.03901 2.242 0.024984 *
## sp_ptbBM102 2.19132 0.99918 2.193 0.028299 *
## sp_ptbBM103 0.36868 0.93673 0.394 0.693891
## sp_ptbBM104 2.00894 1.04092 1.930 0.053610 .
## sp_ptbBM105 -0.52288 1.02570 -0.510 0.610205
## sp_ptbBM106 0.94505 1.29570 0.729 0.465771
## sp_ptbBM107 -2.29271 0.83679 -2.740 0.006146 **
## sp_ptbBM108 1.04051 0.97213 1.070 0.284467
## sp_ptbBM109 -0.05316 0.90364 -0.059 0.953093
## sp_ptbBM110 0.69175 0.74014 0.935 0.349981
## sp_ptbBM111 0.23033 0.83124 0.277 0.781712
## sp_ptbBM112 -1.65137 0.93701 -1.762 0.078003 .
## sp_ptbBM113 -1.11980 1.22600 -0.913 0.361044
## sp_ptbBM114 -0.44777 0.93604 -0.478 0.632393
## sp_ptbBM115 -1.73166 0.97052 -1.784 0.074381 .
## sp_ptbBM116 1.45048 0.99388 1.459 0.144453
## sp_ptbBM117 -0.72861 1.13163 -0.644 0.519668
## sp_ptbBM118 1.19723 0.92765 1.291 0.196842
## sp_ptbBM119 -0.65676 1.24795 -0.526 0.598699
## sp_ptbBM120 0.16055 1.22291 0.131 0.895550
## sp_ptbBM121 -1.08101 1.00068 -1.080 0.280018
## sp_ptbBM122 1.16090 0.84481 1.374 0.169392
## sp_ptbBM123 -0.23810 0.70580 -0.337 0.735853
## sp_ptbBM124 1.68549 0.78262 2.154 0.031267 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(m11a.resid, 1) -0.44287 0.03073 -14.411 < 2e-16 ***
## lag(m11a.resid, 2) -0.49062 0.03587 -13.677 < 2e-16 ***
## lag(m11a.resid, 3) -0.57803 0.03799 -15.215 < 2e-16 ***
## lag(m11a.resid, 4) -0.58969 0.04149 -14.213 < 2e-16 ***
## lag(m11a.resid, 5) -0.59583 0.04179 -14.256 < 2e-16 ***
## lag(m11a.resid, 6) -0.55176 0.04274 -12.909 < 2e-16 ***
## lag(m11a.resid, 7) -0.52488 0.04063 -12.917 < 2e-16 ***
## lag(m11a.resid, 8) -0.46847 0.03892 -12.036 < 2e-16 ***
## lag(m11a.resid, 9) -0.37479 0.03640 -10.297 < 2e-16 ***
## lag(m11a.resid, 10) -0.31837 0.03285 -9.692 < 2e-16 ***
## lag(m11a.resid, 11) -0.20941 0.03174 -6.597 4.20e-11 ***
## lag(m11a.resid, 12) -0.16756 0.02911 -5.755 8.64e-09 ***
## lag(m11a.resid, 25) -0.04451 0.02433 -1.829 0.067376 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(47615.61) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 544.86 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2802.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 47616
## Std. Err.: 187023
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2526.783m11a.resid_ac<-residuals(m11a.ac, type="deviance")
m11a.pred_ac<-predict(m11a.ac, type="response")
pacf(m11a.resid_ac,na.action = na.omit) 
length(m11a.pred_ac); length(m11a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m11a.pred,lwd=1, col="blue")
plot(week$time,m11a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m11a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,m11a.pred_ac,lwd=1, col="blue")
plot(week$time,m11a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(m11a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.m11a <- crosspred(cb11.maxT, m11a.ac, cen = 32.1, by=0.1,cumul=TRUE)
###final model #####
mod_fullbm<-glm.nb(ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun + cb1.avgWindSp + sp_ptbBM, data = week)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullbm)##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBM, data = week, init.theta = 26334.1553,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4316 -0.7824 -0.1085 0.5665 2.4215
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.280e+01 2.081e+01 -0.615 0.53842
## cb3.RFv1.l1 -1.734e-01 2.912e-01 -0.595 0.55153
## cb3.RFv1.l2 2.015e-01 2.136e-01 0.943 0.34566
## cb3.RFv2.l1 5.018e-02 4.746e-01 0.106 0.91579
## cb3.RFv2.l2 3.610e-01 3.279e-01 1.101 0.27089
## cb3.RFv3.l1 3.205e-01 7.315e-01 0.438 0.66129
## cb3.RFv3.l2 1.446e-01 5.228e-01 0.277 0.78210
## cb9.minTv1.l1 -3.153e-02 2.898e-01 -0.109 0.91337
## cb9.minTv1.l2 -8.557e-02 2.125e-01 -0.403 0.68725
## cb9.minTv2.l1 3.864e-01 9.589e-01 0.403 0.68697
## cb9.minTv2.l2 -7.364e-01 7.432e-01 -0.991 0.32176
## cb9.minTv3.l1 8.261e-01 5.447e-01 1.517 0.12936
## cb9.minTv3.l2 5.500e-01 3.993e-01 1.378 0.16834
## cb5.minRHv1.l1 2.540e-01 2.847e-01 0.892 0.37225
## cb5.minRHv1.l2 1.848e-01 1.923e-01 0.961 0.33663
## cb5.minRHv2.l1 -1.062e+00 1.022e+00 -1.038 0.29905
## cb5.minRHv2.l2 -4.335e-01 7.436e-01 -0.583 0.55989
## cb5.minRHv3.l1 -9.220e-01 7.099e-01 -1.299 0.19401
## cb5.minRHv3.l2 -8.996e-01 5.322e-01 -1.690 0.09095 .
## cb2.sunv1.l1 1.664e-01 2.299e-01 0.724 0.46927
## cb2.sunv1.l2 -1.570e-01 1.618e-01 -0.971 0.33164
## cb2.sunv2.l1 7.668e-01 9.257e-01 0.828 0.40749
## cb2.sunv2.l2 3.225e-01 6.306e-01 0.511 0.60908
## cb2.sunv3.l1 5.157e-01 3.563e-01 1.447 0.14777
## cb2.sunv3.l2 4.750e-01 2.312e-01 2.055 0.03991 *
## cb1.avgWindSpv1.l1 3.350e-01 3.264e-01 1.026 0.30476
## cb1.avgWindSpv1.l2 3.870e-01 2.293e-01 1.688 0.09150 .
## cb1.avgWindSpv2.l1 1.589e+00 7.294e-01 2.179 0.02936 *
## cb1.avgWindSpv2.l2 1.729e-01 5.060e-01 0.342 0.73259
## cb1.avgWindSpv3.l1 9.618e-01 8.213e-01 1.171 0.24161
## cb1.avgWindSpv3.l2 -3.886e-01 5.065e-01 -0.767 0.44288
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -1.823e+06 2.230e+06 -0.818 0.41362
## sp_ptbBM5 2.008e+01 1.263e+01 1.589 0.11197
## sp_ptbBM6 -6.066e+00 2.510e+00 -2.417 0.01566 *
## sp_ptbBM7 -3.021e+00 2.383e+00 -1.268 0.20492
## sp_ptbBM8 -5.988e+00 2.468e+00 -2.427 0.01524 *
## sp_ptbBM9 -2.039e+00 2.205e+00 -0.925 0.35506
## sp_ptbBM10 -6.864e+00 2.128e+00 -3.226 0.00126 **
## sp_ptbBM11 -2.288e+00 1.882e+00 -1.216 0.22408
## sp_ptbBM12 -4.147e+00 1.832e+00 -2.263 0.02361 *
## sp_ptbBM13 -2.090e+00 2.063e+00 -1.013 0.31087
## sp_ptbBM14 -5.242e+00 1.962e+00 -2.672 0.00754 **
## sp_ptbBM15 -1.587e+00 1.646e+00 -0.964 0.33520
## sp_ptbBM16 -1.213e+00 1.864e+00 -0.651 0.51525
## sp_ptbBM17 -1.825e+00 1.634e+00 -1.117 0.26401
## sp_ptbBM18 -1.730e+00 1.643e+00 -1.053 0.29234
## sp_ptbBM19 -3.094e+00 1.616e+00 -1.915 0.05552 .
## sp_ptbBM20 -2.882e+00 1.733e+00 -1.663 0.09633 .
## sp_ptbBM21 -2.271e+00 1.936e+00 -1.173 0.24060
## sp_ptbBM22 -3.110e+00 1.659e+00 -1.875 0.06084 .
## sp_ptbBM23 -2.060e+00 1.646e+00 -1.252 0.21067
## sp_ptbBM24 -2.621e+00 1.547e+00 -1.694 0.09019 .
## sp_ptbBM25 -1.993e+00 1.305e+00 -1.527 0.12671
## sp_ptbBM26 -2.037e+00 1.471e+00 -1.385 0.16606
## sp_ptbBM27 -3.990e+00 1.667e+00 -2.394 0.01669 *
## sp_ptbBM28 -2.076e+00 1.728e+00 -1.201 0.22963
## sp_ptbBM29 -2.643e+00 1.814e+00 -1.457 0.14512
## sp_ptbBM30 -1.698e+00 1.712e+00 -0.992 0.32132
## sp_ptbBM31 -1.001e+00 1.695e+00 -0.591 0.55480
## sp_ptbBM32 -2.650e+00 1.859e+00 -1.425 0.15416
## sp_ptbBM33 -1.029e+00 1.842e+00 -0.559 0.57633
## sp_ptbBM34 3.486e-01 2.212e+00 0.158 0.87479
## sp_ptbBM35 1.088e+00 2.348e+00 0.464 0.64296
## sp_ptbBM36 3.622e+00 2.653e+00 1.365 0.17227
## sp_ptbBM37 -1.308e-01 2.790e+00 -0.047 0.96261
## sp_ptbBM38 6.059e-02 2.643e+00 0.023 0.98171
## sp_ptbBM39 -1.944e+00 2.616e+00 -0.743 0.45745
## sp_ptbBM40 -1.113e+00 2.547e+00 -0.437 0.66219
## sp_ptbBM41 -2.811e+00 2.335e+00 -1.204 0.22864
## sp_ptbBM42 -2.355e+00 2.186e+00 -1.077 0.28134
## sp_ptbBM43 -3.292e+00 2.168e+00 -1.518 0.12895
## sp_ptbBM44 -2.451e+00 2.013e+00 -1.218 0.22325
## sp_ptbBM45 -2.496e+00 1.960e+00 -1.273 0.20293
## sp_ptbBM46 -3.849e+00 2.019e+00 -1.907 0.05656 .
## sp_ptbBM47 -3.584e+00 2.152e+00 -1.665 0.09584 .
## sp_ptbBM48 -3.413e+00 2.233e+00 -1.528 0.12644
## sp_ptbBM49 -2.569e+00 2.438e+00 -1.054 0.29198
## sp_ptbBM50 -3.533e+00 2.138e+00 -1.653 0.09835 .
## sp_ptbBM51 -1.426e+00 2.140e+00 -0.666 0.50532
## sp_ptbBM52 -2.810e+00 2.044e+00 -1.375 0.16913
## sp_ptbBM53 -1.171e+00 1.945e+00 -0.602 0.54705
## sp_ptbBM54 -2.126e+00 1.917e+00 -1.109 0.26732
## sp_ptbBM55 -1.303e+00 1.751e+00 -0.744 0.45697
## sp_ptbBM56 -1.435e+00 2.168e+00 -0.662 0.50805
## sp_ptbBM57 -9.701e-01 1.828e+00 -0.531 0.59553
## sp_ptbBM58 -1.357e+00 1.740e+00 -0.780 0.43540
## sp_ptbBM59 -3.033e+00 1.629e+00 -1.861 0.06272 .
## sp_ptbBM60 -3.101e+00 1.705e+00 -1.819 0.06890 .
## sp_ptbBM61 -1.962e+00 1.585e+00 -1.237 0.21597
## sp_ptbBM62 -1.615e+00 1.869e+00 -0.864 0.38762
## sp_ptbBM63 -3.444e+00 2.244e+00 -1.535 0.12484
## sp_ptbBM64 -3.180e+00 2.560e+00 -1.242 0.21410
## sp_ptbBM65 -1.837e+00 2.619e+00 -0.701 0.48299
## sp_ptbBM66 -4.672e+00 2.654e+00 -1.760 0.07833 .
## sp_ptbBM67 -4.283e+00 2.572e+00 -1.665 0.09587 .
## sp_ptbBM68 -6.199e+00 2.691e+00 -2.304 0.02124 *
## sp_ptbBM69 -6.243e+00 2.733e+00 -2.284 0.02235 *
## sp_ptbBM70 -7.909e+00 2.566e+00 -3.082 0.00205 **
## sp_ptbBM71 -5.079e+00 2.440e+00 -2.082 0.03738 *
## sp_ptbBM72 -3.418e+00 2.224e+00 -1.537 0.12437
## sp_ptbBM73 -3.557e+00 2.183e+00 -1.629 0.10322
## sp_ptbBM74 -2.047e+00 2.060e+00 -0.994 0.32034
## sp_ptbBM75 -2.293e+00 2.091e+00 -1.097 0.27276
## sp_ptbBM76 -1.334e+00 2.048e+00 -0.651 0.51491
## sp_ptbBM77 -2.865e+00 2.271e+00 -1.262 0.20711
## sp_ptbBM78 -1.920e+00 2.331e+00 -0.824 0.41015
## sp_ptbBM79 -2.221e+00 2.251e+00 -0.987 0.32372
## sp_ptbBM80 -3.184e+00 2.258e+00 -1.410 0.15849
## sp_ptbBM81 -4.274e+00 2.291e+00 -1.866 0.06206 .
## sp_ptbBM82 -4.425e+00 2.169e+00 -2.040 0.04135 *
## sp_ptbBM83 -4.138e+00 2.167e+00 -1.909 0.05623 .
## sp_ptbBM84 -4.253e+00 2.511e+00 -1.693 0.09040 .
## sp_ptbBM85 -6.855e+00 2.721e+00 -2.519 0.01176 *
## sp_ptbBM86 -3.952e+00 2.711e+00 -1.458 0.14488
## sp_ptbBM87 -4.588e+00 2.537e+00 -1.808 0.07056 .
## sp_ptbBM88 -5.469e+00 2.484e+00 -2.202 0.02768 *
## sp_ptbBM89 -7.187e+00 2.569e+00 -2.798 0.00514 **
## sp_ptbBM90 -5.837e+00 2.648e+00 -2.205 0.02747 *
## sp_ptbBM91 -5.802e+00 2.674e+00 -2.170 0.03002 *
## sp_ptbBM92 -3.888e+00 2.612e+00 -1.489 0.13661
## sp_ptbBM93 -2.264e+00 2.605e+00 -0.869 0.38462
## sp_ptbBM94 -2.784e+00 2.509e+00 -1.110 0.26709
## sp_ptbBM95 -2.629e+00 2.546e+00 -1.033 0.30177
## sp_ptbBM96 -2.402e+00 2.520e+00 -0.953 0.34043
## sp_ptbBM97 -2.312e+00 2.796e+00 -0.827 0.40843
## sp_ptbBM98 -6.646e+00 2.954e+00 -2.250 0.02445 *
## sp_ptbBM99 -5.743e+00 2.895e+00 -1.984 0.04727 *
## sp_ptbBM100 -3.852e+00 2.925e+00 -1.317 0.18785
## sp_ptbBM101 -3.239e+00 2.732e+00 -1.186 0.23578
## sp_ptbBM102 -3.283e+00 2.466e+00 -1.332 0.18302
## sp_ptbBM103 -4.162e+00 2.468e+00 -1.686 0.09172 .
## sp_ptbBM104 -2.644e+00 2.292e+00 -1.154 0.24870
## sp_ptbBM105 -7.395e+00 3.014e+00 -2.453 0.01416 *
## sp_ptbBM106 -9.139e+00 3.809e+00 -2.399 0.01643 *
## sp_ptbBM107 -7.515e+00 4.408e+00 -1.705 0.08819 .
## sp_ptbBM108 -7.150e+00 3.889e+00 -1.839 0.06596 .
## sp_ptbBM109 -1.050e+01 4.136e+00 -2.540 0.01110 *
## sp_ptbBM110 -1.116e+01 4.055e+00 -2.752 0.00592 **
## sp_ptbBM111 -1.314e+01 4.482e+00 -2.931 0.00337 **
## sp_ptbBM112 -1.013e+01 3.768e+00 -2.688 0.00719 **
## sp_ptbBM113 -6.880e+00 2.835e+00 -2.427 0.01521 *
## sp_ptbBM114 -9.915e-01 1.950e+00 -0.508 0.61122
## sp_ptbBM115 -3.397e+00 1.853e+00 -1.833 0.06680 .
## sp_ptbBM116 8.256e-01 1.711e+00 0.483 0.62945
## sp_ptbBM117 -3.350e+00 1.559e+00 -2.149 0.03162 *
## sp_ptbBM118 1.249e+00 1.333e+00 0.937 0.34888
## sp_ptbBM119 -2.342e+00 1.440e+00 -1.626 0.10389
## sp_ptbBM120 -1.156e+00 1.402e+00 -0.824 0.40983
## sp_ptbBM121 -6.117e-01 1.187e+00 -0.515 0.60647
## sp_ptbBM122 2.620e-01 9.913e-01 0.264 0.79156
## sp_ptbBM123 1.663e-01 9.701e-01 0.171 0.86385
## sp_ptbBM124 1.323e+00 8.961e-01 1.477 0.13973
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(26334.16) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 908.66 on 735 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3247.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 26334
## Std. Err.: 151768
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2941.513pred.fullbm<-predict(mod_fullbm, type="response") #fitted
resid.fullbm<-residuals(mod_fullbm, type="deviance") #residuals deviance
length(pred.fullbm)## [1] 939length(week$ptbBM)## [1] 939length(resid.fullbm)## [1] 939pacf(resid.fullbm,na.action=na.omit) #PACF for residuals, sig lags from 1-10, 12,17 & 25
#ensure that the lags are dplyr lags
mod_fullbm.ac<-update(mod_fullbm,.~.+lag(resid.fullbm,1)+lag(resid.fullbm,2)+lag(resid.fullbm,3)+lag(resid.fullbm,4)+
lag(resid.fullbm,5)+lag(resid.fullbm,6)+lag(resid.fullbm,7)+lag(resid.fullbm,8)+
lag(resid.fullbm,9)+lag(resid.fullbm,10)+lag(resid.fullbm,12)+lag(resid.fullbm,17)+lag(resid.fullbm,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullbm.ac)##aic=2853.4##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBM + lag(resid.fullbm, 1) + lag(resid.fullbm,
## 2) + lag(resid.fullbm, 3) + lag(resid.fullbm, 4) + lag(resid.fullbm,
## 5) + lag(resid.fullbm, 6) + lag(resid.fullbm, 7) + lag(resid.fullbm,
## 8) + lag(resid.fullbm, 9) + lag(resid.fullbm, 10) + lag(resid.fullbm,
## 12) + lag(resid.fullbm, 17) + lag(resid.fullbm, 25), data = week,
## init.theta = 48297.52007, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.43365 -0.69942 -0.07149 0.49249 2.06348
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -28.562548 22.231312 -1.285 0.198866
## cb3.RFv1.l1 0.114837 0.308769 0.372 0.709954
## cb3.RFv1.l2 0.031240 0.225886 0.138 0.890003
## cb3.RFv2.l1 -0.126714 0.511190 -0.248 0.804226
## cb3.RFv2.l2 0.197484 0.355318 0.556 0.578352
## cb3.RFv3.l1 -0.030323 0.798713 -0.038 0.969716
## cb3.RFv3.l2 0.205980 0.579959 0.355 0.722468
## cb9.minTv1.l1 0.097831 0.298037 0.328 0.742721
## cb9.minTv1.l2 -0.184845 0.224431 -0.824 0.410157
## cb9.minTv2.l1 1.894780 1.024479 1.850 0.064385 .
## cb9.minTv2.l2 0.272927 0.781463 0.349 0.726901
## cb9.minTv3.l1 1.388982 0.557064 2.493 0.012653 *
## cb9.minTv3.l2 1.030672 0.415889 2.478 0.013203 *
## cb5.minRHv1.l1 0.237152 0.297142 0.798 0.424805
## cb5.minRHv1.l2 0.144177 0.195038 0.739 0.459771
## cb5.minRHv2.l1 -0.631561 1.074396 -0.588 0.556647
## cb5.minRHv2.l2 -0.425887 0.776675 -0.548 0.583454
## cb5.minRHv3.l1 -0.533824 0.768072 -0.695 0.487043
## cb5.minRHv3.l2 -0.126402 0.568118 -0.222 0.823931
## cb2.sunv1.l1 0.047054 0.241181 0.195 0.845317
## cb2.sunv1.l2 0.176312 0.170482 1.034 0.301043
## cb2.sunv2.l1 0.584054 0.974407 0.599 0.548910
## cb2.sunv2.l2 1.761893 0.664331 2.652 0.007999 **
## cb2.sunv3.l1 0.105127 0.381316 0.276 0.782781
## cb2.sunv3.l2 0.806680 0.239944 3.362 0.000774 ***
## cb1.avgWindSpv1.l1 0.225235 0.341871 0.659 0.510004
## cb1.avgWindSpv1.l2 0.137268 0.239281 0.574 0.566191
## cb1.avgWindSpv2.l1 0.421899 0.790568 0.534 0.593573
## cb1.avgWindSpv2.l2 0.277928 0.531583 0.523 0.601092
## cb1.avgWindSpv3.l1 -0.606982 0.885746 -0.685 0.493168
## cb1.avgWindSpv3.l2 -0.035109 0.555268 -0.063 0.949584
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 2.274770 45.013654 0.051 0.959696
## sp_ptbBM9 0.477077 4.664324 0.102 0.918533
## sp_ptbBM10 -2.948535 2.645762 -1.114 0.265092
## sp_ptbBM11 -1.067352 2.028066 -0.526 0.598686
## sp_ptbBM12 -0.749641 1.976049 -0.379 0.704418
## sp_ptbBM13 0.843765 2.143014 0.394 0.693782
## sp_ptbBM14 -3.068884 2.077714 -1.477 0.139663
## sp_ptbBM15 1.869402 1.727686 1.082 0.279241
## sp_ptbBM16 -0.707872 1.897789 -0.373 0.709150
## sp_ptbBM17 0.572816 1.694142 0.338 0.735276
## sp_ptbBM18 -1.981922 1.726615 -1.148 0.251024
## sp_ptbBM19 -0.082989 1.663215 -0.050 0.960205
## sp_ptbBM20 -3.159101 1.800187 -1.755 0.079281 .
## sp_ptbBM21 0.388893 2.037345 0.191 0.848618
## sp_ptbBM22 -2.399976 1.698869 -1.413 0.157747
## sp_ptbBM23 -0.638773 1.689087 -0.378 0.705300
## sp_ptbBM24 -0.782850 1.603256 -0.488 0.625346
## sp_ptbBM25 -1.798268 1.357032 -1.325 0.185122
## sp_ptbBM26 -0.275004 1.542580 -0.178 0.858507
## sp_ptbBM27 -4.101012 1.694131 -2.421 0.015490 *
## sp_ptbBM28 -0.806805 1.795592 -0.449 0.653197
## sp_ptbBM29 -1.635411 1.882799 -0.869 0.385062
## sp_ptbBM30 0.366678 1.799110 0.204 0.838501
## sp_ptbBM31 -1.441362 1.741708 -0.828 0.407922
## sp_ptbBM32 -0.358674 1.902639 -0.189 0.850474
## sp_ptbBM33 -2.728528 1.906209 -1.431 0.152318
## sp_ptbBM34 2.340206 2.237426 1.046 0.295590
## sp_ptbBM35 0.373066 2.403179 0.155 0.876633
## sp_ptbBM36 4.514664 2.696754 1.674 0.094109 .
## sp_ptbBM37 1.061059 2.891243 0.367 0.713626
## sp_ptbBM38 1.431577 2.752082 0.520 0.602938
## sp_ptbBM39 1.017115 2.731588 0.372 0.709630
## sp_ptbBM40 -0.599127 2.674783 -0.224 0.822764
## sp_ptbBM41 0.003827 2.472927 0.002 0.998765
## sp_ptbBM42 -5.035922 2.308236 -2.182 0.029130 *
## sp_ptbBM43 0.878875 2.323311 0.378 0.705218
## sp_ptbBM44 -4.270862 2.113080 -2.021 0.043264 *
## sp_ptbBM45 0.392007 2.101134 0.187 0.851999
## sp_ptbBM46 -3.068657 2.114783 -1.451 0.146766
## sp_ptbBM47 -1.651454 2.283079 -0.723 0.469468
## sp_ptbBM48 -2.760040 2.355082 -1.172 0.241217
## sp_ptbBM49 -0.980672 2.545698 -0.385 0.700069
## sp_ptbBM50 -1.663025 2.257917 -0.737 0.461408
## sp_ptbBM51 -0.182800 2.242320 -0.082 0.935026
## sp_ptbBM52 -0.442823 2.105248 -0.210 0.833400
## sp_ptbBM53 0.947888 2.010539 0.471 0.637313
## sp_ptbBM54 0.066778 1.996602 0.033 0.973319
## sp_ptbBM55 -0.461606 1.840587 -0.251 0.801975
## sp_ptbBM56 -0.569687 2.254712 -0.253 0.800527
## sp_ptbBM57 0.508008 1.909516 0.266 0.790208
## sp_ptbBM58 -0.859260 1.781730 -0.482 0.629620
## sp_ptbBM59 -1.496307 1.681322 -0.890 0.373488
## sp_ptbBM60 -1.791240 1.773943 -1.010 0.312615
## sp_ptbBM61 -2.570503 1.672862 -1.537 0.124394
## sp_ptbBM62 -0.905364 1.940919 -0.466 0.640885
## sp_ptbBM63 -3.536753 2.333562 -1.516 0.129620
## sp_ptbBM64 1.572254 2.648354 0.594 0.552731
## sp_ptbBM65 -2.931481 2.705414 -1.084 0.278559
## sp_ptbBM66 -0.161885 2.767210 -0.059 0.953349
## sp_ptbBM67 -3.556814 2.698416 -1.318 0.187466
## sp_ptbBM68 -3.567019 2.829050 -1.261 0.207361
## sp_ptbBM69 -4.124500 2.877901 -1.433 0.151811
## sp_ptbBM70 -6.559641 2.677048 -2.450 0.014273 *
## sp_ptbBM71 -3.148722 2.642337 -1.192 0.233401
## sp_ptbBM72 -3.308239 2.326213 -1.422 0.154981
## sp_ptbBM73 -4.899128 2.361512 -2.075 0.038026 *
## sp_ptbBM74 -0.970548 2.177461 -0.446 0.655796
## sp_ptbBM75 -5.643491 2.229546 -2.531 0.011366 *
## sp_ptbBM76 0.799606 2.177754 0.367 0.713492
## sp_ptbBM77 -4.584730 2.396636 -1.913 0.055750 .
## sp_ptbBM78 1.367188 2.460376 0.556 0.578428
## sp_ptbBM79 -1.560238 2.369122 -0.659 0.510171
## sp_ptbBM80 -0.035781 2.349433 -0.015 0.987849
## sp_ptbBM81 -4.642427 2.427006 -1.913 0.055771 .
## sp_ptbBM82 -0.750542 2.294159 -0.327 0.743552
## sp_ptbBM83 -5.887742 2.299595 -2.560 0.010457 *
## sp_ptbBM84 -0.553201 2.635398 -0.210 0.833736
## sp_ptbBM85 -4.777589 2.851921 -1.675 0.093892 .
## sp_ptbBM86 -2.094519 2.802777 -0.747 0.454882
## sp_ptbBM87 -1.696407 2.655228 -0.639 0.522892
## sp_ptbBM88 -4.528334 2.615617 -1.731 0.083404 .
## sp_ptbBM89 -5.356437 2.726458 -1.965 0.049459 *
## sp_ptbBM90 -6.082782 2.770801 -2.195 0.028141 *
## sp_ptbBM91 -3.635547 2.804640 -1.296 0.194885
## sp_ptbBM92 -1.731640 2.723214 -0.636 0.524854
## sp_ptbBM93 -0.644562 2.701445 -0.239 0.811417
## sp_ptbBM94 -1.566225 2.595514 -0.603 0.546219
## sp_ptbBM95 -3.345100 2.622581 -1.275 0.202133
## sp_ptbBM96 -1.934744 2.629475 -0.736 0.461858
## sp_ptbBM97 -2.560235 2.892529 -0.885 0.376092
## sp_ptbBM98 -7.832546 3.106911 -2.521 0.011702 *
## sp_ptbBM99 -1.891040 3.027862 -0.625 0.532269
## sp_ptbBM100 -2.215755 3.026747 -0.732 0.464133
## sp_ptbBM101 -1.059536 2.820301 -0.376 0.707153
## sp_ptbBM102 -2.954109 2.531101 -1.167 0.243160
## sp_ptbBM103 -4.619438 2.545261 -1.815 0.069537 .
## sp_ptbBM104 -2.730842 2.372104 -1.151 0.249637
## sp_ptbBM105 -3.273169 3.128867 -1.046 0.295506
## sp_ptbBM106 -3.787090 3.921888 -0.966 0.334230
## sp_ptbBM107 -4.738834 4.447391 -1.066 0.286636
## sp_ptbBM108 -5.031651 3.976530 -1.265 0.205751
## sp_ptbBM109 -8.157039 4.227390 -1.930 0.053660 .
## sp_ptbBM110 -12.967765 4.138305 -3.134 0.001727 **
## sp_ptbBM111 -13.842832 4.617172 -2.998 0.002717 **
## sp_ptbBM112 -13.822036 3.873112 -3.569 0.000359 ***
## sp_ptbBM113 -8.463468 2.918365 -2.900 0.003731 **
## sp_ptbBM114 -0.727466 1.997597 -0.364 0.715731
## sp_ptbBM115 -3.218202 1.902037 -1.692 0.090650 .
## sp_ptbBM116 1.236617 1.761545 0.702 0.482675
## sp_ptbBM117 -2.647435 1.595532 -1.659 0.097059 .
## sp_ptbBM118 -0.155990 1.366134 -0.114 0.909092
## sp_ptbBM119 -3.205437 1.502580 -2.133 0.032901 *
## sp_ptbBM120 -2.578330 1.474100 -1.749 0.080276 .
## sp_ptbBM121 -2.208809 1.231093 -1.794 0.072784 .
## sp_ptbBM122 0.374962 0.999906 0.375 0.707662
## sp_ptbBM123 -1.403727 1.004861 -1.397 0.162433
## sp_ptbBM124 2.396657 0.895385 2.677 0.007436 **
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(resid.fullbm, 1) -0.436258 0.031389 -13.899 < 2e-16 ***
## lag(resid.fullbm, 2) -0.478593 0.036135 -13.244 < 2e-16 ***
## lag(resid.fullbm, 3) -0.562952 0.038915 -14.466 < 2e-16 ***
## lag(resid.fullbm, 4) -0.566300 0.041519 -13.639 < 2e-16 ***
## lag(resid.fullbm, 5) -0.548945 0.041140 -13.343 < 2e-16 ***
## lag(resid.fullbm, 6) -0.494001 0.041447 -11.919 < 2e-16 ***
## lag(resid.fullbm, 7) -0.430718 0.039031 -11.035 < 2e-16 ***
## lag(resid.fullbm, 8) -0.359051 0.035833 -10.020 < 2e-16 ***
## lag(resid.fullbm, 9) -0.262765 0.033135 -7.930 2.19e-15 ***
## lag(resid.fullbm, 10) -0.209557 0.029323 -7.147 8.89e-13 ***
## lag(resid.fullbm, 12) -0.071095 0.026202 -2.713 0.006661 **
## lag(resid.fullbm, 17) 0.044570 0.024955 1.786 0.074098 .
## lag(resid.fullbm, 25) -0.050905 0.025257 -2.015 0.043855 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(48297.52) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 547.46 on 701 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2853.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 48298
## Std. Err.: 191308
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2529.387resid.fullbm.ac<-residuals(mod_fullbm.ac, type="deviance")
pred.fullbm.ac<-predict(mod_fullbm.ac, type="response")
pacf(resid.fullbm.ac,na.action = na.omit) 
length(pred.fullbm.ac)## [1] 939length(resid.fullbm.ac)## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,pred.fullbm,lwd=1, col="dark blue")
plot(week$time,resid.fullbm)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,pred.fullbm.ac,lwd=1, col="dark blue")
plot(week$time,resid.fullbm.ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm.ac, week$ptbBM)
abline(coef = c(0,1), col="red")
##checking general model fit plot
plot(mod_fullbm)## Warning: not plotting observations with leverage one:
## 53
plot(mod_fullbm.ac)
#1. plotting the dose reponse and slices now for min temperature
predbm.temp <- crosspred(cb9.minT, mod_fullbm.ac,cen = 24.0, by=0.1,cumul=TRUE)
#cumulative effect of mean temperature
plot(predbm.temp, "overall", xlab="Min temperature (?C)",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of min temperature on ptb")
#90th %
plot(predbm.temp,"slices", var=c(24.6),type="p",ci="bars",col=1,pch=19,ylim=c(0.85,1.04),
xlab="Lag (weeks)",ylab="RR")
##getting est from predbm.temp
#for 5th % - mat
predbm.temp$matRRfit["22.9","lag0"]; predbm.temp$matRRlow["22.9","lag0"]; predbm.temp$matRRhigh["22.9","lag0"]## [1] 0.9073167## [1] 0.7475689## [1] 1.101201predbm.temp$matRRfit["22.9","lag13"]; predbm.temp$matRRlow["22.9","lag13"]; predbm.temp$matRRhigh["22.9","lag13"]## [1] 0.934485## [1] 0.8007847## [1] 1.090508predbm.temp$matRRfit["22.9","lag26"]; predbm.temp$matRRlow["22.9","lag26"]; predbm.temp$matRRhigh["22.9","lag26"]## [1] 0.9624668## [1] 0.835515## [1] 1.108708predbm.temp$matRRfit["22.9","lag39"]; predbm.temp$matRRlow["22.9","lag39"]; predbm.temp$matRRhigh["22.9","lag39"]## [1] 0.9912864## [1] 0.8436407## [1] 1.164772predbm.temp$matRRfit["22.9","lag52"]; predbm.temp$matRRlow["22.9","lag52"]; predbm.temp$matRRhigh["22.9","lag52"]## [1] 1.020969## [1] 0.8320939## [1] 1.252716#for 95th % - mat
predbm.temp$matRRfit["25.1","lag0"]; predbm.temp$matRRlow["25.1","lag0"]; predbm.temp$matRRhigh["25.1","lag0"]## [1] 0.9355388## [1] 0.7665484## [1] 1.141784predbm.temp$matRRfit["25.1","lag13"]; predbm.temp$matRRlow["25.1","lag13"]; predbm.temp$matRRhigh["25.1","lag13"]## [1] 1.031766## [1] 0.8789424## [1] 1.211161predbm.temp$matRRfit["25.1","lag26"]; predbm.temp$matRRlow["25.1","lag26"]; predbm.temp$matRRhigh["25.1","lag26"]## [1] 1.137891## [1] 0.9834511## [1] 1.316584predbm.temp$matRRfit["25.1","lag28"]; predbm.temp$matRRlow["25.1","lag28"]; predbm.temp$matRRhigh["25.1","lag28"]## [1] 1.15516## [1] 0.9977991## [1] 1.337338predbm.temp$matRRfit["25.1","lag29"]; predbm.temp$matRRlow["25.1","lag29"]; predbm.temp$matRRhigh["25.1","lag29"]## [1] 1.163892## [1] 1.004762## [1] 1.348226predbm.temp$matRRfit["25.1","lag30"]; predbm.temp$matRRlow["25.1","lag30"]; predbm.temp$matRRhigh["25.1","lag30"]## [1] 1.172691## [1] 1.011581## [1] 1.35946predbm.temp$matRRfit["25.1","lag39"]; predbm.temp$matRRlow["25.1","lag39"]; predbm.temp$matRRhigh["25.1","lag39"]## [1] 1.254932## [1] 1.066655## [1] 1.476441predbm.temp$matRRfit["25.1","lag52"]; predbm.temp$matRRlow["25.1","lag52"]; predbm.temp$matRRhigh["25.1","lag52"]## [1] 1.384011## [1] 1.129929## [1] 1.695227#2. plotting the dose reponse and slices now for RF
predbm.rf <- crosspred(cb3.RF, mod_fullbm.ac,cen = 44.9, by=0.1,cumul=TRUE)
#cumulative effect of RF
plot(predbm.rf, "overall", xlab="Total precipitation",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of temperature on ptb")
#3. plotting the dose reponse and slices now for wind
predbm.wind <- crosspred(cb1.avgWindSp, mod_fullbm.ac,cen = 4.5, by=0.1,cumul=TRUE)
#cumulative effect of wind
plot(predbm.wind, "overall", xlab="Av wind speed",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of wind speed on ptb")
#4. plotting the dose reponse and slices now for sun
predbm.sun <- crosspred(cb2.sun, mod_fullbm.ac,cen = 50.7, by=0.1,cumul=TRUE)
#cumulative effect of sun
plot(predbm.sun, "overall", xlab="Total sunshine hours",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of sunshine on ptb")
#5. plotting the dose reponse and slices now for minRH
predbm.minRH <- crosspred(cb5.minRH, mod_fullbm.ac,cen = 63.0, by=0.1, cumul=TRUE)
#cumulative effect
plot(predbm.minRH, "overall", xlab="Mean RH",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of minRH on ptb")
Codes for ptbBM analysis: Figures 1-3 and S1 Figure
##3d plots for ptbBM version or all mod_fullbm model------
## Because not possible to arrange grid for contour plots, used GIMP to form 3d & contour multiplot
#tiff("fig_3dFullptbBM_minT.tiff", units="in", width=5, height=6, res=300)
plot(predbm.temp, xlab="Minimum Temperature", zlab="RR", ylab = "Lag (weeks)", theta=200, phi=40, lphi=30,cex.axis=0.6,cex.lab=0.7)
#dev.off()
#tiff("fig_ConFullptbBM_minT.tiff", units="in", width=5, height=6, res=300)
plot(predbm.temp, "contour", xlab="Minimum Temperature", ylab = "Lag (weeks)", key.title=title("RR"))
#dev.off()
#tiff("fig_3dFullptbBM_wind.tiff", units="in", width=5, height=6, res=300)
plot(predbm.wind, xlab="Average wind speed", zlab="RR", ylab = "Lag (weeks)", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
#dev.off()
#tiff("fig_ConFullptbBM_wind.tiff", units="in", width=5, height=6, res=300)
plot(predbm.wind, "contour", xlab="Average wind speed", ylab = "Lag (weeks)", key.title=title("RR"))
#dev.off()
#tiff("fig_3dFullptbBM_rf.tiff", units="in", width=5, height=6, res=300)
plot(predbm.rf, xlab="Total rainfall", zlab="RR", ylab = "Lag (weeks)",theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
#dev.off()
#tiff("fig_ConFullptbBM_rf.tiff", units="in", width=5, height=6, res=300)
plot(predbm.rf, "contour", xlab="Total rainfall", ylab = "Lag (weeks)", key.title=title("RR"))
#dev.off()
#tiff("fig_3dFullptbBM_sun.tiff", units="in", width=5, height=6, res=300)
plot(predbm.sun, xlab="Total sunshine hours", zlab="RR", ylab = "Lag (weeks)", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
#dev.off()
#tiff("fig_ConFullptbBM_sun.tiff", units="in", width=5, height=6, res=300)
plot(predbm.sun, "contour", xlab="Total sunshine hours", ylab = "Lag (weeks)", key.title=title("RR"))
#dev.off()
#tiff("fig_3dFullptbBM_minRH.tiff", units="in", width=5, height=6, res=300)
plot(predbm.minRH, xlab="Minimum RH", zlab="RR", ylab = "Lag (weeks)", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
#dev.off()
#tiff("fig_ConFullptbBM_minRH.tiff", units="in", width=5, height=6, res=300)
plot(predbm.minRH, "contour", xlab="Minimum RH", ylab = "Lag (weeks)", key.title=title("RR"))
#dev.off()
###to make lag plots for uni models, ptbBM version -----
##order: minT, rf, sun, minRH & avgWindSp
#tiff("fig_lagUniptbBM_Apr7.tiff", units="in", width=10, height=6, res=400)
par(mfrow=c(5,5),mar=c(2.5,4,2.5,2))
for (i in c(0,13,26,39,52)){
x = crosspred(cb1.avgWindSp, m1a.ac, cen=4.5)
title = paste(c(i,"week lag"),collapse=" ")
plot(x,lag=i,main=title,ylab="RR (Av wind speed)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(3.0,3.6,4.5,6.1,10.2), labels = c("3.0","5th","50th","95th","10.2"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb9.minT, m9a.ac, cen=24.0)
title = paste(c("Lag",i),collapse="-")
plot(x,lag=i,ylab="RR (Min Temp)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(21.8,22.9,24.0,25.1,26.3), labels = c("21.8","5th","50th","95th","26.3"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb3.RF, m3a.ac, cen=44.9)
plot(x,lag=i,ylab="RR (Rainfall)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(0.0,0.8,44.9,160.7,414.5), labels = c("0.0","5th","50th","95th","414.5"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb2.sun, m2a.ac, cen=50.7)
plot(x,lag=i,ylab="RR (Sunshine hours)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(11.2,30.8,50.7,66.1,76.0), labels = c("11.2","5th","50th","95th","76.0"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb5.minRH, m5a.ac, cen=63.0)
plot(x,lag=i,ylab="RR (Min RH)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(45.0,53.7,63.0,70.3,80.6), labels = c("45.0","5th","50th","95th","80.6"),
cex.axis=0.8)
}
#dev.off()
###to make lag plots for full model, ptbBM version -----
##order: minT, rf, sun, minRH & avgWindSp
#tiff("fig_lagFullptbBM_Apr7.tiff", units="in", width=10, height=6, res=400)
par(mfrow=c(5,5),mar=c(2.5,4,2.5,2))
#axis(1, at= c(3,3.7,4.5,5.4,10.2)
for (i in c(0,13,26,39,52)){
x = crosspred(cb1.avgWindSp, mod_fullbm.ac, cen=4.5)
title = paste(c(i,"week lag"),collapse=" ")
plot(x,lag=i,main=title,ylab="RR (Av wind speed)", xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(3.0,3.6,4.5,6.1,10.2), labels = c("3.0","5th","50th","95th","10.2"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb9.minT, mod_fullbm.ac, cen=24.1)
plot(x,lag=i,ylab="RR (Min Temp)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(21.8,22.9,24.0,25.1,26.3), labels = c("21.8","5th","50th","95th","26.3"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb3.RF, mod_fullbm.ac, cen=44.9)
plot(x,lag=i,ylab="RR (Rainfall)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(0.0,0.8,44.9,160.7,414.5), labels = c("0.0","5th","50th","95th","414.5"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb2.sun, mod_fullbm.ac, cen=50.7)
plot(x,lag=i,ylab="RR (Sunshine hours)", xlab="",col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(11.2,30.8,50.7,66.1,76.0), labels = c("11.2","5th","50th","95th","76.0"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb5.minRH, mod_fullbm.ac, cen=63.0)
plot(x,lag=i,ylab="RR (Min RH)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.5,2.0),xaxt = "n")
axis(1, at= c(45.0,53.7,63.0,70.3,80.6), labels = c("45.0","5th","50th","95th","80.6"),
cex.axis=0.8)
}
# for (i in c(0,13,26,20,39,52)){
# x = crosspred(cb6.meanRH, mod_fullbm.ac, cen=82.7)
# plot(x,lag=i,ylab="RR (Mean RH)",xlab="", col=4,cex.lab=0.9,
# ylim=c(0.5,2.0),xaxt = "n")
# axis(1, at= c(70.0,77.7,82.7,86.6,92.1), labels = c("Min","10th","Median","90th","Max"))
# }
#dev.off()
##lag plots from univariate & multivariate models are similarAnalysis fot ptbBM with ns7 for long-term trend
##smptbBM = Weekly smear-positive PTB case counts from Brunei-Muara district
###Sub-analysis SA2- using smptbBM & ns7 -----
sp_ptbBM <-ns(week$time,df=18*7)
options(na.action="na.exclude")
SA2m1a <- glm.nb(smptbBM ~ cb1.avgWindSp + sp_ptbBM,data=week); summary(SA2m1a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb1.avgWindSp + sp_ptbBM, data = week,
## init.theta = 17447.71098, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1734 -0.8573 -0.1561 0.5224 3.3710
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.292e+01 7.619e+00 -1.696 0.0900 .
## cb1.avgWindSpv1.l1 1.888e-01 3.652e-01 0.517 0.6052
## cb1.avgWindSpv1.l2 2.797e-01 2.576e-01 1.086 0.2775
## cb1.avgWindSpv2.l1 1.384e+00 8.106e-01 1.707 0.0878 .
## cb1.avgWindSpv2.l2 1.731e-01 5.465e-01 0.317 0.7514
## cb1.avgWindSpv3.l1 9.448e-01 9.295e-01 1.016 0.3094
## cb1.avgWindSpv3.l2 1.280e-01 5.572e-01 0.230 0.8183
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.738e+06 2.849e+06 -0.961 0.3365
## sp_ptbBM5 1.443e+01 1.348e+01 1.070 0.2846
## sp_ptbBM6 -2.732e+00 2.488e+00 -1.098 0.2722
## sp_ptbBM7 8.036e-01 2.181e+00 0.368 0.7126
## sp_ptbBM8 -2.085e+00 2.312e+00 -0.902 0.3672
## sp_ptbBM9 -5.571e-01 2.061e+00 -0.270 0.7870
## sp_ptbBM10 -4.160e+00 2.020e+00 -2.060 0.0394 *
## sp_ptbBM11 -5.063e-01 1.851e+00 -0.273 0.7845
## sp_ptbBM12 -2.161e+00 1.725e+00 -1.253 0.2104
## sp_ptbBM13 -1.330e+00 1.970e+00 -0.675 0.4994
## sp_ptbBM14 -6.800e-01 1.669e+00 -0.407 0.6837
## sp_ptbBM15 6.273e-01 1.174e+00 0.534 0.5932
## sp_ptbBM16 -6.993e-03 1.129e+00 -0.006 0.9951
## sp_ptbBM17 -2.985e-01 1.090e+00 -0.274 0.7843
## sp_ptbBM18 -1.360e-01 1.213e+00 -0.112 0.9107
## sp_ptbBM19 -1.229e-01 1.151e+00 -0.107 0.9150
## sp_ptbBM20 -9.573e-01 1.243e+00 -0.770 0.4414
## sp_ptbBM21 9.940e-01 1.553e+00 0.640 0.5222
## sp_ptbBM22 1.047e+00 1.206e+00 0.868 0.3855
## sp_ptbBM23 -4.498e-01 1.183e+00 -0.380 0.7037
## sp_ptbBM24 3.355e-01 1.234e+00 0.272 0.7858
## sp_ptbBM25 4.234e-02 1.044e+00 0.041 0.9676
## sp_ptbBM26 7.956e-01 1.077e+00 0.738 0.4603
## sp_ptbBM27 -2.206e+00 1.380e+00 -1.599 0.1098
## sp_ptbBM28 3.666e-01 1.375e+00 0.267 0.7898
## sp_ptbBM29 8.775e-01 1.522e+00 0.576 0.5644
## sp_ptbBM30 -2.213e-01 1.132e+00 -0.195 0.8450
## sp_ptbBM31 8.782e-01 1.146e+00 0.766 0.4437
## sp_ptbBM32 -2.348e-01 1.120e+00 -0.210 0.8339
## sp_ptbBM33 -1.144e+00 1.264e+00 -0.905 0.3655
## sp_ptbBM34 2.072e-01 1.033e+00 0.201 0.8410
## sp_ptbBM35 3.297e-01 1.041e+00 0.317 0.7515
## sp_ptbBM36 1.591e+00 1.078e+00 1.475 0.1402
## sp_ptbBM37 -3.041e-01 1.163e+00 -0.262 0.7937
## sp_ptbBM38 7.258e-01 1.232e+00 0.589 0.5558
## sp_ptbBM39 -1.248e+00 1.480e+00 -0.843 0.3991
## sp_ptbBM40 9.130e-01 1.304e+00 0.700 0.4839
## sp_ptbBM41 -1.876e+00 1.676e+00 -1.120 0.2629
## sp_ptbBM42 -1.123e+00 1.761e+00 -0.638 0.5238
## sp_ptbBM43 -1.024e+00 1.909e+00 -0.536 0.5918
## sp_ptbBM44 -1.096e+00 1.919e+00 -0.571 0.5680
## sp_ptbBM45 -4.228e-01 1.893e+00 -0.223 0.8233
## sp_ptbBM46 -1.298e+00 1.919e+00 -0.677 0.4987
## sp_ptbBM47 -1.118e+00 2.110e+00 -0.530 0.5962
## sp_ptbBM48 -1.288e+00 2.276e+00 -0.566 0.5714
## sp_ptbBM49 -7.155e-01 2.185e+00 -0.327 0.7434
## sp_ptbBM50 -2.271e+00 2.023e+00 -1.123 0.2616
## sp_ptbBM51 3.116e-01 1.746e+00 0.178 0.8584
## sp_ptbBM52 -2.131e+00 1.556e+00 -1.370 0.1707
## sp_ptbBM53 -5.951e-01 1.482e+00 -0.401 0.6881
## sp_ptbBM54 -1.312e+00 1.508e+00 -0.870 0.3843
## sp_ptbBM55 -9.392e-01 1.335e+00 -0.703 0.4818
## sp_ptbBM56 -1.752e-01 1.395e+00 -0.126 0.9001
## sp_ptbBM57 5.453e-02 1.265e+00 0.043 0.9656
## sp_ptbBM58 -2.184e-01 1.220e+00 -0.179 0.8579
## sp_ptbBM59 -9.233e-01 1.201e+00 -0.769 0.4419
## sp_ptbBM60 -8.580e-01 1.389e+00 -0.618 0.5369
## sp_ptbBM61 6.316e-02 1.183e+00 0.053 0.9574
## sp_ptbBM62 -4.469e-01 1.476e+00 -0.303 0.7620
## sp_ptbBM63 -1.288e-01 1.675e+00 -0.077 0.9387
## sp_ptbBM64 3.861e-01 1.985e+00 0.195 0.8457
## sp_ptbBM65 -1.633e-01 1.986e+00 -0.082 0.9345
## sp_ptbBM66 -1.921e+00 2.393e+00 -0.803 0.4221
## sp_ptbBM67 -6.949e-02 2.341e+00 -0.030 0.9763
## sp_ptbBM68 -1.870e+00 2.574e+00 -0.726 0.4675
## sp_ptbBM69 -6.537e-01 2.507e+00 -0.261 0.7943
## sp_ptbBM70 -3.100e+00 2.353e+00 -1.318 0.1876
## sp_ptbBM71 -9.928e-01 2.373e+00 -0.418 0.6757
## sp_ptbBM72 -3.409e+00 2.366e+00 -1.441 0.1496
## sp_ptbBM73 -1.867e+00 2.194e+00 -0.851 0.3947
## sp_ptbBM74 -1.555e+00 2.106e+00 -0.739 0.4602
## sp_ptbBM75 -1.331e+00 2.041e+00 -0.652 0.5143
## sp_ptbBM76 -1.091e+00 2.075e+00 -0.526 0.5990
## sp_ptbBM77 -1.764e+00 2.151e+00 -0.820 0.4122
## sp_ptbBM78 -3.780e-01 2.131e+00 -0.177 0.8592
## sp_ptbBM79 -1.565e+00 2.046e+00 -0.765 0.4444
## sp_ptbBM80 -9.256e-01 2.132e+00 -0.434 0.6641
## sp_ptbBM81 -1.991e+00 2.182e+00 -0.913 0.3615
## sp_ptbBM82 -1.926e+00 2.206e+00 -0.873 0.3827
## sp_ptbBM83 -1.635e+00 2.131e+00 -0.767 0.4429
## sp_ptbBM84 -8.141e-01 2.395e+00 -0.340 0.7339
## sp_ptbBM85 -2.816e+00 2.444e+00 -1.152 0.2493
## sp_ptbBM86 -6.597e-01 2.243e+00 -0.294 0.7686
## sp_ptbBM87 -2.476e+00 2.214e+00 -1.118 0.2635
## sp_ptbBM88 -8.568e-01 2.139e+00 -0.401 0.6887
## sp_ptbBM89 -3.580e+00 2.313e+00 -1.548 0.1216
## sp_ptbBM90 -1.411e+00 2.232e+00 -0.632 0.5272
## sp_ptbBM91 -1.904e+00 2.156e+00 -0.883 0.3773
## sp_ptbBM92 -2.524e-01 2.317e+00 -0.109 0.9132
## sp_ptbBM93 -2.605e+00 2.273e+00 -1.146 0.2516
## sp_ptbBM94 -1.947e+00 2.154e+00 -0.904 0.3661
## sp_ptbBM95 -2.215e+00 2.168e+00 -1.022 0.3069
## sp_ptbBM96 -8.057e-01 2.067e+00 -0.390 0.6967
## sp_ptbBM97 -2.531e+00 2.146e+00 -1.179 0.2382
## sp_ptbBM98 -1.875e+00 2.305e+00 -0.813 0.4160
## sp_ptbBM99 -3.847e-01 2.248e+00 -0.171 0.8641
## sp_ptbBM100 -1.011e+00 2.357e+00 -0.429 0.6679
## sp_ptbBM101 -8.368e-01 2.196e+00 -0.381 0.7032
## sp_ptbBM102 -9.472e-01 1.850e+00 -0.512 0.6086
## sp_ptbBM103 -5.643e-01 1.563e+00 -0.361 0.7180
## sp_ptbBM104 -4.033e-01 1.431e+00 -0.282 0.7781
## sp_ptbBM105 -2.954e-01 1.437e+00 -0.206 0.8371
## sp_ptbBM106 -4.336e-01 1.942e+00 -0.223 0.8234
## sp_ptbBM107 -6.866e-01 1.568e+00 -0.438 0.6616
## sp_ptbBM108 3.251e-01 1.498e+00 0.217 0.8283
## sp_ptbBM109 -5.456e-01 1.541e+00 -0.354 0.7234
## sp_ptbBM110 -1.294e+00 1.639e+00 -0.790 0.4298
## sp_ptbBM111 -1.018e+00 1.764e+00 -0.577 0.5638
## sp_ptbBM112 -2.259e+00 1.405e+00 -1.607 0.1080
## sp_ptbBM113 1.759e+00 1.017e+00 1.730 0.0837 .
## sp_ptbBM114 4.648e-01 9.390e-01 0.495 0.6206
## sp_ptbBM115 -4.518e-01 1.005e+00 -0.449 0.6531
## sp_ptbBM116 1.679e+00 9.161e-01 1.833 0.0669 .
## sp_ptbBM117 -1.008e+00 1.109e+00 -0.908 0.3638
## sp_ptbBM118 1.673e+00 1.019e+00 1.642 0.1005
## sp_ptbBM119 -5.145e-01 1.093e+00 -0.471 0.6379
## sp_ptbBM120 1.698e+00 1.178e+00 1.442 0.1494
## sp_ptbBM121 -2.708e-01 9.554e-01 -0.283 0.7768
## sp_ptbBM122 1.899e+00 9.358e-01 2.030 0.0424 *
## sp_ptbBM123 -7.059e-01 8.490e-01 -0.831 0.4057
## sp_ptbBM124 2.208e+00 1.104e+00 2.000 0.0455 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17447.71) family taken to be 1)
##
## Null deviance: 1073.13 on 886 degrees of freedom
## Residual deviance: 925.89 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2887
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17448
## Std. Err.: 102606
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2629.011SA2m2a <- glm.nb(smptbBM ~ cb2.sun + sp_ptbBM,data=week); summary(SA2m2a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb2.sun + sp_ptbBM, data = week, init.theta = 17721.422,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2402 -0.8797 -0.1478 0.5391 3.3445
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.467e+00 1.092e+01 -0.775 0.43805
## cb2.sunv1.l1 2.908e-01 2.303e-01 1.263 0.20667
## cb2.sunv1.l2 -2.009e-01 1.695e-01 -1.185 0.23596
## cb2.sunv2.l1 8.097e-01 9.558e-01 0.847 0.39691
## cb2.sunv2.l2 -5.924e-01 6.575e-01 -0.901 0.36752
## cb2.sunv3.l1 6.334e-01 3.487e-01 1.816 0.06933 .
## cb2.sunv3.l2 2.178e-01 2.457e-01 0.887 0.37528
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.119e+06 2.891e+06 -0.733 0.46359
## sp_ptbBM5 1.147e+01 1.320e+01 0.869 0.38504
## sp_ptbBM6 -4.368e-02 2.191e+00 -0.020 0.98409
## sp_ptbBM7 2.515e+00 1.266e+00 1.986 0.04701 *
## sp_ptbBM8 1.675e-03 1.131e+00 0.001 0.99882
## sp_ptbBM9 1.487e+00 1.023e+00 1.453 0.14612
## sp_ptbBM10 -2.859e+00 1.332e+00 -2.147 0.03180 *
## sp_ptbBM11 1.597e+00 9.418e-01 1.696 0.08987 .
## sp_ptbBM12 -4.623e-01 1.133e+00 -0.408 0.68330
## sp_ptbBM13 4.868e-01 1.125e+00 0.433 0.66513
## sp_ptbBM14 -2.666e-01 1.146e+00 -0.233 0.81604
## sp_ptbBM15 -1.837e-01 1.120e+00 -0.164 0.86973
## sp_ptbBM16 2.534e-01 1.106e+00 0.229 0.81873
## sp_ptbBM17 -3.439e-01 1.121e+00 -0.307 0.75900
## sp_ptbBM18 4.976e-02 1.140e+00 0.044 0.96519
## sp_ptbBM19 -2.994e-01 1.153e+00 -0.260 0.79506
## sp_ptbBM20 2.776e-01 1.098e+00 0.253 0.80032
## sp_ptbBM21 6.544e-01 1.186e+00 0.552 0.58115
## sp_ptbBM22 1.388e+00 1.065e+00 1.303 0.19247
## sp_ptbBM23 5.676e-01 1.173e+00 0.484 0.62846
## sp_ptbBM24 1.413e+00 1.042e+00 1.356 0.17513
## sp_ptbBM25 7.012e-01 1.078e+00 0.650 0.51551
## sp_ptbBM26 1.197e+00 1.162e+00 1.030 0.30287
## sp_ptbBM27 -7.627e-01 1.352e+00 -0.564 0.57257
## sp_ptbBM28 -6.348e-01 1.236e+00 -0.513 0.60763
## sp_ptbBM29 5.601e-01 1.222e+00 0.458 0.64668
## sp_ptbBM30 9.812e-02 1.144e+00 0.086 0.93167
## sp_ptbBM31 9.386e-01 1.151e+00 0.816 0.41463
## sp_ptbBM32 -1.915e-01 1.223e+00 -0.157 0.87563
## sp_ptbBM33 -3.646e-01 1.221e+00 -0.299 0.76522
## sp_ptbBM34 1.408e+00 1.190e+00 1.184 0.23660
## sp_ptbBM35 1.426e+00 1.217e+00 1.172 0.24131
## sp_ptbBM36 2.298e+00 1.217e+00 1.888 0.05897 .
## sp_ptbBM37 1.599e+00 1.173e+00 1.364 0.17264
## sp_ptbBM38 2.810e+00 1.139e+00 2.466 0.01366 *
## sp_ptbBM39 1.103e+00 1.152e+00 0.957 0.33848
## sp_ptbBM40 3.704e+00 1.211e+00 3.059 0.00222 **
## sp_ptbBM41 1.024e+00 1.129e+00 0.907 0.36440
## sp_ptbBM42 1.968e+00 1.224e+00 1.608 0.10773
## sp_ptbBM43 1.465e+00 1.033e+00 1.418 0.15616
## sp_ptbBM44 1.285e+00 9.393e-01 1.368 0.17125
## sp_ptbBM45 1.374e+00 8.925e-01 1.539 0.12374
## sp_ptbBM46 5.825e-02 9.360e-01 0.062 0.95038
## sp_ptbBM47 3.205e-01 1.093e+00 0.293 0.76931
## sp_ptbBM48 6.675e-01 9.452e-01 0.706 0.48008
## sp_ptbBM49 6.725e-01 1.282e+00 0.525 0.59990
## sp_ptbBM50 5.094e-01 1.052e+00 0.484 0.62811
## sp_ptbBM51 2.731e+00 1.056e+00 2.585 0.00973 **
## sp_ptbBM52 2.523e-01 9.977e-01 0.253 0.80034
## sp_ptbBM53 1.492e+00 9.840e-01 1.516 0.12949
## sp_ptbBM54 2.814e-01 1.004e+00 0.280 0.77930
## sp_ptbBM55 1.620e-01 1.030e+00 0.157 0.87502
## sp_ptbBM56 1.293e+00 1.208e+00 1.070 0.28464
## sp_ptbBM57 1.580e+00 1.154e+00 1.369 0.17098
## sp_ptbBM58 1.080e+00 1.043e+00 1.035 0.30056
## sp_ptbBM59 1.014e+00 1.042e+00 0.973 0.33064
## sp_ptbBM60 7.721e-01 9.887e-01 0.781 0.43485
## sp_ptbBM61 1.244e+00 1.070e+00 1.163 0.24491
## sp_ptbBM62 4.166e-01 1.073e+00 0.388 0.69794
## sp_ptbBM63 8.239e-02 1.236e+00 0.067 0.94686
## sp_ptbBM64 2.293e-01 1.158e+00 0.198 0.84304
## sp_ptbBM65 1.355e+00 1.058e+00 1.281 0.20031
## sp_ptbBM66 -5.301e-01 1.035e+00 -0.512 0.60854
## sp_ptbBM67 1.466e+00 9.512e-01 1.541 0.12322
## sp_ptbBM68 -3.877e-01 1.010e+00 -0.384 0.70111
## sp_ptbBM69 7.004e-01 1.036e+00 0.676 0.49881
## sp_ptbBM70 -7.821e-01 1.417e+00 -0.552 0.58103
## sp_ptbBM71 2.085e+00 1.323e+00 1.576 0.11508
## sp_ptbBM72 -3.898e-01 1.452e+00 -0.268 0.78834
## sp_ptbBM73 1.398e+00 1.295e+00 1.079 0.28037
## sp_ptbBM74 1.572e+00 1.293e+00 1.215 0.22426
## sp_ptbBM75 1.580e+00 1.173e+00 1.347 0.17813
## sp_ptbBM76 1.910e+00 1.205e+00 1.585 0.11294
## sp_ptbBM77 1.222e+00 1.240e+00 0.985 0.32449
## sp_ptbBM78 2.035e+00 1.201e+00 1.694 0.09035 .
## sp_ptbBM79 9.500e-01 1.146e+00 0.829 0.40710
## sp_ptbBM80 1.565e+00 1.185e+00 1.321 0.18666
## sp_ptbBM81 -7.640e-02 1.269e+00 -0.060 0.95198
## sp_ptbBM82 3.392e-01 1.147e+00 0.296 0.76734
## sp_ptbBM83 1.949e-01 1.151e+00 0.169 0.86547
## sp_ptbBM84 4.363e-01 1.159e+00 0.376 0.70662
## sp_ptbBM85 -1.779e+00 1.309e+00 -1.359 0.17412
## sp_ptbBM86 8.353e-01 1.236e+00 0.676 0.49910
## sp_ptbBM87 -4.653e-02 1.178e+00 -0.040 0.96849
## sp_ptbBM88 1.786e+00 1.197e+00 1.492 0.13571
## sp_ptbBM89 -8.129e-01 1.422e+00 -0.572 0.56764
## sp_ptbBM90 1.882e+00 1.306e+00 1.440 0.14973
## sp_ptbBM91 1.870e+00 1.374e+00 1.361 0.17356
## sp_ptbBM92 2.376e+00 1.144e+00 2.076 0.03786 *
## sp_ptbBM93 7.167e-01 1.234e+00 0.581 0.56155
## sp_ptbBM94 1.181e+00 1.189e+00 0.993 0.32077
## sp_ptbBM95 9.451e-01 1.080e+00 0.875 0.38154
## sp_ptbBM96 2.252e+00 1.052e+00 2.140 0.03233 *
## sp_ptbBM97 2.473e-01 1.074e+00 0.230 0.81791
## sp_ptbBM98 1.336e+00 1.420e+00 0.941 0.34672
## sp_ptbBM99 1.985e+00 1.067e+00 1.859 0.06301 .
## sp_ptbBM100 1.836e+00 1.102e+00 1.666 0.09564 .
## sp_ptbBM101 1.491e+00 1.010e+00 1.476 0.13982
## sp_ptbBM102 1.356e+00 1.041e+00 1.302 0.19277
## sp_ptbBM103 1.168e+00 1.052e+00 1.110 0.26679
## sp_ptbBM104 1.310e+00 9.769e-01 1.341 0.17985
## sp_ptbBM105 -4.534e-01 1.317e+00 -0.344 0.73060
## sp_ptbBM106 -3.253e-01 1.134e+00 -0.287 0.77425
## sp_ptbBM107 -1.059e+00 1.232e+00 -0.859 0.39027
## sp_ptbBM108 3.786e-03 1.245e+00 0.003 0.99757
## sp_ptbBM109 -2.306e-01 1.210e+00 -0.190 0.84893
## sp_ptbBM110 -3.911e-01 1.381e+00 -0.283 0.77701
## sp_ptbBM111 6.038e-01 1.248e+00 0.484 0.62841
## sp_ptbBM112 -4.786e-01 1.143e+00 -0.419 0.67534
## sp_ptbBM113 2.366e+00 1.072e+00 2.208 0.02728 *
## sp_ptbBM114 1.193e+00 9.805e-01 1.217 0.22379
## sp_ptbBM115 8.943e-01 1.151e+00 0.777 0.43713
## sp_ptbBM116 2.755e+00 1.023e+00 2.694 0.00707 **
## sp_ptbBM117 -8.531e-01 1.275e+00 -0.669 0.50330
## sp_ptbBM118 2.922e+00 1.162e+00 2.514 0.01193 *
## sp_ptbBM119 7.258e-01 1.324e+00 0.548 0.58365
## sp_ptbBM120 2.051e+00 1.133e+00 1.811 0.07019 .
## sp_ptbBM121 4.275e-01 1.064e+00 0.402 0.68783
## sp_ptbBM122 2.271e+00 9.682e-01 2.346 0.01899 *
## sp_ptbBM123 7.221e-01 8.618e-01 0.838 0.40209
## sp_ptbBM124 1.970e+00 1.122e+00 1.756 0.07912 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17721.42) family taken to be 1)
##
## Null deviance: 1073.1 on 886 degrees of freedom
## Residual deviance: 920.3 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2881.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17721
## Std. Err.: 102690
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2623.416SA2m3a <- glm.nb(smptbBM ~ cb3.RF + sp_ptbBM,data=week); summary(SA2m3a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb3.RF + sp_ptbBM, data = week, init.theta = 17311.02781,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1072 -0.8474 -0.1545 0.5437 3.3762
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.039e-01 3.091e+00 0.195 0.84509
## cb3.RFv1.l1 2.795e-01 3.032e-01 0.922 0.35673
## cb3.RFv1.l2 5.948e-02 2.121e-01 0.280 0.77918
## cb3.RFv2.l1 -3.413e-01 4.369e-01 -0.781 0.43464
## cb3.RFv2.l2 4.430e-01 3.182e-01 1.392 0.16393
## cb3.RFv3.l1 -4.776e-01 7.029e-01 -0.679 0.49683
## cb3.RFv3.l2 5.909e-01 5.428e-01 1.089 0.27628
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.127e+06 2.881e+06 -0.738 0.46045
## sp_ptbBM5 9.343e+00 1.300e+01 0.719 0.47230
## sp_ptbBM6 -1.472e-01 2.150e+00 -0.068 0.94541
## sp_ptbBM7 2.343e+00 1.288e+00 1.820 0.06881 .
## sp_ptbBM8 5.184e-02 1.201e+00 0.043 0.96557
## sp_ptbBM9 2.209e+00 1.103e+00 2.002 0.04530 *
## sp_ptbBM10 -1.757e+00 1.397e+00 -1.257 0.20873
## sp_ptbBM11 2.289e+00 9.885e-01 2.315 0.02059 *
## sp_ptbBM12 8.056e-01 1.116e+00 0.722 0.47046
## sp_ptbBM13 2.493e+00 1.160e+00 2.149 0.03164 *
## sp_ptbBM14 9.179e-01 1.116e+00 0.823 0.41064
## sp_ptbBM15 1.380e+00 1.029e+00 1.341 0.17988
## sp_ptbBM16 1.299e+00 1.160e+00 1.120 0.26280
## sp_ptbBM17 1.235e+00 1.123e+00 1.099 0.27183
## sp_ptbBM18 1.364e+00 1.118e+00 1.220 0.22237
## sp_ptbBM19 1.429e+00 1.082e+00 1.321 0.18636
## sp_ptbBM20 2.140e+00 1.300e+00 1.646 0.09973 .
## sp_ptbBM21 1.889e+00 1.070e+00 1.765 0.07748 .
## sp_ptbBM22 2.051e+00 1.055e+00 1.945 0.05175 .
## sp_ptbBM23 8.189e-01 1.085e+00 0.755 0.45028
## sp_ptbBM24 1.761e+00 1.109e+00 1.587 0.11243
## sp_ptbBM25 1.351e+00 1.073e+00 1.259 0.20811
## sp_ptbBM26 2.111e+00 1.134e+00 1.861 0.06275 .
## sp_ptbBM27 3.860e-01 1.369e+00 0.282 0.77803
## sp_ptbBM28 8.888e-01 1.259e+00 0.706 0.48027
## sp_ptbBM29 1.938e+00 1.140e+00 1.699 0.08924 .
## sp_ptbBM30 1.195e+00 1.149e+00 1.040 0.29846
## sp_ptbBM31 2.028e+00 9.850e-01 2.059 0.03946 *
## sp_ptbBM32 1.036e+00 1.060e+00 0.978 0.32831
## sp_ptbBM33 6.523e-01 1.147e+00 0.569 0.56967
## sp_ptbBM34 1.885e+00 1.088e+00 1.733 0.08308 .
## sp_ptbBM35 1.669e+00 1.015e+00 1.644 0.10018
## sp_ptbBM36 1.487e+00 9.289e-01 1.601 0.10942
## sp_ptbBM37 1.420e+00 1.074e+00 1.322 0.18619
## sp_ptbBM38 1.827e+00 9.800e-01 1.864 0.06232 .
## sp_ptbBM39 6.273e-01 1.033e+00 0.607 0.54388
## sp_ptbBM40 2.226e+00 9.734e-01 2.287 0.02222 *
## sp_ptbBM41 1.082e+00 1.066e+00 1.016 0.30985
## sp_ptbBM42 1.446e+00 1.116e+00 1.295 0.19534
## sp_ptbBM43 1.406e+00 1.030e+00 1.365 0.17228
## sp_ptbBM44 1.827e+00 1.015e+00 1.799 0.07198 .
## sp_ptbBM45 2.026e+00 1.005e+00 2.016 0.04383 *
## sp_ptbBM46 1.327e+00 9.894e-01 1.341 0.17993
## sp_ptbBM47 1.198e+00 9.042e-01 1.325 0.18528
## sp_ptbBM48 1.473e+00 9.582e-01 1.537 0.12425
## sp_ptbBM49 2.367e+00 1.036e+00 2.285 0.02230 *
## sp_ptbBM50 5.538e-01 1.060e+00 0.522 0.60138
## sp_ptbBM51 3.215e+00 9.693e-01 3.317 0.00091 ***
## sp_ptbBM52 8.381e-01 1.060e+00 0.790 0.42932
## sp_ptbBM53 1.713e+00 1.022e+00 1.676 0.09370 .
## sp_ptbBM54 1.085e+00 1.074e+00 1.010 0.31239
## sp_ptbBM55 9.351e-01 1.069e+00 0.875 0.38173
## sp_ptbBM56 2.306e+00 1.449e+00 1.592 0.11149
## sp_ptbBM57 2.171e+00 1.160e+00 1.872 0.06122 .
## sp_ptbBM58 1.297e+00 1.126e+00 1.152 0.24917
## sp_ptbBM59 8.377e-01 1.065e+00 0.787 0.43152
## sp_ptbBM60 6.641e-01 1.108e+00 0.599 0.54902
## sp_ptbBM61 1.261e+00 1.023e+00 1.233 0.21763
## sp_ptbBM62 1.011e+00 1.277e+00 0.792 0.42842
## sp_ptbBM63 2.032e+00 1.454e+00 1.397 0.16239
## sp_ptbBM64 2.248e+00 1.298e+00 1.732 0.08329 .
## sp_ptbBM65 2.450e+00 1.146e+00 2.138 0.03249 *
## sp_ptbBM66 1.086e+00 1.092e+00 0.995 0.31993
## sp_ptbBM67 3.150e+00 1.137e+00 2.771 0.00559 **
## sp_ptbBM68 1.235e+00 9.704e-01 1.272 0.20330
## sp_ptbBM69 2.731e+00 1.234e+00 2.212 0.02695 *
## sp_ptbBM70 7.114e-02 1.289e+00 0.055 0.95597
## sp_ptbBM71 1.381e+00 1.313e+00 1.052 0.29287
## sp_ptbBM72 -8.720e-01 1.318e+00 -0.661 0.50831
## sp_ptbBM73 2.941e-01 1.130e+00 0.260 0.79468
## sp_ptbBM74 8.138e-01 9.769e-01 0.833 0.40481
## sp_ptbBM75 1.131e+00 1.056e+00 1.071 0.28412
## sp_ptbBM76 1.309e+00 1.047e+00 1.250 0.21148
## sp_ptbBM77 1.065e+00 1.078e+00 0.988 0.32309
## sp_ptbBM78 2.457e+00 1.153e+00 2.131 0.03305 *
## sp_ptbBM79 1.399e+00 1.181e+00 1.184 0.23624
## sp_ptbBM80 1.923e+00 1.100e+00 1.749 0.08028 .
## sp_ptbBM81 7.618e-01 1.201e+00 0.634 0.52589
## sp_ptbBM82 8.513e-01 1.089e+00 0.781 0.43457
## sp_ptbBM83 1.363e+00 1.090e+00 1.251 0.21088
## sp_ptbBM84 1.684e+00 1.069e+00 1.575 0.11524
## sp_ptbBM85 -3.127e-01 1.192e+00 -0.262 0.79314
## sp_ptbBM86 2.462e+00 1.015e+00 2.425 0.01533 *
## sp_ptbBM87 3.422e-01 1.031e+00 0.332 0.73990
## sp_ptbBM88 2.147e+00 9.900e-01 2.168 0.03014 *
## sp_ptbBM89 -7.474e-01 1.161e+00 -0.644 0.51959
## sp_ptbBM90 2.262e+00 1.079e+00 2.097 0.03598 *
## sp_ptbBM91 1.452e+00 1.202e+00 1.208 0.22701
## sp_ptbBM92 1.631e+00 1.024e+00 1.594 0.11104
## sp_ptbBM93 6.669e-01 1.221e+00 0.546 0.58481
## sp_ptbBM94 1.129e+00 1.174e+00 0.962 0.33620
## sp_ptbBM95 9.532e-01 1.082e+00 0.881 0.37836
## sp_ptbBM96 1.854e+00 9.216e-01 2.012 0.04421 *
## sp_ptbBM97 5.638e-01 1.148e+00 0.491 0.62332
## sp_ptbBM98 1.678e+00 1.272e+00 1.319 0.18721
## sp_ptbBM99 1.826e+00 1.130e+00 1.616 0.10609
## sp_ptbBM100 1.551e+00 1.052e+00 1.475 0.14015
## sp_ptbBM101 1.529e+00 9.687e-01 1.579 0.11445
## sp_ptbBM102 1.299e+00 1.009e+00 1.288 0.19784
## sp_ptbBM103 1.403e+00 1.009e+00 1.391 0.16429
## sp_ptbBM104 2.148e+00 9.998e-01 2.148 0.03169 *
## sp_ptbBM105 1.416e+00 1.286e+00 1.101 0.27109
## sp_ptbBM106 6.852e-01 1.185e+00 0.578 0.56308
## sp_ptbBM107 3.551e-01 1.110e+00 0.320 0.74908
## sp_ptbBM108 1.269e+00 1.068e+00 1.188 0.23497
## sp_ptbBM109 1.451e+00 1.144e+00 1.269 0.20456
## sp_ptbBM110 8.159e-01 9.817e-01 0.831 0.40595
## sp_ptbBM111 2.318e+00 1.289e+00 1.798 0.07216 .
## sp_ptbBM112 -6.968e-02 1.226e+00 -0.057 0.95467
## sp_ptbBM113 2.564e+00 1.003e+00 2.556 0.01059 *
## sp_ptbBM114 1.483e+00 1.042e+00 1.424 0.15458
## sp_ptbBM115 7.475e-01 1.129e+00 0.662 0.50802
## sp_ptbBM116 2.507e+00 1.005e+00 2.495 0.01259 *
## sp_ptbBM117 -7.662e-01 1.201e+00 -0.638 0.52351
## sp_ptbBM118 2.598e+00 1.116e+00 2.328 0.01993 *
## sp_ptbBM119 1.365e-01 1.302e+00 0.105 0.91650
## sp_ptbBM120 1.603e+00 1.076e+00 1.489 0.13645
## sp_ptbBM121 4.199e-03 1.043e+00 0.004 0.99679
## sp_ptbBM122 1.997e+00 1.005e+00 1.987 0.04692 *
## sp_ptbBM123 -7.013e-01 8.890e-01 -0.789 0.43016
## sp_ptbBM124 1.960e+00 1.110e+00 1.766 0.07741 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17311.03) family taken to be 1)
##
## Null deviance: 1073.13 on 886 degrees of freedom
## Residual deviance: 926.96 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2888.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17311
## Std. Err.: 101315
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2630.083SA2m5a <- glm.nb(smptbBM ~ cb5.minRH + sp_ptbBM,data=week); summary(SA2m5a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb5.minRH + sp_ptbBM, data = week,
## init.theta = 17146.60379, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2022 -0.8207 -0.1746 0.5366 3.4423
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.245e+01 1.203e+01 1.035 0.3006
## cb5.minRHv1.l1 -2.014e-01 2.717e-01 -0.741 0.4584
## cb5.minRHv1.l2 -1.065e-01 1.854e-01 -0.574 0.5657
## cb5.minRHv2.l1 -1.326e+00 1.052e+00 -1.261 0.2075
## cb5.minRHv2.l2 -4.867e-01 7.829e-01 -0.622 0.5341
## cb5.minRHv3.l1 -1.049e+00 6.873e-01 -1.526 0.1269
## cb5.minRHv3.l2 -7.475e-02 5.228e-01 -0.143 0.8863
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.208e+06 2.877e+06 -0.768 0.4427
## sp_ptbBM5 1.047e+01 1.294e+01 0.809 0.4185
## sp_ptbBM6 -7.047e-01 2.175e+00 -0.324 0.7459
## sp_ptbBM7 1.768e+00 1.268e+00 1.394 0.1633
## sp_ptbBM8 -4.314e-01 1.200e+00 -0.360 0.7191
## sp_ptbBM9 1.238e+00 1.170e+00 1.058 0.2903
## sp_ptbBM10 -2.633e+00 1.299e+00 -2.028 0.0426 *
## sp_ptbBM11 1.305e+00 1.140e+00 1.145 0.2523
## sp_ptbBM12 -6.931e-01 1.044e+00 -0.664 0.5066
## sp_ptbBM13 3.038e-01 1.177e+00 0.258 0.7963
## sp_ptbBM14 -5.900e-01 1.317e+00 -0.448 0.6541
## sp_ptbBM15 -3.868e-01 1.396e+00 -0.277 0.7818
## sp_ptbBM16 -9.142e-01 1.547e+00 -0.591 0.5546
## sp_ptbBM17 -6.489e-01 1.325e+00 -0.490 0.6242
## sp_ptbBM18 -3.635e-02 1.334e+00 -0.027 0.9783
## sp_ptbBM19 -1.132e-01 1.236e+00 -0.092 0.9270
## sp_ptbBM20 -2.381e-01 1.373e+00 -0.173 0.8623
## sp_ptbBM21 1.008e+00 1.268e+00 0.795 0.4266
## sp_ptbBM22 1.222e+00 1.158e+00 1.055 0.2914
## sp_ptbBM23 1.961e-02 1.155e+00 0.017 0.9865
## sp_ptbBM24 8.196e-01 1.013e+00 0.809 0.4184
## sp_ptbBM25 6.572e-01 9.107e-01 0.722 0.4705
## sp_ptbBM26 8.621e-01 9.685e-01 0.890 0.3734
## sp_ptbBM27 -7.593e-01 1.087e+00 -0.699 0.4848
## sp_ptbBM28 -3.870e-01 1.224e+00 -0.316 0.7519
## sp_ptbBM29 9.068e-01 1.082e+00 0.838 0.4020
## sp_ptbBM30 -5.232e-01 1.228e+00 -0.426 0.6700
## sp_ptbBM31 6.980e-01 1.266e+00 0.551 0.5813
## sp_ptbBM32 -7.384e-01 1.568e+00 -0.471 0.6378
## sp_ptbBM33 -1.423e+00 1.493e+00 -0.953 0.3407
## sp_ptbBM34 -7.583e-01 1.593e+00 -0.476 0.6340
## sp_ptbBM35 -1.153e+00 1.830e+00 -0.630 0.5285
## sp_ptbBM36 -1.079e+00 1.866e+00 -0.579 0.5629
## sp_ptbBM37 -1.159e+00 1.633e+00 -0.710 0.4777
## sp_ptbBM38 2.210e-01 1.385e+00 0.160 0.8732
## sp_ptbBM39 -8.584e-01 1.402e+00 -0.612 0.5404
## sp_ptbBM40 1.136e+00 1.181e+00 0.962 0.3359
## sp_ptbBM41 -8.396e-01 1.295e+00 -0.648 0.5168
## sp_ptbBM42 -2.606e-02 1.289e+00 -0.020 0.9839
## sp_ptbBM43 1.044e-01 1.127e+00 0.093 0.9261
## sp_ptbBM44 3.191e-02 1.146e+00 0.028 0.9778
## sp_ptbBM45 5.143e-01 1.079e+00 0.477 0.6336
## sp_ptbBM46 4.816e-02 1.123e+00 0.043 0.9658
## sp_ptbBM47 -3.113e-02 1.181e+00 -0.026 0.9790
## sp_ptbBM48 1.395e-02 9.592e-01 0.015 0.9884
## sp_ptbBM49 2.430e-01 1.427e+00 0.170 0.8648
## sp_ptbBM50 -9.178e-01 1.200e+00 -0.765 0.4443
## sp_ptbBM51 1.035e+00 1.294e+00 0.800 0.4236
## sp_ptbBM52 -8.799e-01 1.229e+00 -0.716 0.4741
## sp_ptbBM53 5.530e-01 1.159e+00 0.477 0.6334
## sp_ptbBM54 -1.093e-01 1.138e+00 -0.096 0.9235
## sp_ptbBM55 -4.604e-01 1.232e+00 -0.374 0.7087
## sp_ptbBM56 9.365e-01 1.345e+00 0.696 0.4861
## sp_ptbBM57 7.976e-01 1.277e+00 0.624 0.5323
## sp_ptbBM58 5.718e-01 1.086e+00 0.527 0.5984
## sp_ptbBM59 -3.490e-02 1.221e+00 -0.029 0.9772
## sp_ptbBM60 2.655e-01 1.227e+00 0.216 0.8288
## sp_ptbBM61 1.734e-01 1.214e+00 0.143 0.8864
## sp_ptbBM62 -1.327e-01 1.233e+00 -0.108 0.9143
## sp_ptbBM63 -7.531e-01 1.552e+00 -0.485 0.6274
## sp_ptbBM64 -4.754e-01 1.676e+00 -0.284 0.7767
## sp_ptbBM65 -2.762e-01 1.380e+00 -0.200 0.8414
## sp_ptbBM66 -6.488e-01 1.104e+00 -0.588 0.5566
## sp_ptbBM67 1.470e+00 8.933e-01 1.646 0.0998 .
## sp_ptbBM68 6.558e-02 8.663e-01 0.076 0.9397
## sp_ptbBM69 1.229e+00 9.491e-01 1.295 0.1952
## sp_ptbBM70 -9.788e-01 1.315e+00 -0.744 0.4567
## sp_ptbBM71 1.067e+00 1.249e+00 0.854 0.3933
## sp_ptbBM72 -1.312e+00 1.312e+00 -1.000 0.3173
## sp_ptbBM73 1.094e-01 1.154e+00 0.095 0.9245
## sp_ptbBM74 4.537e-01 1.064e+00 0.426 0.6698
## sp_ptbBM75 3.540e-01 1.088e+00 0.325 0.7449
## sp_ptbBM76 7.591e-01 9.293e-01 0.817 0.4140
## sp_ptbBM77 -6.185e-02 1.186e+00 -0.052 0.9584
## sp_ptbBM78 1.106e+00 1.316e+00 0.840 0.4009
## sp_ptbBM79 -1.316e-02 1.172e+00 -0.011 0.9910
## sp_ptbBM80 5.194e-01 1.195e+00 0.435 0.6637
## sp_ptbBM81 -6.148e-01 1.150e+00 -0.535 0.5929
## sp_ptbBM82 -2.858e-01 1.061e+00 -0.269 0.7877
## sp_ptbBM83 -1.521e-01 1.114e+00 -0.137 0.8913
## sp_ptbBM84 6.460e-01 1.222e+00 0.529 0.5969
## sp_ptbBM85 -1.577e+00 1.428e+00 -1.105 0.2693
## sp_ptbBM86 9.463e-01 1.302e+00 0.727 0.4673
## sp_ptbBM87 -1.639e-01 1.065e+00 -0.154 0.8778
## sp_ptbBM88 1.374e+00 1.078e+00 1.275 0.2023
## sp_ptbBM89 -1.308e+00 1.142e+00 -1.146 0.2517
## sp_ptbBM90 1.393e+00 1.118e+00 1.246 0.2129
## sp_ptbBM91 2.003e+00 2.044e+00 0.980 0.3271
## sp_ptbBM92 2.735e+00 1.500e+00 1.823 0.0683 .
## sp_ptbBM93 1.142e+00 1.629e+00 0.701 0.4833
## sp_ptbBM94 1.855e+00 1.363e+00 1.361 0.1735
## sp_ptbBM95 1.701e+00 1.360e+00 1.251 0.2110
## sp_ptbBM96 2.962e+00 1.381e+00 2.144 0.0320 *
## sp_ptbBM97 1.752e+00 1.721e+00 1.018 0.3087
## sp_ptbBM98 1.769e+00 1.646e+00 1.075 0.2825
## sp_ptbBM99 2.603e+00 1.409e+00 1.847 0.0648 .
## sp_ptbBM100 2.406e+00 1.297e+00 1.855 0.0637 .
## sp_ptbBM101 2.288e+00 1.244e+00 1.839 0.0659 .
## sp_ptbBM102 1.968e+00 1.193e+00 1.650 0.0990 .
## sp_ptbBM103 1.781e+00 1.137e+00 1.566 0.1173
## sp_ptbBM104 2.172e+00 1.078e+00 2.016 0.0438 *
## sp_ptbBM105 1.047e+00 1.180e+00 0.887 0.3751
## sp_ptbBM106 1.587e+00 1.273e+00 1.246 0.2126
## sp_ptbBM107 3.905e-01 1.060e+00 0.368 0.7126
## sp_ptbBM108 1.574e+00 1.102e+00 1.429 0.1531
## sp_ptbBM109 1.133e+00 8.777e-01 1.291 0.1967
## sp_ptbBM110 1.061e+00 9.052e-01 1.172 0.2413
## sp_ptbBM111 1.779e+00 9.014e-01 1.974 0.0484 *
## sp_ptbBM112 -7.253e-01 1.017e+00 -0.713 0.4756
## sp_ptbBM113 1.956e+00 1.163e+00 1.682 0.0926 .
## sp_ptbBM114 9.573e-01 1.120e+00 0.855 0.3928
## sp_ptbBM115 -2.512e-02 1.137e+00 -0.022 0.9824
## sp_ptbBM116 2.074e+00 1.022e+00 2.029 0.0424 *
## sp_ptbBM117 -9.378e-01 1.308e+00 -0.717 0.4735
## sp_ptbBM118 1.805e+00 1.073e+00 1.682 0.0926 .
## sp_ptbBM119 2.527e-01 1.332e+00 0.190 0.8496
## sp_ptbBM120 1.523e+00 1.298e+00 1.174 0.2406
## sp_ptbBM121 2.957e-01 1.210e+00 0.244 0.8069
## sp_ptbBM122 2.439e+00 1.081e+00 2.256 0.0240 *
## sp_ptbBM123 -1.224e-01 8.909e-01 -0.137 0.8907
## sp_ptbBM124 2.149e+00 1.095e+00 1.963 0.0496 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17146.6) family taken to be 1)
##
## Null deviance: 1073.12 on 886 degrees of freedom
## Residual deviance: 927.39 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2888.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17147
## Std. Err.: 101690
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2630.514SA2m6a <- glm.nb(smptbBM ~ cb6.meanRH + sp_ptbBM,data=week); summary(SA2m6a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb6.meanRH + sp_ptbBM, data = week,
## init.theta = 17067.03987, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1893 -0.8318 -0.1696 0.5429 3.5139
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.322e+01 1.434e+01 0.922 0.35641
## cb6.meanRHv1.l1 -3.531e-01 3.273e-01 -1.079 0.28069
## cb6.meanRHv1.l2 -2.075e-01 2.090e-01 -0.993 0.32080
## cb6.meanRHv2.l1 -1.075e+00 1.182e+00 -0.909 0.36319
## cb6.meanRHv2.l2 3.727e-02 7.894e-01 0.047 0.96234
## cb6.meanRHv3.l1 -5.443e-01 5.374e-01 -1.013 0.31118
## cb6.meanRHv3.l2 4.040e-01 3.875e-01 1.043 0.29714
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.019e+06 2.868e+06 -0.704 0.48148
## sp_ptbBM5 9.276e+00 1.286e+01 0.721 0.47070
## sp_ptbBM6 -4.270e-02 2.213e+00 -0.019 0.98461
## sp_ptbBM7 2.120e+00 1.274e+00 1.664 0.09612 .
## sp_ptbBM8 -3.400e-01 1.191e+00 -0.286 0.77523
## sp_ptbBM9 1.252e+00 1.179e+00 1.062 0.28826
## sp_ptbBM10 -2.471e+00 1.356e+00 -1.822 0.06843 .
## sp_ptbBM11 1.531e+00 1.095e+00 1.398 0.16215
## sp_ptbBM12 -5.186e-01 1.063e+00 -0.488 0.62557
## sp_ptbBM13 1.185e+00 1.347e+00 0.880 0.37880
## sp_ptbBM14 -3.506e-02 1.359e+00 -0.026 0.97942
## sp_ptbBM15 2.898e-01 1.309e+00 0.221 0.82482
## sp_ptbBM16 -3.720e-01 1.498e+00 -0.248 0.80381
## sp_ptbBM17 1.592e-02 1.320e+00 0.012 0.99037
## sp_ptbBM18 3.850e-01 1.344e+00 0.287 0.77447
## sp_ptbBM19 3.461e-01 1.300e+00 0.266 0.79013
## sp_ptbBM20 5.118e-01 1.437e+00 0.356 0.72173
## sp_ptbBM21 9.967e-01 1.201e+00 0.830 0.40653
## sp_ptbBM22 7.815e-01 1.322e+00 0.591 0.55452
## sp_ptbBM23 -1.120e+00 1.733e+00 -0.646 0.51811
## sp_ptbBM24 -8.019e-01 2.229e+00 -0.360 0.71899
## sp_ptbBM25 -1.804e+00 2.650e+00 -0.681 0.49602
## sp_ptbBM26 -1.724e+00 2.896e+00 -0.595 0.55162
## sp_ptbBM27 -4.399e+00 3.241e+00 -1.358 0.17462
## sp_ptbBM28 -3.763e+00 3.290e+00 -1.144 0.25272
## sp_ptbBM29 -2.470e+00 3.275e+00 -0.754 0.45073
## sp_ptbBM30 -3.317e+00 3.200e+00 -1.037 0.29991
## sp_ptbBM31 -2.243e+00 3.221e+00 -0.696 0.48623
## sp_ptbBM32 -2.951e+00 3.227e+00 -0.915 0.36040
## sp_ptbBM33 -3.544e+00 3.215e+00 -1.102 0.27036
## sp_ptbBM34 -2.619e+00 3.080e+00 -0.850 0.39514
## sp_ptbBM35 -3.253e+00 3.448e+00 -0.943 0.34554
## sp_ptbBM36 -2.867e+00 3.228e+00 -0.888 0.37442
## sp_ptbBM37 -2.750e+00 2.697e+00 -1.019 0.30801
## sp_ptbBM38 -1.098e+00 2.425e+00 -0.453 0.65071
## sp_ptbBM39 -2.164e+00 2.160e+00 -1.002 0.31641
## sp_ptbBM40 3.826e-01 1.786e+00 0.214 0.83032
## sp_ptbBM41 -1.480e+00 1.792e+00 -0.826 0.40881
## sp_ptbBM42 -4.178e-01 1.485e+00 -0.281 0.77845
## sp_ptbBM43 -1.640e-02 1.487e+00 -0.011 0.99120
## sp_ptbBM44 -3.618e-01 1.584e+00 -0.228 0.81938
## sp_ptbBM45 3.111e-01 1.677e+00 0.186 0.85283
## sp_ptbBM46 -3.853e-01 1.681e+00 -0.229 0.81867
## sp_ptbBM47 -4.561e-01 1.866e+00 -0.244 0.80689
## sp_ptbBM48 -5.871e-01 1.643e+00 -0.357 0.72087
## sp_ptbBM49 -4.409e-01 1.970e+00 -0.224 0.82295
## sp_ptbBM50 -1.074e+00 1.556e+00 -0.690 0.49009
## sp_ptbBM51 1.040e+00 1.567e+00 0.664 0.50665
## sp_ptbBM52 -7.777e-01 1.408e+00 -0.552 0.58076
## sp_ptbBM53 5.517e-01 1.377e+00 0.401 0.68858
## sp_ptbBM54 3.996e-01 1.264e+00 0.316 0.75183
## sp_ptbBM55 -2.326e-01 1.238e+00 -0.188 0.85101
## sp_ptbBM56 1.475e+00 1.256e+00 1.175 0.24001
## sp_ptbBM57 1.295e+00 1.307e+00 0.991 0.32174
## sp_ptbBM58 6.961e-01 1.184e+00 0.588 0.55652
## sp_ptbBM59 -8.252e-02 1.376e+00 -0.060 0.95219
## sp_ptbBM60 -2.446e-01 1.463e+00 -0.167 0.86720
## sp_ptbBM61 -5.959e-02 1.439e+00 -0.041 0.96696
## sp_ptbBM62 -1.181e+00 1.455e+00 -0.811 0.41716
## sp_ptbBM63 -7.913e-01 1.751e+00 -0.452 0.65139
## sp_ptbBM64 -9.411e-01 2.024e+00 -0.465 0.64189
## sp_ptbBM65 -2.937e-01 1.604e+00 -0.183 0.85468
## sp_ptbBM66 -6.373e-01 1.386e+00 -0.460 0.64565
## sp_ptbBM67 1.653e+00 1.165e+00 1.418 0.15605
## sp_ptbBM68 1.840e-01 1.117e+00 0.165 0.86918
## sp_ptbBM69 1.619e+00 1.164e+00 1.390 0.16440
## sp_ptbBM70 -5.437e-02 1.390e+00 -0.039 0.96881
## sp_ptbBM71 1.800e+00 1.334e+00 1.349 0.17723
## sp_ptbBM72 -6.125e-01 1.331e+00 -0.460 0.64535
## sp_ptbBM73 6.208e-01 1.147e+00 0.541 0.58846
## sp_ptbBM74 7.104e-01 1.071e+00 0.664 0.50695
## sp_ptbBM75 5.841e-01 1.095e+00 0.533 0.59382
## sp_ptbBM76 7.045e-01 9.403e-01 0.749 0.45373
## sp_ptbBM77 5.353e-01 1.162e+00 0.461 0.64495
## sp_ptbBM78 1.614e+00 1.321e+00 1.221 0.22193
## sp_ptbBM79 2.837e-01 1.203e+00 0.236 0.81365
## sp_ptbBM80 7.936e-01 1.243e+00 0.638 0.52326
## sp_ptbBM81 -1.137e-01 1.251e+00 -0.091 0.92762
## sp_ptbBM82 -1.229e-01 1.123e+00 -0.109 0.91288
## sp_ptbBM83 1.583e-01 1.189e+00 0.133 0.89411
## sp_ptbBM84 6.627e-01 1.232e+00 0.538 0.59052
## sp_ptbBM85 -1.730e+00 1.547e+00 -1.118 0.26344
## sp_ptbBM86 9.198e-01 1.395e+00 0.659 0.50962
## sp_ptbBM87 -1.932e-01 1.237e+00 -0.156 0.87587
## sp_ptbBM88 1.695e+00 1.221e+00 1.388 0.16500
## sp_ptbBM89 -1.164e+00 1.375e+00 -0.847 0.39718
## sp_ptbBM90 2.266e+00 1.264e+00 1.792 0.07309 .
## sp_ptbBM91 2.311e+00 1.607e+00 1.438 0.15057
## sp_ptbBM92 2.651e+00 1.230e+00 2.156 0.03105 *
## sp_ptbBM93 5.767e-01 1.291e+00 0.447 0.65505
## sp_ptbBM94 9.585e-01 1.081e+00 0.887 0.37513
## sp_ptbBM95 5.947e-01 9.941e-01 0.598 0.54970
## sp_ptbBM96 1.447e+00 1.070e+00 1.352 0.17634
## sp_ptbBM97 4.177e-01 1.135e+00 0.368 0.71289
## sp_ptbBM98 2.354e+00 1.514e+00 1.555 0.11990
## sp_ptbBM99 2.285e+00 1.127e+00 2.027 0.04269 *
## sp_ptbBM100 2.400e+00 1.088e+00 2.206 0.02739 *
## sp_ptbBM101 1.724e+00 9.718e-01 1.774 0.07613 .
## sp_ptbBM102 1.114e+00 9.309e-01 1.197 0.23139
## sp_ptbBM103 8.615e-01 9.362e-01 0.920 0.35747
## sp_ptbBM104 1.753e+00 9.875e-01 1.775 0.07592 .
## sp_ptbBM105 1.569e+00 1.216e+00 1.290 0.19696
## sp_ptbBM106 8.454e-01 1.001e+00 0.845 0.39834
## sp_ptbBM107 1.547e-02 1.053e+00 0.015 0.98828
## sp_ptbBM108 1.452e+00 9.930e-01 1.462 0.14367
## sp_ptbBM109 1.084e+00 9.254e-01 1.171 0.24155
## sp_ptbBM110 4.492e-01 8.735e-01 0.514 0.60709
## sp_ptbBM111 1.698e+00 1.142e+00 1.487 0.13692
## sp_ptbBM112 -6.338e-01 1.185e+00 -0.535 0.59266
## sp_ptbBM113 2.259e+00 1.161e+00 1.946 0.05162 .
## sp_ptbBM114 9.062e-01 1.098e+00 0.825 0.40919
## sp_ptbBM115 7.514e-02 1.148e+00 0.065 0.94784
## sp_ptbBM116 2.341e+00 1.101e+00 2.125 0.03357 *
## sp_ptbBM117 -6.456e-01 1.332e+00 -0.485 0.62784
## sp_ptbBM118 2.749e+00 1.228e+00 2.239 0.02514 *
## sp_ptbBM119 9.527e-01 1.423e+00 0.670 0.50313
## sp_ptbBM120 2.473e+00 1.292e+00 1.914 0.05564 .
## sp_ptbBM121 6.759e-01 1.136e+00 0.595 0.55173
## sp_ptbBM122 2.785e+00 1.053e+00 2.644 0.00819 **
## sp_ptbBM123 -1.935e-01 9.057e-01 -0.214 0.83083
## sp_ptbBM124 1.992e+00 1.090e+00 1.827 0.06764 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17067.04) family taken to be 1)
##
## Null deviance: 1073.12 on 886 degrees of freedom
## Residual deviance: 926.37 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2887.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17067
## Std. Err.: 100044
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2629.492SA2m7a <- glm.nb(smptbBM ~ cb7.maxRH + sp_ptbBM,data=week); summary(SA2m7a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb7.maxRH + sp_ptbBM, data = week,
## init.theta = 17158.53339, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1619 -0.8170 -0.1844 0.5355 3.3494
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 3.825e+00 1.399e+01 0.273 0.7846
## cb7.maxRHv1.l1 -2.678e-01 3.385e-01 -0.791 0.4289
## cb7.maxRHv1.l2 -2.562e-02 2.333e-01 -0.110 0.9126
## cb7.maxRHv2.l1 -1.244e-01 1.105e+00 -0.113 0.9104
## cb7.maxRHv2.l2 -6.610e-02 7.754e-01 -0.085 0.9321
## cb7.maxRHv3.l1 1.198e-01 3.285e-01 0.365 0.7153
## cb7.maxRHv3.l2 2.017e-01 2.168e-01 0.930 0.3522
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.128e+06 2.871e+06 -0.741 0.4585
## sp_ptbBM5 1.035e+01 1.316e+01 0.786 0.4316
## sp_ptbBM6 9.651e-01 2.370e+00 0.407 0.6838
## sp_ptbBM7 2.780e+00 1.584e+00 1.755 0.0793 .
## sp_ptbBM8 7.044e-01 1.356e+00 0.519 0.6035
## sp_ptbBM9 2.341e+00 1.220e+00 1.918 0.0551 .
## sp_ptbBM10 -1.815e+00 1.319e+00 -1.376 0.1687
## sp_ptbBM11 2.211e+00 9.793e-01 2.257 0.0240 *
## sp_ptbBM12 -2.688e-01 9.576e-01 -0.281 0.7789
## sp_ptbBM13 2.149e+00 1.213e+00 1.772 0.0764 .
## sp_ptbBM14 4.869e-01 1.003e+00 0.486 0.6272
## sp_ptbBM15 5.584e-01 9.027e-01 0.619 0.5362
## sp_ptbBM16 9.567e-01 9.819e-01 0.974 0.3299
## sp_ptbBM17 3.323e-01 9.428e-01 0.352 0.7245
## sp_ptbBM18 6.789e-02 9.590e-01 0.071 0.9436
## sp_ptbBM19 9.319e-02 9.904e-01 0.094 0.9250
## sp_ptbBM20 1.179e+00 1.395e+00 0.845 0.3979
## sp_ptbBM21 1.008e+00 9.418e-01 1.071 0.2844
## sp_ptbBM22 1.244e+00 9.503e-01 1.309 0.1906
## sp_ptbBM23 -3.688e-01 1.055e+00 -0.349 0.7268
## sp_ptbBM24 9.571e-01 9.150e-01 1.046 0.2956
## sp_ptbBM25 -1.084e-01 1.128e+00 -0.096 0.9234
## sp_ptbBM26 3.789e-01 1.324e+00 0.286 0.7748
## sp_ptbBM27 -5.618e-01 1.660e+00 -0.338 0.7350
## sp_ptbBM28 -1.066e+00 1.498e+00 -0.712 0.4766
## sp_ptbBM29 -4.480e-01 2.466e+00 -0.182 0.8559
## sp_ptbBM30 -2.052e+00 2.849e+00 -0.720 0.4714
## sp_ptbBM31 -2.155e+00 4.080e+00 -0.528 0.5974
## sp_ptbBM32 -3.509e+00 5.315e+00 -0.660 0.5092
## sp_ptbBM33 -3.878e+00 6.534e+00 -0.594 0.5528
## sp_ptbBM34 -1.758e+00 7.125e+00 -0.247 0.8051
## sp_ptbBM35 -2.330e+00 8.526e+00 -0.273 0.7847
## sp_ptbBM36 -1.765e+00 7.346e+00 -0.240 0.8101
## sp_ptbBM37 -1.717e+00 6.744e+00 -0.255 0.7990
## sp_ptbBM38 -5.712e-01 6.021e+00 -0.095 0.9244
## sp_ptbBM39 -1.648e+00 4.937e+00 -0.334 0.7386
## sp_ptbBM40 8.077e-01 3.871e+00 0.209 0.8347
## sp_ptbBM41 -5.186e-01 3.422e+00 -0.152 0.8795
## sp_ptbBM42 6.324e-01 2.394e+00 0.264 0.7917
## sp_ptbBM43 9.099e-01 2.496e+00 0.364 0.7155
## sp_ptbBM44 5.956e-01 2.778e+00 0.214 0.8302
## sp_ptbBM45 1.022e+00 2.966e+00 0.345 0.7304
## sp_ptbBM46 5.142e-02 3.047e+00 0.017 0.9865
## sp_ptbBM47 -1.525e-02 3.468e+00 -0.004 0.9965
## sp_ptbBM48 1.440e-01 3.178e+00 0.045 0.9639
## sp_ptbBM49 7.561e-01 3.121e+00 0.242 0.8086
## sp_ptbBM50 2.571e-03 2.553e+00 0.001 0.9992
## sp_ptbBM51 2.628e+00 2.394e+00 1.098 0.2721
## sp_ptbBM52 5.124e-01 2.141e+00 0.239 0.8108
## sp_ptbBM53 1.784e+00 1.926e+00 0.926 0.3543
## sp_ptbBM54 1.658e+00 1.658e+00 1.000 0.3173
## sp_ptbBM55 1.831e+00 1.624e+00 1.128 0.2594
## sp_ptbBM56 2.172e+00 1.477e+00 1.470 0.1416
## sp_ptbBM57 2.775e+00 1.560e+00 1.779 0.0753 .
## sp_ptbBM58 1.827e+00 1.526e+00 1.198 0.2311
## sp_ptbBM59 1.298e+00 1.677e+00 0.774 0.4390
## sp_ptbBM60 1.061e+00 1.951e+00 0.544 0.5863
## sp_ptbBM61 1.088e+00 1.955e+00 0.557 0.5778
## sp_ptbBM62 7.409e-01 1.952e+00 0.380 0.7043
## sp_ptbBM63 8.693e-01 2.147e+00 0.405 0.6856
## sp_ptbBM64 1.286e+00 2.500e+00 0.514 0.6070
## sp_ptbBM65 1.723e+00 2.117e+00 0.814 0.4157
## sp_ptbBM66 9.272e-01 1.974e+00 0.470 0.6385
## sp_ptbBM67 3.150e+00 1.665e+00 1.892 0.0585 .
## sp_ptbBM68 1.786e+00 1.587e+00 1.125 0.2604
## sp_ptbBM69 3.485e+00 1.441e+00 2.418 0.0156 *
## sp_ptbBM70 9.634e-01 1.494e+00 0.645 0.5190
## sp_ptbBM71 2.742e+00 1.466e+00 1.871 0.0613 .
## sp_ptbBM72 3.441e-01 1.510e+00 0.228 0.8198
## sp_ptbBM73 1.383e+00 1.353e+00 1.022 0.3068
## sp_ptbBM74 1.406e+00 1.400e+00 1.004 0.3155
## sp_ptbBM75 1.375e+00 1.341e+00 1.026 0.3049
## sp_ptbBM76 1.794e+00 1.348e+00 1.331 0.1831
## sp_ptbBM77 1.473e+00 1.317e+00 1.118 0.2634
## sp_ptbBM78 2.961e+00 1.371e+00 2.159 0.0308 *
## sp_ptbBM79 1.558e+00 1.314e+00 1.186 0.2356
## sp_ptbBM80 1.891e+00 1.325e+00 1.427 0.1536
## sp_ptbBM81 5.147e-01 1.474e+00 0.349 0.7269
## sp_ptbBM82 5.527e-01 1.373e+00 0.403 0.6873
## sp_ptbBM83 1.092e+00 1.238e+00 0.882 0.3777
## sp_ptbBM84 1.844e+00 1.385e+00 1.332 0.1829
## sp_ptbBM85 7.517e-02 1.739e+00 0.043 0.9655
## sp_ptbBM86 2.715e+00 1.510e+00 1.798 0.0722 .
## sp_ptbBM87 1.109e+00 1.512e+00 0.734 0.4632
## sp_ptbBM88 2.612e+00 1.437e+00 1.818 0.0691 .
## sp_ptbBM89 -1.875e-01 1.646e+00 -0.114 0.9093
## sp_ptbBM90 2.876e+00 1.418e+00 2.029 0.0425 *
## sp_ptbBM91 1.470e+00 1.312e+00 1.121 0.2625
## sp_ptbBM92 2.221e+00 1.184e+00 1.875 0.0607 .
## sp_ptbBM93 1.720e-01 1.247e+00 0.138 0.8903
## sp_ptbBM94 6.312e-01 1.152e+00 0.548 0.5838
## sp_ptbBM95 3.523e-01 1.109e+00 0.318 0.7506
## sp_ptbBM96 1.173e+00 1.050e+00 1.117 0.2638
## sp_ptbBM97 5.261e-01 1.086e+00 0.485 0.6280
## sp_ptbBM98 1.489e+00 1.342e+00 1.109 0.2672
## sp_ptbBM99 2.039e+00 1.030e+00 1.979 0.0478 *
## sp_ptbBM100 1.786e+00 9.639e-01 1.853 0.0639 .
## sp_ptbBM101 1.213e+00 8.996e-01 1.348 0.1777
## sp_ptbBM102 1.063e+00 9.421e-01 1.128 0.2593
## sp_ptbBM103 8.971e-01 9.111e-01 0.985 0.3248
## sp_ptbBM104 1.771e+00 9.334e-01 1.898 0.0578 .
## sp_ptbBM105 1.675e+00 1.168e+00 1.434 0.1516
## sp_ptbBM106 2.682e-01 1.295e+00 0.207 0.8360
## sp_ptbBM107 2.977e-01 1.369e+00 0.217 0.8279
## sp_ptbBM108 7.931e-01 1.125e+00 0.705 0.4806
## sp_ptbBM109 6.152e-01 1.278e+00 0.482 0.6302
## sp_ptbBM110 1.639e-02 1.246e+00 0.013 0.9895
## sp_ptbBM111 2.427e+00 1.600e+00 1.517 0.1294
## sp_ptbBM112 -5.123e-01 1.435e+00 -0.357 0.7211
## sp_ptbBM113 2.505e+00 1.085e+00 2.310 0.0209 *
## sp_ptbBM114 5.899e-01 1.069e+00 0.552 0.5811
## sp_ptbBM115 4.337e-02 1.057e+00 0.041 0.9673
## sp_ptbBM116 1.866e+00 1.046e+00 1.784 0.0745 .
## sp_ptbBM117 -1.789e+00 1.312e+00 -1.364 0.1727
## sp_ptbBM118 1.553e+00 1.399e+00 1.110 0.2671
## sp_ptbBM119 -1.140e+00 1.450e+00 -0.786 0.4316
## sp_ptbBM120 9.145e-01 1.189e+00 0.769 0.4416
## sp_ptbBM121 -4.970e-01 1.066e+00 -0.466 0.6410
## sp_ptbBM122 1.279e+00 1.088e+00 1.175 0.2399
## sp_ptbBM123 -1.407e+00 1.005e+00 -1.400 0.1615
## sp_ptbBM124 1.657e+00 1.157e+00 1.432 0.1521
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17158.53) family taken to be 1)
##
## Null deviance: 1073.12 on 886 degrees of freedom
## Residual deviance: 927.74 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2888.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17159
## Std. Err.: 101850
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2630.858SA2m9a <- glm.nb(smptbBM ~ cb9.minT + sp_ptbBM,data=week); summary(SA2m9a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb9.minT + sp_ptbBM, data = week,
## init.theta = 17244.58218, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2806 -0.8281 -0.1674 0.5151 3.4436
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.655e+00 1.181e+01 -0.563 0.57320
## cb9.minTv1.l1 1.717e-01 2.921e-01 0.588 0.55677
## cb9.minTv1.l2 -3.204e-01 2.064e-01 -1.552 0.12061
## cb9.minTv2.l1 1.055e+00 1.003e+00 1.052 0.29277
## cb9.minTv2.l2 -1.871e+00 7.590e-01 -2.465 0.01368 *
## cb9.minTv3.l1 9.312e-01 5.899e-01 1.579 0.11440
## cb9.minTv3.l2 -2.211e-01 4.271e-01 -0.518 0.60467
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.192e+06 2.875e+06 -0.762 0.44583
## sp_ptbBM5 9.556e+00 1.293e+01 0.739 0.45989
## sp_ptbBM6 -1.237e+00 2.136e+00 -0.579 0.56256
## sp_ptbBM7 9.743e-01 1.303e+00 0.748 0.45459
## sp_ptbBM8 -1.159e+00 1.262e+00 -0.918 0.35854
## sp_ptbBM9 1.203e+00 1.206e+00 0.997 0.31860
## sp_ptbBM10 -2.838e+00 1.528e+00 -1.857 0.06334 .
## sp_ptbBM11 1.716e+00 1.066e+00 1.609 0.10760
## sp_ptbBM12 -8.127e-01 1.139e+00 -0.713 0.47564
## sp_ptbBM13 2.837e-01 1.085e+00 0.262 0.79368
## sp_ptbBM14 -5.042e-01 1.161e+00 -0.434 0.66396
## sp_ptbBM15 1.789e-01 1.094e+00 0.163 0.87016
## sp_ptbBM16 -1.238e-01 1.301e+00 -0.095 0.92421
## sp_ptbBM17 -2.990e-01 1.183e+00 -0.253 0.80045
## sp_ptbBM18 -2.622e-01 1.271e+00 -0.206 0.83658
## sp_ptbBM19 -9.826e-01 1.317e+00 -0.746 0.45573
## sp_ptbBM20 -7.331e-01 1.312e+00 -0.559 0.57637
## sp_ptbBM21 1.608e-01 1.139e+00 0.141 0.88771
## sp_ptbBM22 7.798e-02 1.117e+00 0.070 0.94434
## sp_ptbBM23 -1.166e-01 1.116e+00 -0.104 0.91681
## sp_ptbBM24 4.975e-01 1.004e+00 0.495 0.62041
## sp_ptbBM25 5.006e-01 8.988e-01 0.557 0.57754
## sp_ptbBM26 7.711e-01 9.535e-01 0.809 0.41865
## sp_ptbBM27 -1.738e+00 1.249e+00 -1.391 0.16418
## sp_ptbBM28 6.916e-01 1.095e+00 0.632 0.52753
## sp_ptbBM29 1.133e+00 9.108e-01 1.244 0.21354
## sp_ptbBM30 3.826e-01 1.019e+00 0.376 0.70725
## sp_ptbBM31 1.562e+00 1.079e+00 1.448 0.14770
## sp_ptbBM32 8.492e-01 1.085e+00 0.783 0.43374
## sp_ptbBM33 7.951e-01 1.216e+00 0.654 0.51320
## sp_ptbBM34 2.631e+00 1.621e+00 1.622 0.10471
## sp_ptbBM35 3.228e+00 1.655e+00 1.950 0.05115 .
## sp_ptbBM36 3.409e+00 1.896e+00 1.798 0.07216 .
## sp_ptbBM37 2.641e+00 2.287e+00 1.155 0.24826
## sp_ptbBM38 2.817e+00 2.161e+00 1.304 0.19240
## sp_ptbBM39 5.310e-01 2.047e+00 0.259 0.79538
## sp_ptbBM40 1.662e+00 2.038e+00 0.816 0.41469
## sp_ptbBM41 -6.515e-01 1.571e+00 -0.415 0.67843
## sp_ptbBM42 7.416e-01 1.393e+00 0.533 0.59434
## sp_ptbBM43 3.800e-01 1.305e+00 0.291 0.77092
## sp_ptbBM44 4.347e-01 9.476e-01 0.459 0.64645
## sp_ptbBM45 1.429e+00 9.538e-01 1.498 0.13420
## sp_ptbBM46 8.901e-01 1.002e+00 0.888 0.37436
## sp_ptbBM47 1.304e+00 9.612e-01 1.356 0.17497
## sp_ptbBM48 3.732e-01 1.152e+00 0.324 0.74602
## sp_ptbBM49 2.506e+00 1.049e+00 2.390 0.01684 *
## sp_ptbBM50 8.726e-01 1.220e+00 0.715 0.47453
## sp_ptbBM51 3.290e+00 1.145e+00 2.873 0.00407 **
## sp_ptbBM52 1.112e+00 1.442e+00 0.771 0.44045
## sp_ptbBM53 2.467e+00 1.411e+00 1.749 0.08025 .
## sp_ptbBM54 1.032e+00 1.449e+00 0.712 0.47633
## sp_ptbBM55 -2.079e-01 1.328e+00 -0.157 0.87555
## sp_ptbBM56 1.319e+00 1.386e+00 0.952 0.34128
## sp_ptbBM57 1.307e+00 1.159e+00 1.127 0.25956
## sp_ptbBM58 9.991e-01 1.151e+00 0.868 0.38521
## sp_ptbBM59 2.573e-01 9.622e-01 0.267 0.78919
## sp_ptbBM60 8.643e-01 9.156e-01 0.944 0.34517
## sp_ptbBM61 5.587e-01 8.883e-01 0.629 0.52941
## sp_ptbBM62 -1.903e-01 9.764e-01 -0.195 0.84547
## sp_ptbBM63 1.250e-01 1.040e+00 0.120 0.90433
## sp_ptbBM64 -4.573e-01 1.486e+00 -0.308 0.75830
## sp_ptbBM65 4.506e-01 1.574e+00 0.286 0.77461
## sp_ptbBM66 -1.368e+00 1.532e+00 -0.893 0.37172
## sp_ptbBM67 1.182e-01 1.413e+00 0.084 0.93334
## sp_ptbBM68 -1.629e+00 1.493e+00 -1.090 0.27551
## sp_ptbBM69 -1.079e+00 1.504e+00 -0.717 0.47317
## sp_ptbBM70 -2.806e+00 1.576e+00 -1.780 0.07509 .
## sp_ptbBM71 4.137e-02 1.221e+00 0.034 0.97296
## sp_ptbBM72 -1.140e+00 1.235e+00 -0.923 0.35621
## sp_ptbBM73 6.324e-01 1.080e+00 0.586 0.55813
## sp_ptbBM74 5.359e-01 1.006e+00 0.533 0.59412
## sp_ptbBM75 8.929e-01 1.055e+00 0.846 0.39758
## sp_ptbBM76 3.291e-01 1.009e+00 0.326 0.74427
## sp_ptbBM77 -5.638e-02 1.056e+00 -0.053 0.95741
## sp_ptbBM78 7.911e-01 1.123e+00 0.705 0.48109
## sp_ptbBM79 -2.908e-01 1.157e+00 -0.251 0.80160
## sp_ptbBM80 9.298e-01 1.096e+00 0.848 0.39619
## sp_ptbBM81 -6.246e-01 1.168e+00 -0.535 0.59279
## sp_ptbBM82 -4.184e-01 1.183e+00 -0.354 0.72360
## sp_ptbBM83 -2.222e-01 1.206e+00 -0.184 0.85377
## sp_ptbBM84 4.044e-01 1.329e+00 0.304 0.76094
## sp_ptbBM85 -2.666e+00 1.659e+00 -1.607 0.10804
## sp_ptbBM86 7.108e-01 1.626e+00 0.437 0.66194
## sp_ptbBM87 -1.082e+00 1.627e+00 -0.665 0.50606
## sp_ptbBM88 -5.881e-02 1.562e+00 -0.038 0.96997
## sp_ptbBM89 -3.142e+00 1.666e+00 -1.886 0.05934 .
## sp_ptbBM90 -1.254e+00 1.683e+00 -0.745 0.45632
## sp_ptbBM91 -6.237e-01 1.338e+00 -0.466 0.64101
## sp_ptbBM92 5.943e-01 1.256e+00 0.473 0.63604
## sp_ptbBM93 -1.049e+00 1.488e+00 -0.705 0.48091
## sp_ptbBM94 2.211e-01 1.484e+00 0.149 0.88154
## sp_ptbBM95 -1.067e+00 1.463e+00 -0.729 0.46586
## sp_ptbBM96 1.020e-01 1.418e+00 0.072 0.94265
## sp_ptbBM97 -2.585e+00 1.635e+00 -1.581 0.11382
## sp_ptbBM98 -1.779e+00 1.688e+00 -1.054 0.29185
## sp_ptbBM99 -1.191e+00 1.645e+00 -0.724 0.46904
## sp_ptbBM100 -1.260e-01 1.658e+00 -0.076 0.93944
## sp_ptbBM101 -7.338e-01 1.817e+00 -0.404 0.68624
## sp_ptbBM102 -1.010e+00 1.688e+00 -0.598 0.54977
## sp_ptbBM103 -1.443e+00 1.874e+00 -0.770 0.44118
## sp_ptbBM104 -8.012e-01 1.814e+00 -0.442 0.65869
## sp_ptbBM105 -3.293e+00 2.800e+00 -1.176 0.23962
## sp_ptbBM106 -5.380e+00 3.910e+00 -1.376 0.16883
## sp_ptbBM107 -6.159e+00 4.737e+00 -1.300 0.19348
## sp_ptbBM108 -5.147e+00 4.158e+00 -1.238 0.21576
## sp_ptbBM109 -6.187e+00 4.475e+00 -1.383 0.16679
## sp_ptbBM110 -6.522e+00 4.291e+00 -1.520 0.12855
## sp_ptbBM111 -6.321e+00 4.814e+00 -1.313 0.18917
## sp_ptbBM112 -6.662e+00 4.074e+00 -1.635 0.10205
## sp_ptbBM113 -2.352e+00 2.668e+00 -0.882 0.37799
## sp_ptbBM114 -1.463e+00 1.782e+00 -0.821 0.41163
## sp_ptbBM115 -2.146e+00 1.667e+00 -1.288 0.19785
## sp_ptbBM116 2.054e-01 1.623e+00 0.127 0.89928
## sp_ptbBM117 -2.752e+00 1.449e+00 -1.899 0.05752 .
## sp_ptbBM118 4.002e-01 1.189e+00 0.337 0.73633
## sp_ptbBM119 -1.210e+00 1.185e+00 -1.021 0.30704
## sp_ptbBM120 3.803e-01 1.100e+00 0.346 0.72967
## sp_ptbBM121 -4.307e-01 9.751e-01 -0.442 0.65867
## sp_ptbBM122 1.917e+00 9.272e-01 2.068 0.03867 *
## sp_ptbBM123 -3.913e-01 9.475e-01 -0.413 0.67958
## sp_ptbBM124 2.574e+00 1.134e+00 2.270 0.02319 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17244.58) family taken to be 1)
##
## Null deviance: 1073.12 on 886 degrees of freedom
## Residual deviance: 919.41 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2880.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17245
## Std. Err.: 95888
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2622.532SA2m10a <- glm.nb(smptbBM ~ cb10.aveT + sp_ptbBM,data=week); summary(SA2m10a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb10.aveT + sp_ptbBM, data = week,
## init.theta = 17620.30367, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1542 -0.8562 -0.1691 0.5245 3.4475
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.545e+01 1.767e+01 -1.441 0.149693
## cb10.aveTv1.l1 4.852e-01 3.776e-01 1.285 0.198871
## cb10.aveTv1.l2 -5.254e-01 2.796e-01 -1.879 0.060200 .
## cb10.aveTv2.l1 2.360e+00 1.404e+00 1.681 0.092849 .
## cb10.aveTv2.l2 -2.002e+00 1.098e+00 -1.823 0.068297 .
## cb10.aveTv3.l1 1.300e+00 5.970e-01 2.178 0.029405 *
## cb10.aveTv3.l2 -3.019e-01 4.457e-01 -0.677 0.498201
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.112e+06 2.877e+06 -0.734 0.462712
## sp_ptbBM5 1.166e+01 1.301e+01 0.896 0.370025
## sp_ptbBM6 1.050e+00 2.199e+00 0.477 0.633142
## sp_ptbBM7 3.582e+00 1.612e+00 2.223 0.026238 *
## sp_ptbBM8 9.892e-01 1.296e+00 0.764 0.445161
## sp_ptbBM9 2.823e+00 1.188e+00 2.375 0.017527 *
## sp_ptbBM10 -1.069e+00 1.307e+00 -0.818 0.413474
## sp_ptbBM11 3.461e+00 1.266e+00 2.734 0.006263 **
## sp_ptbBM12 1.134e+00 1.180e+00 0.961 0.336618
## sp_ptbBM13 2.533e+00 1.141e+00 2.219 0.026476 *
## sp_ptbBM14 1.355e+00 1.296e+00 1.045 0.295957
## sp_ptbBM15 1.826e+00 1.286e+00 1.420 0.155565
## sp_ptbBM16 1.376e+00 1.087e+00 1.267 0.205271
## sp_ptbBM17 1.892e+00 1.190e+00 1.591 0.111722
## sp_ptbBM18 1.821e+00 1.147e+00 1.587 0.112410
## sp_ptbBM19 2.006e+00 1.195e+00 1.678 0.093287 .
## sp_ptbBM20 1.799e+00 1.273e+00 1.413 0.157581
## sp_ptbBM21 2.261e+00 1.385e+00 1.633 0.102412
## sp_ptbBM22 2.282e+00 1.269e+00 1.799 0.072015 .
## sp_ptbBM23 1.693e+00 1.269e+00 1.334 0.182146
## sp_ptbBM24 2.709e+00 1.312e+00 2.066 0.038850 *
## sp_ptbBM25 2.704e+00 1.460e+00 1.852 0.063991 .
## sp_ptbBM26 3.347e+00 1.404e+00 2.383 0.017175 *
## sp_ptbBM27 1.025e+00 1.557e+00 0.659 0.510185
## sp_ptbBM28 2.127e+00 1.635e+00 1.301 0.193377
## sp_ptbBM29 3.261e+00 1.367e+00 2.385 0.017073 *
## sp_ptbBM30 2.271e+00 1.252e+00 1.813 0.069791 .
## sp_ptbBM31 3.109e+00 1.248e+00 2.492 0.012689 *
## sp_ptbBM32 2.136e+00 1.212e+00 1.762 0.078020 .
## sp_ptbBM33 1.295e+00 1.345e+00 0.963 0.335551
## sp_ptbBM34 3.324e+00 1.365e+00 2.436 0.014870 *
## sp_ptbBM35 2.989e+00 1.534e+00 1.948 0.051450 .
## sp_ptbBM36 2.850e+00 1.399e+00 2.038 0.041593 *
## sp_ptbBM37 2.916e+00 1.606e+00 1.815 0.069501 .
## sp_ptbBM38 3.636e+00 1.365e+00 2.665 0.007706 **
## sp_ptbBM39 2.072e+00 1.488e+00 1.393 0.163749
## sp_ptbBM40 4.128e+00 1.542e+00 2.677 0.007424 **
## sp_ptbBM41 1.909e+00 1.383e+00 1.380 0.167553
## sp_ptbBM42 3.159e+00 1.628e+00 1.940 0.052342 .
## sp_ptbBM43 2.887e+00 1.367e+00 2.111 0.034730 *
## sp_ptbBM44 2.517e+00 1.211e+00 2.078 0.037741 *
## sp_ptbBM45 3.335e+00 1.241e+00 2.688 0.007196 **
## sp_ptbBM46 2.413e+00 1.318e+00 1.831 0.067083 .
## sp_ptbBM47 3.244e+00 1.330e+00 2.439 0.014745 *
## sp_ptbBM48 2.831e+00 1.473e+00 1.922 0.054562 .
## sp_ptbBM49 4.394e+00 1.524e+00 2.882 0.003948 **
## sp_ptbBM50 3.456e+00 1.776e+00 1.946 0.051674 .
## sp_ptbBM51 5.277e+00 1.544e+00 3.418 0.000632 ***
## sp_ptbBM52 2.640e+00 1.657e+00 1.593 0.111163
## sp_ptbBM53 3.424e+00 1.654e+00 2.070 0.038445 *
## sp_ptbBM54 2.487e+00 1.667e+00 1.492 0.135726
## sp_ptbBM55 1.805e+00 1.638e+00 1.103 0.270220
## sp_ptbBM56 3.793e+00 1.755e+00 2.161 0.030683 *
## sp_ptbBM57 3.990e+00 1.681e+00 2.374 0.017585 *
## sp_ptbBM58 2.635e+00 1.248e+00 2.112 0.034709 *
## sp_ptbBM59 1.898e+00 1.140e+00 1.665 0.095967 .
## sp_ptbBM60 1.323e+00 1.037e+00 1.277 0.201762
## sp_ptbBM61 1.230e+00 9.080e-01 1.355 0.175530
## sp_ptbBM62 2.172e-01 1.027e+00 0.212 0.832430
## sp_ptbBM63 8.572e-01 1.108e+00 0.774 0.439055
## sp_ptbBM64 3.134e-01 1.285e+00 0.244 0.807361
## sp_ptbBM65 1.364e+00 9.735e-01 1.401 0.161242
## sp_ptbBM66 5.860e-01 9.607e-01 0.610 0.541859
## sp_ptbBM67 2.686e+00 9.317e-01 2.883 0.003943 **
## sp_ptbBM68 1.045e+00 9.604e-01 1.088 0.276431
## sp_ptbBM69 2.329e+00 1.169e+00 1.992 0.046323 *
## sp_ptbBM70 1.134e+00 1.533e+00 0.740 0.459372
## sp_ptbBM71 3.188e+00 1.704e+00 1.871 0.061366 .
## sp_ptbBM72 1.790e+00 1.713e+00 1.045 0.295932
## sp_ptbBM73 2.785e+00 1.423e+00 1.957 0.050333 .
## sp_ptbBM74 2.781e+00 1.353e+00 2.055 0.039836 *
## sp_ptbBM75 2.461e+00 1.370e+00 1.796 0.072488 .
## sp_ptbBM76 2.088e+00 1.355e+00 1.541 0.123402
## sp_ptbBM77 1.769e+00 1.262e+00 1.402 0.160927
## sp_ptbBM78 2.910e+00 1.537e+00 1.893 0.058311 .
## sp_ptbBM79 1.528e+00 1.208e+00 1.265 0.206042
## sp_ptbBM80 2.638e+00 1.163e+00 2.268 0.023315 *
## sp_ptbBM81 1.446e+00 1.165e+00 1.241 0.214605
## sp_ptbBM82 1.640e+00 1.203e+00 1.364 0.172577
## sp_ptbBM83 1.564e+00 1.169e+00 1.337 0.181067
## sp_ptbBM84 2.081e+00 1.252e+00 1.663 0.096299 .
## sp_ptbBM85 -3.791e-01 1.388e+00 -0.273 0.784737
## sp_ptbBM86 2.647e+00 1.274e+00 2.077 0.037761 *
## sp_ptbBM87 1.314e+00 1.272e+00 1.033 0.301498
## sp_ptbBM88 2.998e+00 1.354e+00 2.214 0.026862 *
## sp_ptbBM89 6.403e-01 1.434e+00 0.447 0.655191
## sp_ptbBM90 2.003e+00 1.467e+00 1.366 0.172003
## sp_ptbBM91 4.955e+00 1.998e+00 2.480 0.013128 *
## sp_ptbBM92 4.360e+00 1.628e+00 2.677 0.007419 **
## sp_ptbBM93 2.279e+00 1.421e+00 1.604 0.108806
## sp_ptbBM94 2.856e+00 1.395e+00 2.048 0.040556 *
## sp_ptbBM95 2.102e+00 1.347e+00 1.560 0.118804
## sp_ptbBM96 3.151e+00 1.213e+00 2.599 0.009360 **
## sp_ptbBM97 5.768e-01 1.659e+00 0.348 0.728078
## sp_ptbBM98 2.863e+00 1.449e+00 1.976 0.048139 *
## sp_ptbBM99 2.528e+00 1.343e+00 1.883 0.059727 .
## sp_ptbBM100 3.334e+00 1.320e+00 2.527 0.011513 *
## sp_ptbBM101 2.137e+00 1.029e+00 2.077 0.037766 *
## sp_ptbBM102 2.331e+00 1.007e+00 2.316 0.020547 *
## sp_ptbBM103 1.837e+00 9.489e-01 1.936 0.052870 .
## sp_ptbBM104 1.609e+00 8.625e-01 1.866 0.062040 .
## sp_ptbBM105 1.132e+00 1.065e+00 1.062 0.288196
## sp_ptbBM106 1.610e-01 1.124e+00 0.143 0.886107
## sp_ptbBM107 -2.068e+00 1.700e+00 -1.217 0.223759
## sp_ptbBM108 3.573e-02 1.210e+00 0.030 0.976451
## sp_ptbBM109 -7.995e-01 1.507e+00 -0.531 0.595643
## sp_ptbBM110 -1.020e+00 1.355e+00 -0.753 0.451592
## sp_ptbBM111 -6.142e-01 1.522e+00 -0.404 0.686529
## sp_ptbBM112 -2.228e+00 1.574e+00 -1.416 0.156860
## sp_ptbBM113 1.391e-01 1.876e+00 0.074 0.940895
## sp_ptbBM114 1.893e+00 1.248e+00 1.517 0.129306
## sp_ptbBM115 6.061e-01 1.145e+00 0.529 0.596591
## sp_ptbBM116 3.484e+00 1.383e+00 2.519 0.011775 *
## sp_ptbBM117 1.824e-01 1.398e+00 0.130 0.896224
## sp_ptbBM118 3.267e+00 1.253e+00 2.607 0.009136 **
## sp_ptbBM119 1.415e+00 1.434e+00 0.987 0.323855
## sp_ptbBM120 2.875e+00 1.356e+00 2.120 0.034028 *
## sp_ptbBM121 1.303e+00 1.166e+00 1.118 0.263590
## sp_ptbBM122 2.974e+00 1.027e+00 2.895 0.003793 **
## sp_ptbBM123 6.467e-02 9.618e-01 0.067 0.946391
## sp_ptbBM124 2.022e+00 1.083e+00 1.866 0.062008 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17620.3) family taken to be 1)
##
## Null deviance: 1073.13 on 886 degrees of freedom
## Residual deviance: 920.36 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2881.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17620
## Std. Err.: 101020
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2623.481SA2m11a <- glm.nb(smptbBM ~ cb11.maxT + sp_ptbBM,data=week); summary(SA2m11a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = smptbBM ~ cb11.maxT + sp_ptbBM, data = week,
## init.theta = 17657.07734, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1960 -0.8504 -0.1813 0.5293 3.5106
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.447e+01 1.967e+01 -1.752 0.07974 .
## cb11.maxTv1.l1 7.338e-01 4.336e-01 1.692 0.09060 .
## cb11.maxTv1.l2 -2.381e-01 3.119e-01 -0.763 0.44531
## cb11.maxTv2.l1 2.965e+00 1.537e+00 1.929 0.05376 .
## cb11.maxTv2.l2 -1.619e+00 1.144e+00 -1.415 0.15698
## cb11.maxTv3.l1 1.278e+00 5.970e-01 2.141 0.03224 *
## cb11.maxTv3.l2 -2.029e-01 4.335e-01 -0.468 0.63982
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.014e+06 2.911e+06 -0.692 0.48914
## sp_ptbBM5 9.672e+00 1.306e+01 0.740 0.45904
## sp_ptbBM6 1.700e-01 2.176e+00 0.078 0.93774
## sp_ptbBM7 2.201e+00 1.722e+00 1.278 0.20114
## sp_ptbBM8 -2.236e-01 1.400e+00 -0.160 0.87313
## sp_ptbBM9 2.375e+00 1.156e+00 2.055 0.03991 *
## sp_ptbBM10 -1.806e+00 1.233e+00 -1.465 0.14299
## sp_ptbBM11 3.189e+00 1.060e+00 3.009 0.00262 **
## sp_ptbBM12 4.452e-01 9.314e-01 0.478 0.63262
## sp_ptbBM13 1.205e+00 9.182e-01 1.313 0.18934
## sp_ptbBM14 -7.388e-01 1.405e+00 -0.526 0.59913
## sp_ptbBM15 -3.086e-01 1.390e+00 -0.222 0.82435
## sp_ptbBM16 -3.761e-01 1.260e+00 -0.298 0.76541
## sp_ptbBM17 -4.096e-02 1.218e+00 -0.034 0.97318
## sp_ptbBM18 6.804e-01 1.204e+00 0.565 0.57190
## sp_ptbBM19 3.060e-01 1.217e+00 0.251 0.80156
## sp_ptbBM20 -1.164e-01 1.255e+00 -0.093 0.92609
## sp_ptbBM21 6.143e-01 1.488e+00 0.413 0.67965
## sp_ptbBM22 8.342e-01 1.445e+00 0.577 0.56374
## sp_ptbBM23 3.264e-01 1.282e+00 0.255 0.79905
## sp_ptbBM24 1.241e+00 1.102e+00 1.126 0.26024
## sp_ptbBM25 1.621e+00 1.149e+00 1.410 0.15856
## sp_ptbBM26 1.899e+00 1.093e+00 1.738 0.08222 .
## sp_ptbBM27 -1.362e-01 1.256e+00 -0.108 0.91370
## sp_ptbBM28 2.786e-01 1.441e+00 0.193 0.84673
## sp_ptbBM29 1.347e+00 1.218e+00 1.106 0.26864
## sp_ptbBM30 5.759e-01 1.042e+00 0.553 0.58036
## sp_ptbBM31 2.093e+00 1.076e+00 1.946 0.05168 .
## sp_ptbBM32 9.284e-01 1.014e+00 0.915 0.35996
## sp_ptbBM33 1.522e-01 1.096e+00 0.139 0.88959
## sp_ptbBM34 9.529e-01 1.012e+00 0.941 0.34648
## sp_ptbBM35 -1.879e-01 1.537e+00 -0.122 0.90271
## sp_ptbBM36 2.398e-01 1.251e+00 0.192 0.84802
## sp_ptbBM37 4.468e-01 1.292e+00 0.346 0.72936
## sp_ptbBM38 1.787e+00 9.986e-01 1.789 0.07358 .
## sp_ptbBM39 5.518e-01 1.100e+00 0.502 0.61592
## sp_ptbBM40 2.496e+00 1.051e+00 2.373 0.01762 *
## sp_ptbBM41 1.788e-01 1.096e+00 0.163 0.87041
## sp_ptbBM42 6.946e-01 1.481e+00 0.469 0.63905
## sp_ptbBM43 9.028e-01 1.219e+00 0.741 0.45881
## sp_ptbBM44 1.098e+00 1.091e+00 1.006 0.31421
## sp_ptbBM45 1.690e+00 1.038e+00 1.628 0.10348
## sp_ptbBM46 1.021e+00 9.709e-01 1.052 0.29290
## sp_ptbBM47 7.966e-01 1.118e+00 0.712 0.47628
## sp_ptbBM48 8.523e-01 1.027e+00 0.830 0.40682
## sp_ptbBM49 1.233e+00 1.414e+00 0.872 0.38324
## sp_ptbBM50 4.391e-01 1.414e+00 0.311 0.75609
## sp_ptbBM51 2.965e+00 1.055e+00 2.811 0.00493 **
## sp_ptbBM52 9.533e-01 1.103e+00 0.864 0.38758
## sp_ptbBM53 2.291e+00 1.139e+00 2.011 0.04434 *
## sp_ptbBM54 1.269e+00 1.134e+00 1.118 0.26339
## sp_ptbBM55 4.936e-01 1.270e+00 0.389 0.69752
## sp_ptbBM56 1.329e+00 1.434e+00 0.926 0.35422
## sp_ptbBM57 1.085e+00 1.451e+00 0.748 0.45470
## sp_ptbBM58 9.557e-01 1.061e+00 0.901 0.36760
## sp_ptbBM59 -1.151e-01 1.261e+00 -0.091 0.92730
## sp_ptbBM60 -6.087e-02 1.388e+00 -0.044 0.96503
## sp_ptbBM61 -9.423e-04 1.235e+00 -0.001 0.99939
## sp_ptbBM62 -9.163e-01 1.273e+00 -0.720 0.47165
## sp_ptbBM63 -1.781e+00 1.577e+00 -1.129 0.25887
## sp_ptbBM64 -1.478e+00 1.762e+00 -0.839 0.40158
## sp_ptbBM65 -3.645e-01 1.351e+00 -0.270 0.78731
## sp_ptbBM66 -7.856e-01 1.143e+00 -0.687 0.49180
## sp_ptbBM67 1.603e+00 1.040e+00 1.541 0.12322
## sp_ptbBM68 9.113e-01 1.062e+00 0.858 0.39086
## sp_ptbBM69 1.083e+00 1.199e+00 0.903 0.36668
## sp_ptbBM70 -6.644e-01 1.691e+00 -0.393 0.69433
## sp_ptbBM71 9.057e-01 1.606e+00 0.564 0.57269
## sp_ptbBM72 -2.436e-01 1.567e+00 -0.155 0.87644
## sp_ptbBM73 1.245e+00 1.260e+00 0.988 0.32325
## sp_ptbBM74 1.560e+00 1.117e+00 1.396 0.16260
## sp_ptbBM75 1.844e+00 1.065e+00 1.731 0.08341 .
## sp_ptbBM76 1.389e+00 1.095e+00 1.269 0.20451
## sp_ptbBM77 1.488e-01 1.186e+00 0.126 0.90012
## sp_ptbBM78 6.386e-01 1.484e+00 0.430 0.66685
## sp_ptbBM79 -1.953e-01 1.252e+00 -0.156 0.87607
## sp_ptbBM80 6.775e-01 1.157e+00 0.586 0.55817
## sp_ptbBM81 8.801e-03 1.150e+00 0.008 0.99389
## sp_ptbBM82 2.885e-01 1.106e+00 0.261 0.79423
## sp_ptbBM83 1.023e-01 1.116e+00 0.092 0.92695
## sp_ptbBM84 4.713e-01 1.393e+00 0.338 0.73516
## sp_ptbBM85 -1.958e+00 1.511e+00 -1.296 0.19511
## sp_ptbBM86 1.423e+00 1.154e+00 1.233 0.21751
## sp_ptbBM87 2.492e-01 1.131e+00 0.220 0.82558
## sp_ptbBM88 2.144e+00 1.235e+00 1.737 0.08245 .
## sp_ptbBM89 -8.341e-02 1.283e+00 -0.065 0.94817
## sp_ptbBM90 6.998e-01 1.342e+00 0.521 0.60204
## sp_ptbBM91 4.489e+00 2.385e+00 1.882 0.05985 .
## sp_ptbBM92 3.390e+00 1.867e+00 1.816 0.06936 .
## sp_ptbBM93 2.363e+00 1.759e+00 1.343 0.17918
## sp_ptbBM94 2.919e+00 1.517e+00 1.924 0.05439 .
## sp_ptbBM95 2.585e+00 1.599e+00 1.617 0.10588
## sp_ptbBM96 3.468e+00 1.456e+00 2.382 0.01724 *
## sp_ptbBM97 1.164e+00 2.095e+00 0.556 0.57825
## sp_ptbBM98 2.784e+00 1.810e+00 1.538 0.12397
## sp_ptbBM99 2.621e+00 1.605e+00 1.633 0.10247
## sp_ptbBM100 2.939e+00 1.317e+00 2.232 0.02564 *
## sp_ptbBM101 2.839e+00 1.228e+00 2.312 0.02077 *
## sp_ptbBM102 2.822e+00 1.211e+00 2.330 0.01979 *
## sp_ptbBM103 2.132e+00 1.091e+00 1.953 0.05077 .
## sp_ptbBM104 2.171e+00 1.190e+00 1.823 0.06826 .
## sp_ptbBM105 6.207e-01 1.226e+00 0.506 0.61261
## sp_ptbBM106 -8.946e-02 1.469e+00 -0.061 0.95143
## sp_ptbBM107 -3.481e-01 1.098e+00 -0.317 0.75129
## sp_ptbBM108 5.216e-01 1.165e+00 0.448 0.65436
## sp_ptbBM109 1.571e-01 1.104e+00 0.142 0.88678
## sp_ptbBM110 2.090e-01 9.601e-01 0.218 0.82770
## sp_ptbBM111 4.433e-01 1.031e+00 0.430 0.66736
## sp_ptbBM112 -2.050e+00 1.246e+00 -1.646 0.09983 .
## sp_ptbBM113 8.349e-02 1.489e+00 0.056 0.95529
## sp_ptbBM114 5.382e-01 1.155e+00 0.466 0.64122
## sp_ptbBM115 -4.721e-01 1.209e+00 -0.391 0.69616
## sp_ptbBM116 2.315e+00 1.237e+00 1.872 0.06128 .
## sp_ptbBM117 -7.567e-01 1.416e+00 -0.535 0.59298
## sp_ptbBM118 2.246e+00 1.210e+00 1.856 0.06342 .
## sp_ptbBM119 4.739e-01 1.457e+00 0.325 0.74494
## sp_ptbBM120 1.657e+00 1.501e+00 1.104 0.26967
## sp_ptbBM121 5.124e-01 1.221e+00 0.420 0.67459
## sp_ptbBM122 2.398e+00 1.098e+00 2.184 0.02899 *
## sp_ptbBM123 -7.699e-03 9.545e-01 -0.008 0.99356
## sp_ptbBM124 1.863e+00 1.087e+00 1.713 0.08672 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17657.08) family taken to be 1)
##
## Null deviance: 1073.13 on 886 degrees of freedom
## Residual deviance: 921.32 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2882.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17657
## Std. Err.: 102559
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2624.439##to check model diag for univariate models
options(na.action="na.exclude")
library(dplyr) ##make sure lags are dplyr lags
##for SA2m1a avgWindSp ######
scatter.smooth(predict(SA2m1a, type='response'), rstandard(SA2m1a, type='deviance'), col='gray')
SA2m1a.resid<-residuals(SA2m1a, type="deviance")
SA2m1a.pred<-predict(SA2m1a, type="response")
length(SA2m1a.resid); length(SA2m1a.pred)## [1] 939## [1] 939pacf(SA2m1a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-10,12,19 & 25
library(dplyr)
#ensure that the lags are dplyr lags
SA2m1a.ac<-update(SA2m1a,.~.+lag(SA2m1a.resid,1)+lag(SA2m1a.resid,2)+lag(SA2m1a.resid,3)+ lag(SA2m1a.resid,4)+
lag(SA2m1a.resid,5)+lag(SA2m1a.resid,6)+lag(SA2m1a.resid,7)+ lag(SA2m1a.resid,8)+
lag(SA2m1a.resid,9)+lag(SA2m1a.resid,10)+lag(SA2m1a.resid,12)+ lag(SA2m1a.resid,19)+
lag(SA2m1a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA2m1a.resid_ac<-residuals(SA2m1a.ac, type="deviance")
SA2m1a.pred_ac<-predict(SA2m1a.ac, type="response")
pacf(SA2m1a.resid_ac,na.action = na.omit) 
length(SA2m1a.pred_ac)## [1] 939length(SA2m1a.resid_ac)## [1] 939##plot b4 ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m1a.pred,lwd=1, col="blue")
plot(week$time,SA2m1a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m1a.pred, week$smptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m1a.pred_ac,lwd=1, col="blue")
plot(week$time,SA2m1a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m1a.pred_ac, week$smptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.SA2m1a <- crosspred(cb1.avgWindSp, SA2m1a.ac, cen = 4.5, by=0.1,cumul=TRUE)
##for SA2m2a sun ######
summary(SA2m2a)##
## Call:
## glm.nb(formula = smptbBM ~ cb2.sun + sp_ptbBM, data = week, init.theta = 17721.422,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2402 -0.8797 -0.1478 0.5391 3.3445
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.467e+00 1.092e+01 -0.775 0.43805
## cb2.sunv1.l1 2.908e-01 2.303e-01 1.263 0.20667
## cb2.sunv1.l2 -2.009e-01 1.695e-01 -1.185 0.23596
## cb2.sunv2.l1 8.097e-01 9.558e-01 0.847 0.39691
## cb2.sunv2.l2 -5.924e-01 6.575e-01 -0.901 0.36752
## cb2.sunv3.l1 6.334e-01 3.487e-01 1.816 0.06933 .
## cb2.sunv3.l2 2.178e-01 2.457e-01 0.887 0.37528
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.119e+06 2.891e+06 -0.733 0.46359
## sp_ptbBM5 1.147e+01 1.320e+01 0.869 0.38504
## sp_ptbBM6 -4.368e-02 2.191e+00 -0.020 0.98409
## sp_ptbBM7 2.515e+00 1.266e+00 1.986 0.04701 *
## sp_ptbBM8 1.675e-03 1.131e+00 0.001 0.99882
## sp_ptbBM9 1.487e+00 1.023e+00 1.453 0.14612
## sp_ptbBM10 -2.859e+00 1.332e+00 -2.147 0.03180 *
## sp_ptbBM11 1.597e+00 9.418e-01 1.696 0.08987 .
## sp_ptbBM12 -4.623e-01 1.133e+00 -0.408 0.68330
## sp_ptbBM13 4.868e-01 1.125e+00 0.433 0.66513
## sp_ptbBM14 -2.666e-01 1.146e+00 -0.233 0.81604
## sp_ptbBM15 -1.837e-01 1.120e+00 -0.164 0.86973
## sp_ptbBM16 2.534e-01 1.106e+00 0.229 0.81873
## sp_ptbBM17 -3.439e-01 1.121e+00 -0.307 0.75900
## sp_ptbBM18 4.976e-02 1.140e+00 0.044 0.96519
## sp_ptbBM19 -2.994e-01 1.153e+00 -0.260 0.79506
## sp_ptbBM20 2.776e-01 1.098e+00 0.253 0.80032
## sp_ptbBM21 6.544e-01 1.186e+00 0.552 0.58115
## sp_ptbBM22 1.388e+00 1.065e+00 1.303 0.19247
## sp_ptbBM23 5.676e-01 1.173e+00 0.484 0.62846
## sp_ptbBM24 1.413e+00 1.042e+00 1.356 0.17513
## sp_ptbBM25 7.012e-01 1.078e+00 0.650 0.51551
## sp_ptbBM26 1.197e+00 1.162e+00 1.030 0.30287
## sp_ptbBM27 -7.627e-01 1.352e+00 -0.564 0.57257
## sp_ptbBM28 -6.348e-01 1.236e+00 -0.513 0.60763
## sp_ptbBM29 5.601e-01 1.222e+00 0.458 0.64668
## sp_ptbBM30 9.812e-02 1.144e+00 0.086 0.93167
## sp_ptbBM31 9.386e-01 1.151e+00 0.816 0.41463
## sp_ptbBM32 -1.915e-01 1.223e+00 -0.157 0.87563
## sp_ptbBM33 -3.646e-01 1.221e+00 -0.299 0.76522
## sp_ptbBM34 1.408e+00 1.190e+00 1.184 0.23660
## sp_ptbBM35 1.426e+00 1.217e+00 1.172 0.24131
## sp_ptbBM36 2.298e+00 1.217e+00 1.888 0.05897 .
## sp_ptbBM37 1.599e+00 1.173e+00 1.364 0.17264
## sp_ptbBM38 2.810e+00 1.139e+00 2.466 0.01366 *
## sp_ptbBM39 1.103e+00 1.152e+00 0.957 0.33848
## sp_ptbBM40 3.704e+00 1.211e+00 3.059 0.00222 **
## sp_ptbBM41 1.024e+00 1.129e+00 0.907 0.36440
## sp_ptbBM42 1.968e+00 1.224e+00 1.608 0.10773
## sp_ptbBM43 1.465e+00 1.033e+00 1.418 0.15616
## sp_ptbBM44 1.285e+00 9.393e-01 1.368 0.17125
## sp_ptbBM45 1.374e+00 8.925e-01 1.539 0.12374
## sp_ptbBM46 5.825e-02 9.360e-01 0.062 0.95038
## sp_ptbBM47 3.205e-01 1.093e+00 0.293 0.76931
## sp_ptbBM48 6.675e-01 9.452e-01 0.706 0.48008
## sp_ptbBM49 6.725e-01 1.282e+00 0.525 0.59990
## sp_ptbBM50 5.094e-01 1.052e+00 0.484 0.62811
## sp_ptbBM51 2.731e+00 1.056e+00 2.585 0.00973 **
## sp_ptbBM52 2.523e-01 9.977e-01 0.253 0.80034
## sp_ptbBM53 1.492e+00 9.840e-01 1.516 0.12949
## sp_ptbBM54 2.814e-01 1.004e+00 0.280 0.77930
## sp_ptbBM55 1.620e-01 1.030e+00 0.157 0.87502
## sp_ptbBM56 1.293e+00 1.208e+00 1.070 0.28464
## sp_ptbBM57 1.580e+00 1.154e+00 1.369 0.17098
## sp_ptbBM58 1.080e+00 1.043e+00 1.035 0.30056
## sp_ptbBM59 1.014e+00 1.042e+00 0.973 0.33064
## sp_ptbBM60 7.721e-01 9.887e-01 0.781 0.43485
## sp_ptbBM61 1.244e+00 1.070e+00 1.163 0.24491
## sp_ptbBM62 4.166e-01 1.073e+00 0.388 0.69794
## sp_ptbBM63 8.239e-02 1.236e+00 0.067 0.94686
## sp_ptbBM64 2.293e-01 1.158e+00 0.198 0.84304
## sp_ptbBM65 1.355e+00 1.058e+00 1.281 0.20031
## sp_ptbBM66 -5.301e-01 1.035e+00 -0.512 0.60854
## sp_ptbBM67 1.466e+00 9.512e-01 1.541 0.12322
## sp_ptbBM68 -3.877e-01 1.010e+00 -0.384 0.70111
## sp_ptbBM69 7.004e-01 1.036e+00 0.676 0.49881
## sp_ptbBM70 -7.821e-01 1.417e+00 -0.552 0.58103
## sp_ptbBM71 2.085e+00 1.323e+00 1.576 0.11508
## sp_ptbBM72 -3.898e-01 1.452e+00 -0.268 0.78834
## sp_ptbBM73 1.398e+00 1.295e+00 1.079 0.28037
## sp_ptbBM74 1.572e+00 1.293e+00 1.215 0.22426
## sp_ptbBM75 1.580e+00 1.173e+00 1.347 0.17813
## sp_ptbBM76 1.910e+00 1.205e+00 1.585 0.11294
## sp_ptbBM77 1.222e+00 1.240e+00 0.985 0.32449
## sp_ptbBM78 2.035e+00 1.201e+00 1.694 0.09035 .
## sp_ptbBM79 9.500e-01 1.146e+00 0.829 0.40710
## sp_ptbBM80 1.565e+00 1.185e+00 1.321 0.18666
## sp_ptbBM81 -7.640e-02 1.269e+00 -0.060 0.95198
## sp_ptbBM82 3.392e-01 1.147e+00 0.296 0.76734
## sp_ptbBM83 1.949e-01 1.151e+00 0.169 0.86547
## sp_ptbBM84 4.363e-01 1.159e+00 0.376 0.70662
## sp_ptbBM85 -1.779e+00 1.309e+00 -1.359 0.17412
## sp_ptbBM86 8.353e-01 1.236e+00 0.676 0.49910
## sp_ptbBM87 -4.653e-02 1.178e+00 -0.040 0.96849
## sp_ptbBM88 1.786e+00 1.197e+00 1.492 0.13571
## sp_ptbBM89 -8.129e-01 1.422e+00 -0.572 0.56764
## sp_ptbBM90 1.882e+00 1.306e+00 1.440 0.14973
## sp_ptbBM91 1.870e+00 1.374e+00 1.361 0.17356
## sp_ptbBM92 2.376e+00 1.144e+00 2.076 0.03786 *
## sp_ptbBM93 7.167e-01 1.234e+00 0.581 0.56155
## sp_ptbBM94 1.181e+00 1.189e+00 0.993 0.32077
## sp_ptbBM95 9.451e-01 1.080e+00 0.875 0.38154
## sp_ptbBM96 2.252e+00 1.052e+00 2.140 0.03233 *
## sp_ptbBM97 2.473e-01 1.074e+00 0.230 0.81791
## sp_ptbBM98 1.336e+00 1.420e+00 0.941 0.34672
## sp_ptbBM99 1.985e+00 1.067e+00 1.859 0.06301 .
## sp_ptbBM100 1.836e+00 1.102e+00 1.666 0.09564 .
## sp_ptbBM101 1.491e+00 1.010e+00 1.476 0.13982
## sp_ptbBM102 1.356e+00 1.041e+00 1.302 0.19277
## sp_ptbBM103 1.168e+00 1.052e+00 1.110 0.26679
## sp_ptbBM104 1.310e+00 9.769e-01 1.341 0.17985
## sp_ptbBM105 -4.534e-01 1.317e+00 -0.344 0.73060
## sp_ptbBM106 -3.253e-01 1.134e+00 -0.287 0.77425
## sp_ptbBM107 -1.059e+00 1.232e+00 -0.859 0.39027
## sp_ptbBM108 3.786e-03 1.245e+00 0.003 0.99757
## sp_ptbBM109 -2.306e-01 1.210e+00 -0.190 0.84893
## sp_ptbBM110 -3.911e-01 1.381e+00 -0.283 0.77701
## sp_ptbBM111 6.038e-01 1.248e+00 0.484 0.62841
## sp_ptbBM112 -4.786e-01 1.143e+00 -0.419 0.67534
## sp_ptbBM113 2.366e+00 1.072e+00 2.208 0.02728 *
## sp_ptbBM114 1.193e+00 9.805e-01 1.217 0.22379
## sp_ptbBM115 8.943e-01 1.151e+00 0.777 0.43713
## sp_ptbBM116 2.755e+00 1.023e+00 2.694 0.00707 **
## sp_ptbBM117 -8.531e-01 1.275e+00 -0.669 0.50330
## sp_ptbBM118 2.922e+00 1.162e+00 2.514 0.01193 *
## sp_ptbBM119 7.258e-01 1.324e+00 0.548 0.58365
## sp_ptbBM120 2.051e+00 1.133e+00 1.811 0.07019 .
## sp_ptbBM121 4.275e-01 1.064e+00 0.402 0.68783
## sp_ptbBM122 2.271e+00 9.682e-01 2.346 0.01899 *
## sp_ptbBM123 7.221e-01 8.618e-01 0.838 0.40209
## sp_ptbBM124 1.970e+00 1.122e+00 1.756 0.07912 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17721.42) family taken to be 1)
##
## Null deviance: 1073.1 on 886 degrees of freedom
## Residual deviance: 920.3 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2881.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17721
## Std. Err.: 102690
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2623.416scatter.smooth(predict(SA2m2a, type='response'), rstandard(SA2m2a, type='deviance'), col='gray')
SA2m2a.resid<-residuals(SA2m2a, type="deviance")
SA2m2a.pred<-predict(SA2m2a, type="response")
length(SA2m2a.resid); length(SA2m2a.pred)## [1] 939## [1] 939pacf(SA2m2a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-10, 12, 19 & 25
#ensure that the lags are dplyr lags
SA2m2a.ac<-update(SA2m2a,.~.+lag(SA2m2a.resid,1)+lag(SA2m2a.resid,2)+lag(SA2m2a.resid,3)+lag(SA2m2a.resid,4)+
lag(SA2m2a.resid,5)+lag(SA2m2a.resid,6)+lag(SA2m2a.resid,7)+lag(SA2m2a.resid,8)+
lag(SA2m2a.resid,9)+lag(SA2m2a.resid,10)+lag(SA2m2a.resid,12)+lag(SA2m2a.resid,19)+
lag(SA2m2a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA2m2a.resid_ac<-residuals(SA2m2a.ac, type="deviance")
SA2m2a.pred_ac<-predict(SA2m2a.ac, type="response")
pacf(SA2m2a.resid_ac,na.action = na.omit) 
length(SA2m2a.pred_ac); length(SA2m2a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m2a.pred,lwd=1, col="blue")
plot(week$time,SA2m2a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m2a.pred, week$smptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m2a.pred_ac,lwd=1, col="blue")
plot(week$time,SA2m2a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m2a.pred_ac, week$smptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.SA2m2a <- crosspred(cb2.sun, SA2m2a.ac, cen = 50.7, by=0.1,cumul=TRUE)
##for SA2m3a RF ######
summary(SA2m3a)##
## Call:
## glm.nb(formula = smptbBM ~ cb3.RF + sp_ptbBM, data = week, init.theta = 17311.02781,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1072 -0.8474 -0.1545 0.5437 3.3762
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.039e-01 3.091e+00 0.195 0.84509
## cb3.RFv1.l1 2.795e-01 3.032e-01 0.922 0.35673
## cb3.RFv1.l2 5.948e-02 2.121e-01 0.280 0.77918
## cb3.RFv2.l1 -3.413e-01 4.369e-01 -0.781 0.43464
## cb3.RFv2.l2 4.430e-01 3.182e-01 1.392 0.16393
## cb3.RFv3.l1 -4.776e-01 7.029e-01 -0.679 0.49683
## cb3.RFv3.l2 5.909e-01 5.428e-01 1.089 0.27628
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.127e+06 2.881e+06 -0.738 0.46045
## sp_ptbBM5 9.343e+00 1.300e+01 0.719 0.47230
## sp_ptbBM6 -1.472e-01 2.150e+00 -0.068 0.94541
## sp_ptbBM7 2.343e+00 1.288e+00 1.820 0.06881 .
## sp_ptbBM8 5.184e-02 1.201e+00 0.043 0.96557
## sp_ptbBM9 2.209e+00 1.103e+00 2.002 0.04530 *
## sp_ptbBM10 -1.757e+00 1.397e+00 -1.257 0.20873
## sp_ptbBM11 2.289e+00 9.885e-01 2.315 0.02059 *
## sp_ptbBM12 8.056e-01 1.116e+00 0.722 0.47046
## sp_ptbBM13 2.493e+00 1.160e+00 2.149 0.03164 *
## sp_ptbBM14 9.179e-01 1.116e+00 0.823 0.41064
## sp_ptbBM15 1.380e+00 1.029e+00 1.341 0.17988
## sp_ptbBM16 1.299e+00 1.160e+00 1.120 0.26280
## sp_ptbBM17 1.235e+00 1.123e+00 1.099 0.27183
## sp_ptbBM18 1.364e+00 1.118e+00 1.220 0.22237
## sp_ptbBM19 1.429e+00 1.082e+00 1.321 0.18636
## sp_ptbBM20 2.140e+00 1.300e+00 1.646 0.09973 .
## sp_ptbBM21 1.889e+00 1.070e+00 1.765 0.07748 .
## sp_ptbBM22 2.051e+00 1.055e+00 1.945 0.05175 .
## sp_ptbBM23 8.189e-01 1.085e+00 0.755 0.45028
## sp_ptbBM24 1.761e+00 1.109e+00 1.587 0.11243
## sp_ptbBM25 1.351e+00 1.073e+00 1.259 0.20811
## sp_ptbBM26 2.111e+00 1.134e+00 1.861 0.06275 .
## sp_ptbBM27 3.860e-01 1.369e+00 0.282 0.77803
## sp_ptbBM28 8.888e-01 1.259e+00 0.706 0.48027
## sp_ptbBM29 1.938e+00 1.140e+00 1.699 0.08924 .
## sp_ptbBM30 1.195e+00 1.149e+00 1.040 0.29846
## sp_ptbBM31 2.028e+00 9.850e-01 2.059 0.03946 *
## sp_ptbBM32 1.036e+00 1.060e+00 0.978 0.32831
## sp_ptbBM33 6.523e-01 1.147e+00 0.569 0.56967
## sp_ptbBM34 1.885e+00 1.088e+00 1.733 0.08308 .
## sp_ptbBM35 1.669e+00 1.015e+00 1.644 0.10018
## sp_ptbBM36 1.487e+00 9.289e-01 1.601 0.10942
## sp_ptbBM37 1.420e+00 1.074e+00 1.322 0.18619
## sp_ptbBM38 1.827e+00 9.800e-01 1.864 0.06232 .
## sp_ptbBM39 6.273e-01 1.033e+00 0.607 0.54388
## sp_ptbBM40 2.226e+00 9.734e-01 2.287 0.02222 *
## sp_ptbBM41 1.082e+00 1.066e+00 1.016 0.30985
## sp_ptbBM42 1.446e+00 1.116e+00 1.295 0.19534
## sp_ptbBM43 1.406e+00 1.030e+00 1.365 0.17228
## sp_ptbBM44 1.827e+00 1.015e+00 1.799 0.07198 .
## sp_ptbBM45 2.026e+00 1.005e+00 2.016 0.04383 *
## sp_ptbBM46 1.327e+00 9.894e-01 1.341 0.17993
## sp_ptbBM47 1.198e+00 9.042e-01 1.325 0.18528
## sp_ptbBM48 1.473e+00 9.582e-01 1.537 0.12425
## sp_ptbBM49 2.367e+00 1.036e+00 2.285 0.02230 *
## sp_ptbBM50 5.538e-01 1.060e+00 0.522 0.60138
## sp_ptbBM51 3.215e+00 9.693e-01 3.317 0.00091 ***
## sp_ptbBM52 8.381e-01 1.060e+00 0.790 0.42932
## sp_ptbBM53 1.713e+00 1.022e+00 1.676 0.09370 .
## sp_ptbBM54 1.085e+00 1.074e+00 1.010 0.31239
## sp_ptbBM55 9.351e-01 1.069e+00 0.875 0.38173
## sp_ptbBM56 2.306e+00 1.449e+00 1.592 0.11149
## sp_ptbBM57 2.171e+00 1.160e+00 1.872 0.06122 .
## sp_ptbBM58 1.297e+00 1.126e+00 1.152 0.24917
## sp_ptbBM59 8.377e-01 1.065e+00 0.787 0.43152
## sp_ptbBM60 6.641e-01 1.108e+00 0.599 0.54902
## sp_ptbBM61 1.261e+00 1.023e+00 1.233 0.21763
## sp_ptbBM62 1.011e+00 1.277e+00 0.792 0.42842
## sp_ptbBM63 2.032e+00 1.454e+00 1.397 0.16239
## sp_ptbBM64 2.248e+00 1.298e+00 1.732 0.08329 .
## sp_ptbBM65 2.450e+00 1.146e+00 2.138 0.03249 *
## sp_ptbBM66 1.086e+00 1.092e+00 0.995 0.31993
## sp_ptbBM67 3.150e+00 1.137e+00 2.771 0.00559 **
## sp_ptbBM68 1.235e+00 9.704e-01 1.272 0.20330
## sp_ptbBM69 2.731e+00 1.234e+00 2.212 0.02695 *
## sp_ptbBM70 7.114e-02 1.289e+00 0.055 0.95597
## sp_ptbBM71 1.381e+00 1.313e+00 1.052 0.29287
## sp_ptbBM72 -8.720e-01 1.318e+00 -0.661 0.50831
## sp_ptbBM73 2.941e-01 1.130e+00 0.260 0.79468
## sp_ptbBM74 8.138e-01 9.769e-01 0.833 0.40481
## sp_ptbBM75 1.131e+00 1.056e+00 1.071 0.28412
## sp_ptbBM76 1.309e+00 1.047e+00 1.250 0.21148
## sp_ptbBM77 1.065e+00 1.078e+00 0.988 0.32309
## sp_ptbBM78 2.457e+00 1.153e+00 2.131 0.03305 *
## sp_ptbBM79 1.399e+00 1.181e+00 1.184 0.23624
## sp_ptbBM80 1.923e+00 1.100e+00 1.749 0.08028 .
## sp_ptbBM81 7.618e-01 1.201e+00 0.634 0.52589
## sp_ptbBM82 8.513e-01 1.089e+00 0.781 0.43457
## sp_ptbBM83 1.363e+00 1.090e+00 1.251 0.21088
## sp_ptbBM84 1.684e+00 1.069e+00 1.575 0.11524
## sp_ptbBM85 -3.127e-01 1.192e+00 -0.262 0.79314
## sp_ptbBM86 2.462e+00 1.015e+00 2.425 0.01533 *
## sp_ptbBM87 3.422e-01 1.031e+00 0.332 0.73990
## sp_ptbBM88 2.147e+00 9.900e-01 2.168 0.03014 *
## sp_ptbBM89 -7.474e-01 1.161e+00 -0.644 0.51959
## sp_ptbBM90 2.262e+00 1.079e+00 2.097 0.03598 *
## sp_ptbBM91 1.452e+00 1.202e+00 1.208 0.22701
## sp_ptbBM92 1.631e+00 1.024e+00 1.594 0.11104
## sp_ptbBM93 6.669e-01 1.221e+00 0.546 0.58481
## sp_ptbBM94 1.129e+00 1.174e+00 0.962 0.33620
## sp_ptbBM95 9.532e-01 1.082e+00 0.881 0.37836
## sp_ptbBM96 1.854e+00 9.216e-01 2.012 0.04421 *
## sp_ptbBM97 5.638e-01 1.148e+00 0.491 0.62332
## sp_ptbBM98 1.678e+00 1.272e+00 1.319 0.18721
## sp_ptbBM99 1.826e+00 1.130e+00 1.616 0.10609
## sp_ptbBM100 1.551e+00 1.052e+00 1.475 0.14015
## sp_ptbBM101 1.529e+00 9.687e-01 1.579 0.11445
## sp_ptbBM102 1.299e+00 1.009e+00 1.288 0.19784
## sp_ptbBM103 1.403e+00 1.009e+00 1.391 0.16429
## sp_ptbBM104 2.148e+00 9.998e-01 2.148 0.03169 *
## sp_ptbBM105 1.416e+00 1.286e+00 1.101 0.27109
## sp_ptbBM106 6.852e-01 1.185e+00 0.578 0.56308
## sp_ptbBM107 3.551e-01 1.110e+00 0.320 0.74908
## sp_ptbBM108 1.269e+00 1.068e+00 1.188 0.23497
## sp_ptbBM109 1.451e+00 1.144e+00 1.269 0.20456
## sp_ptbBM110 8.159e-01 9.817e-01 0.831 0.40595
## sp_ptbBM111 2.318e+00 1.289e+00 1.798 0.07216 .
## sp_ptbBM112 -6.968e-02 1.226e+00 -0.057 0.95467
## sp_ptbBM113 2.564e+00 1.003e+00 2.556 0.01059 *
## sp_ptbBM114 1.483e+00 1.042e+00 1.424 0.15458
## sp_ptbBM115 7.475e-01 1.129e+00 0.662 0.50802
## sp_ptbBM116 2.507e+00 1.005e+00 2.495 0.01259 *
## sp_ptbBM117 -7.662e-01 1.201e+00 -0.638 0.52351
## sp_ptbBM118 2.598e+00 1.116e+00 2.328 0.01993 *
## sp_ptbBM119 1.365e-01 1.302e+00 0.105 0.91650
## sp_ptbBM120 1.603e+00 1.076e+00 1.489 0.13645
## sp_ptbBM121 4.199e-03 1.043e+00 0.004 0.99679
## sp_ptbBM122 1.997e+00 1.005e+00 1.987 0.04692 *
## sp_ptbBM123 -7.013e-01 8.890e-01 -0.789 0.43016
## sp_ptbBM124 1.960e+00 1.110e+00 1.766 0.07741 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17311.03) family taken to be 1)
##
## Null deviance: 1073.13 on 886 degrees of freedom
## Residual deviance: 926.96 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2888.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17311
## Std. Err.: 101315
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2630.083scatter.smooth(predict(SA2m3a, type='response'), rstandard(SA2m3a, type='deviance'), col='gray')
SA2m3a.resid<-residuals(SA2m3a, type="deviance")
SA2m3a.pred<-predict(SA2m3a, type="response")
length(SA2m3a.resid); length(SA2m3a.pred)## [1] 939## [1] 939pacf(SA2m3a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-10, 12, 19 & 25
#ensure that the lags are dplyr lags
SA2m3a.ac<-update(SA2m3a,.~.+lag(SA2m3a.resid,1)+lag(SA2m3a.resid,2)+lag(SA2m3a.resid,3)+lag(SA2m3a.resid,4)+
lag(SA2m3a.resid,5)+lag(SA2m3a.resid,6)+lag(SA2m3a.resid,7)+lag(SA2m3a.resid,8)+
lag(SA2m3a.resid,9)+lag(SA2m3a.resid,10)+lag(SA2m3a.resid,12)+lag(SA2m3a.resid,19)+
lag(SA2m3a.resid,25)) ## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA2m3a.resid_ac<-residuals(SA2m3a.ac, type="deviance")
SA2m3a.pred_ac<-predict(SA2m3a.ac, type="response")
pacf(SA2m3a.resid_ac,na.action = na.omit) 
length(SA2m3a.pred_ac); length(SA2m3a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m3a.pred,lwd=1, col="blue")
plot(week$time,SA2m3a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m3a.pred, week$smptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m3a.pred_ac,lwd=1, col="blue")
plot(week$time,SA2m3a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m3a.pred_ac, week$smptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices now
pred.SA2m3a <- crosspred(cb3.RF, SA2m3a.ac, cen = 44.9, by=0.1,cumul=TRUE)
##for SA2m5a minRH ######
summary(SA2m5a)##
## Call:
## glm.nb(formula = smptbBM ~ cb5.minRH + sp_ptbBM, data = week,
## init.theta = 17146.60379, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2022 -0.8207 -0.1746 0.5366 3.4423
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.245e+01 1.203e+01 1.035 0.3006
## cb5.minRHv1.l1 -2.014e-01 2.717e-01 -0.741 0.4584
## cb5.minRHv1.l2 -1.065e-01 1.854e-01 -0.574 0.5657
## cb5.minRHv2.l1 -1.326e+00 1.052e+00 -1.261 0.2075
## cb5.minRHv2.l2 -4.867e-01 7.829e-01 -0.622 0.5341
## cb5.minRHv3.l1 -1.049e+00 6.873e-01 -1.526 0.1269
## cb5.minRHv3.l2 -7.475e-02 5.228e-01 -0.143 0.8863
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.208e+06 2.877e+06 -0.768 0.4427
## sp_ptbBM5 1.047e+01 1.294e+01 0.809 0.4185
## sp_ptbBM6 -7.047e-01 2.175e+00 -0.324 0.7459
## sp_ptbBM7 1.768e+00 1.268e+00 1.394 0.1633
## sp_ptbBM8 -4.314e-01 1.200e+00 -0.360 0.7191
## sp_ptbBM9 1.238e+00 1.170e+00 1.058 0.2903
## sp_ptbBM10 -2.633e+00 1.299e+00 -2.028 0.0426 *
## sp_ptbBM11 1.305e+00 1.140e+00 1.145 0.2523
## sp_ptbBM12 -6.931e-01 1.044e+00 -0.664 0.5066
## sp_ptbBM13 3.038e-01 1.177e+00 0.258 0.7963
## sp_ptbBM14 -5.900e-01 1.317e+00 -0.448 0.6541
## sp_ptbBM15 -3.868e-01 1.396e+00 -0.277 0.7818
## sp_ptbBM16 -9.142e-01 1.547e+00 -0.591 0.5546
## sp_ptbBM17 -6.489e-01 1.325e+00 -0.490 0.6242
## sp_ptbBM18 -3.635e-02 1.334e+00 -0.027 0.9783
## sp_ptbBM19 -1.132e-01 1.236e+00 -0.092 0.9270
## sp_ptbBM20 -2.381e-01 1.373e+00 -0.173 0.8623
## sp_ptbBM21 1.008e+00 1.268e+00 0.795 0.4266
## sp_ptbBM22 1.222e+00 1.158e+00 1.055 0.2914
## sp_ptbBM23 1.961e-02 1.155e+00 0.017 0.9865
## sp_ptbBM24 8.196e-01 1.013e+00 0.809 0.4184
## sp_ptbBM25 6.572e-01 9.107e-01 0.722 0.4705
## sp_ptbBM26 8.621e-01 9.685e-01 0.890 0.3734
## sp_ptbBM27 -7.593e-01 1.087e+00 -0.699 0.4848
## sp_ptbBM28 -3.870e-01 1.224e+00 -0.316 0.7519
## sp_ptbBM29 9.068e-01 1.082e+00 0.838 0.4020
## sp_ptbBM30 -5.232e-01 1.228e+00 -0.426 0.6700
## sp_ptbBM31 6.980e-01 1.266e+00 0.551 0.5813
## sp_ptbBM32 -7.384e-01 1.568e+00 -0.471 0.6378
## sp_ptbBM33 -1.423e+00 1.493e+00 -0.953 0.3407
## sp_ptbBM34 -7.583e-01 1.593e+00 -0.476 0.6340
## sp_ptbBM35 -1.153e+00 1.830e+00 -0.630 0.5285
## sp_ptbBM36 -1.079e+00 1.866e+00 -0.579 0.5629
## sp_ptbBM37 -1.159e+00 1.633e+00 -0.710 0.4777
## sp_ptbBM38 2.210e-01 1.385e+00 0.160 0.8732
## sp_ptbBM39 -8.584e-01 1.402e+00 -0.612 0.5404
## sp_ptbBM40 1.136e+00 1.181e+00 0.962 0.3359
## sp_ptbBM41 -8.396e-01 1.295e+00 -0.648 0.5168
## sp_ptbBM42 -2.606e-02 1.289e+00 -0.020 0.9839
## sp_ptbBM43 1.044e-01 1.127e+00 0.093 0.9261
## sp_ptbBM44 3.191e-02 1.146e+00 0.028 0.9778
## sp_ptbBM45 5.143e-01 1.079e+00 0.477 0.6336
## sp_ptbBM46 4.816e-02 1.123e+00 0.043 0.9658
## sp_ptbBM47 -3.113e-02 1.181e+00 -0.026 0.9790
## sp_ptbBM48 1.395e-02 9.592e-01 0.015 0.9884
## sp_ptbBM49 2.430e-01 1.427e+00 0.170 0.8648
## sp_ptbBM50 -9.178e-01 1.200e+00 -0.765 0.4443
## sp_ptbBM51 1.035e+00 1.294e+00 0.800 0.4236
## sp_ptbBM52 -8.799e-01 1.229e+00 -0.716 0.4741
## sp_ptbBM53 5.530e-01 1.159e+00 0.477 0.6334
## sp_ptbBM54 -1.093e-01 1.138e+00 -0.096 0.9235
## sp_ptbBM55 -4.604e-01 1.232e+00 -0.374 0.7087
## sp_ptbBM56 9.365e-01 1.345e+00 0.696 0.4861
## sp_ptbBM57 7.976e-01 1.277e+00 0.624 0.5323
## sp_ptbBM58 5.718e-01 1.086e+00 0.527 0.5984
## sp_ptbBM59 -3.490e-02 1.221e+00 -0.029 0.9772
## sp_ptbBM60 2.655e-01 1.227e+00 0.216 0.8288
## sp_ptbBM61 1.734e-01 1.214e+00 0.143 0.8864
## sp_ptbBM62 -1.327e-01 1.233e+00 -0.108 0.9143
## sp_ptbBM63 -7.531e-01 1.552e+00 -0.485 0.6274
## sp_ptbBM64 -4.754e-01 1.676e+00 -0.284 0.7767
## sp_ptbBM65 -2.762e-01 1.380e+00 -0.200 0.8414
## sp_ptbBM66 -6.488e-01 1.104e+00 -0.588 0.5566
## sp_ptbBM67 1.470e+00 8.933e-01 1.646 0.0998 .
## sp_ptbBM68 6.558e-02 8.663e-01 0.076 0.9397
## sp_ptbBM69 1.229e+00 9.491e-01 1.295 0.1952
## sp_ptbBM70 -9.788e-01 1.315e+00 -0.744 0.4567
## sp_ptbBM71 1.067e+00 1.249e+00 0.854 0.3933
## sp_ptbBM72 -1.312e+00 1.312e+00 -1.000 0.3173
## sp_ptbBM73 1.094e-01 1.154e+00 0.095 0.9245
## sp_ptbBM74 4.537e-01 1.064e+00 0.426 0.6698
## sp_ptbBM75 3.540e-01 1.088e+00 0.325 0.7449
## sp_ptbBM76 7.591e-01 9.293e-01 0.817 0.4140
## sp_ptbBM77 -6.185e-02 1.186e+00 -0.052 0.9584
## sp_ptbBM78 1.106e+00 1.316e+00 0.840 0.4009
## sp_ptbBM79 -1.316e-02 1.172e+00 -0.011 0.9910
## sp_ptbBM80 5.194e-01 1.195e+00 0.435 0.6637
## sp_ptbBM81 -6.148e-01 1.150e+00 -0.535 0.5929
## sp_ptbBM82 -2.858e-01 1.061e+00 -0.269 0.7877
## sp_ptbBM83 -1.521e-01 1.114e+00 -0.137 0.8913
## sp_ptbBM84 6.460e-01 1.222e+00 0.529 0.5969
## sp_ptbBM85 -1.577e+00 1.428e+00 -1.105 0.2693
## sp_ptbBM86 9.463e-01 1.302e+00 0.727 0.4673
## sp_ptbBM87 -1.639e-01 1.065e+00 -0.154 0.8778
## sp_ptbBM88 1.374e+00 1.078e+00 1.275 0.2023
## sp_ptbBM89 -1.308e+00 1.142e+00 -1.146 0.2517
## sp_ptbBM90 1.393e+00 1.118e+00 1.246 0.2129
## sp_ptbBM91 2.003e+00 2.044e+00 0.980 0.3271
## sp_ptbBM92 2.735e+00 1.500e+00 1.823 0.0683 .
## sp_ptbBM93 1.142e+00 1.629e+00 0.701 0.4833
## sp_ptbBM94 1.855e+00 1.363e+00 1.361 0.1735
## sp_ptbBM95 1.701e+00 1.360e+00 1.251 0.2110
## sp_ptbBM96 2.962e+00 1.381e+00 2.144 0.0320 *
## sp_ptbBM97 1.752e+00 1.721e+00 1.018 0.3087
## sp_ptbBM98 1.769e+00 1.646e+00 1.075 0.2825
## sp_ptbBM99 2.603e+00 1.409e+00 1.847 0.0648 .
## sp_ptbBM100 2.406e+00 1.297e+00 1.855 0.0637 .
## sp_ptbBM101 2.288e+00 1.244e+00 1.839 0.0659 .
## sp_ptbBM102 1.968e+00 1.193e+00 1.650 0.0990 .
## sp_ptbBM103 1.781e+00 1.137e+00 1.566 0.1173
## sp_ptbBM104 2.172e+00 1.078e+00 2.016 0.0438 *
## sp_ptbBM105 1.047e+00 1.180e+00 0.887 0.3751
## sp_ptbBM106 1.587e+00 1.273e+00 1.246 0.2126
## sp_ptbBM107 3.905e-01 1.060e+00 0.368 0.7126
## sp_ptbBM108 1.574e+00 1.102e+00 1.429 0.1531
## sp_ptbBM109 1.133e+00 8.777e-01 1.291 0.1967
## sp_ptbBM110 1.061e+00 9.052e-01 1.172 0.2413
## sp_ptbBM111 1.779e+00 9.014e-01 1.974 0.0484 *
## sp_ptbBM112 -7.253e-01 1.017e+00 -0.713 0.4756
## sp_ptbBM113 1.956e+00 1.163e+00 1.682 0.0926 .
## sp_ptbBM114 9.573e-01 1.120e+00 0.855 0.3928
## sp_ptbBM115 -2.512e-02 1.137e+00 -0.022 0.9824
## sp_ptbBM116 2.074e+00 1.022e+00 2.029 0.0424 *
## sp_ptbBM117 -9.378e-01 1.308e+00 -0.717 0.4735
## sp_ptbBM118 1.805e+00 1.073e+00 1.682 0.0926 .
## sp_ptbBM119 2.527e-01 1.332e+00 0.190 0.8496
## sp_ptbBM120 1.523e+00 1.298e+00 1.174 0.2406
## sp_ptbBM121 2.957e-01 1.210e+00 0.244 0.8069
## sp_ptbBM122 2.439e+00 1.081e+00 2.256 0.0240 *
## sp_ptbBM123 -1.224e-01 8.909e-01 -0.137 0.8907
## sp_ptbBM124 2.149e+00 1.095e+00 1.963 0.0496 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17146.6) family taken to be 1)
##
## Null deviance: 1073.12 on 886 degrees of freedom
## Residual deviance: 927.39 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2888.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17147
## Std. Err.: 101690
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2630.514scatter.smooth(predict(SA2m5a, type='response'), rstandard(SA2m5a, type='deviance'), col='gray')
SA2m5a.resid<-residuals(SA2m5a, type="deviance")
SA2m5a.pred<-predict(SA2m5a, type="response")
length(SA2m5a.resid); length(SA2m5a.pred)## [1] 939## [1] 939pacf(SA2m5a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-10, 12, 19 & 25
#ensure that the lags are dplyr lags
SA2m5a.ac<-update(SA2m5a,.~.+lag(SA2m5a.resid,1)+lag(SA2m5a.resid,2)+lag(SA2m5a.resid,3)+lag(SA2m5a.resid,4)+
lag(SA2m5a.resid,5)+lag(SA2m5a.resid,6)+lag(SA2m5a.resid,7)+lag(SA2m5a.resid,8)+
lag(SA2m5a.resid,9)+lag(SA2m5a.resid,10)+lag(SA2m5a.resid,12)+lag(SA2m5a.resid,19)+
lag(SA2m5a.resid,25)) ## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(SA2m5a.ac)##
## Call:
## glm.nb(formula = smptbBM ~ cb5.minRH + sp_ptbBM + lag(SA2m5a.resid,
## 1) + lag(SA2m5a.resid, 2) + lag(SA2m5a.resid, 3) + lag(SA2m5a.resid,
## 4) + lag(SA2m5a.resid, 5) + lag(SA2m5a.resid, 6) + lag(SA2m5a.resid,
## 7) + lag(SA2m5a.resid, 8) + lag(SA2m5a.resid, 9) + lag(SA2m5a.resid,
## 10) + lag(SA2m5a.resid, 12) + lag(SA2m5a.resid, 19) + lag(SA2m5a.resid,
## 25), data = week, init.theta = 31275.64283, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.16995 -0.80626 -0.09892 0.49257 2.28119
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 4.39911 12.85044 0.342 0.732102
## cb5.minRHv1.l1 0.06066 0.28604 0.212 0.832061
## cb5.minRHv1.l2 -0.05990 0.19311 -0.310 0.756426
## cb5.minRHv2.l1 -0.79951 1.14345 -0.699 0.484423
## cb5.minRHv2.l2 -0.61549 0.83298 -0.739 0.459964
## cb5.minRHv3.l1 -0.84077 0.75756 -1.110 0.267068
## cb5.minRHv3.l2 -0.11387 0.55988 -0.203 0.838838
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -39.45843 53.12206 -0.743 0.457610
## sp_ptbBM9 6.16205 4.82212 1.278 0.201295
## sp_ptbBM10 -4.22890 2.29995 -1.839 0.065960 .
## sp_ptbBM11 1.49648 1.23797 1.209 0.226731
## sp_ptbBM12 -0.53868 1.10636 -0.487 0.626336
## sp_ptbBM13 2.65591 1.18523 2.241 0.025036 *
## sp_ptbBM14 -1.50910 1.32154 -1.142 0.253488
## sp_ptbBM15 2.99976 1.44399 2.077 0.037763 *
## sp_ptbBM16 -1.25185 1.55836 -0.803 0.421796
## sp_ptbBM17 1.69168 1.33869 1.264 0.206343
## sp_ptbBM18 -0.89762 1.36501 -0.658 0.510799
## sp_ptbBM19 2.41770 1.19894 2.017 0.043744 *
## sp_ptbBM20 -2.01465 1.38283 -1.457 0.145145
## sp_ptbBM21 3.10289 1.30642 2.375 0.017544 *
## sp_ptbBM22 -0.04881 1.11562 -0.044 0.965104
## sp_ptbBM23 0.45007 1.13495 0.397 0.691697
## sp_ptbBM24 0.57969 0.98543 0.588 0.556360
## sp_ptbBM25 -0.13144 0.90745 -0.145 0.884835
## sp_ptbBM26 1.68309 0.96170 1.750 0.080099 .
## sp_ptbBM27 -1.80222 0.97168 -1.855 0.063633 .
## sp_ptbBM28 0.02088 1.23102 0.017 0.986465
## sp_ptbBM29 0.48634 1.09043 0.446 0.655593
## sp_ptbBM30 1.66062 1.22748 1.353 0.176096
## sp_ptbBM31 -0.02322 1.31722 -0.018 0.985936
## sp_ptbBM32 2.21853 1.61815 1.371 0.170368
## sp_ptbBM33 -2.68384 1.50630 -1.782 0.074791 .
## sp_ptbBM34 3.13048 1.65678 1.889 0.058825 .
## sp_ptbBM35 -1.03369 1.87502 -0.551 0.581430
## sp_ptbBM36 1.82809 1.93014 0.947 0.343572
## sp_ptbBM37 -0.20748 1.68336 -0.123 0.901908
## sp_ptbBM38 1.36240 1.40669 0.969 0.332788
## sp_ptbBM39 0.30147 1.35955 0.222 0.824514
## sp_ptbBM40 0.73670 1.18728 0.620 0.534931
## sp_ptbBM41 1.07061 1.25603 0.852 0.394004
## sp_ptbBM42 -2.48705 1.34419 -1.850 0.064281 .
## sp_ptbBM43 2.54855 1.19923 2.125 0.033574 *
## sp_ptbBM44 -1.99842 1.13032 -1.768 0.077059 .
## sp_ptbBM45 1.95188 1.05461 1.851 0.064198 .
## sp_ptbBM46 -0.50783 1.13256 -0.448 0.653867
## sp_ptbBM47 1.30042 1.15401 1.127 0.259797
## sp_ptbBM48 -0.58116 0.97697 -0.595 0.551938
## sp_ptbBM49 1.88492 1.44009 1.309 0.190571
## sp_ptbBM50 -1.40572 1.19360 -1.178 0.238910
## sp_ptbBM51 2.10192 1.31800 1.595 0.110761
## sp_ptbBM52 -0.29848 1.27772 -0.234 0.815296
## sp_ptbBM53 1.29958 1.12790 1.152 0.249234
## sp_ptbBM54 0.33204 1.11431 0.298 0.765721
## sp_ptbBM55 0.26103 1.27290 0.205 0.837518
## sp_ptbBM56 0.42874 1.32532 0.323 0.746318
## sp_ptbBM57 1.56053 1.26772 1.231 0.218334
## sp_ptbBM58 0.70832 1.06993 0.662 0.507957
## sp_ptbBM59 0.81148 1.23524 0.657 0.511219
## sp_ptbBM60 1.34291 1.24024 1.083 0.278903
## sp_ptbBM61 -0.12402 1.23968 -0.100 0.920309
## sp_ptbBM62 2.42212 1.26878 1.909 0.056261 .
## sp_ptbBM63 -2.04265 1.59476 -1.281 0.200245
## sp_ptbBM64 3.81125 1.76238 2.163 0.030575 *
## sp_ptbBM65 -2.37255 1.40376 -1.690 0.091002 .
## sp_ptbBM66 3.13894 1.12357 2.794 0.005211 **
## sp_ptbBM67 -0.17221 0.88072 -0.196 0.844979
## sp_ptbBM68 1.65752 0.85296 1.943 0.051986 .
## sp_ptbBM69 0.71958 0.91873 0.783 0.433492
## sp_ptbBM70 0.57638 1.28796 0.448 0.654507
## sp_ptbBM71 -0.55385 1.30794 -0.423 0.671962
## sp_ptbBM72 0.97466 1.29360 0.753 0.451182
## sp_ptbBM73 -3.18125 1.23835 -2.569 0.010201 *
## sp_ptbBM74 2.56191 1.08148 2.369 0.017841 *
## sp_ptbBM75 -4.02771 1.09404 -3.681 0.000232 ***
## sp_ptbBM76 2.81648 1.01930 2.763 0.005725 **
## sp_ptbBM77 -1.24709 1.17913 -1.058 0.290219
## sp_ptbBM78 2.97666 1.33357 2.232 0.025608 *
## sp_ptbBM79 -0.11534 1.20225 -0.096 0.923569
## sp_ptbBM80 1.61243 1.16071 1.389 0.164779
## sp_ptbBM81 -2.53117 1.21920 -2.076 0.037887 *
## sp_ptbBM82 1.53934 1.09458 1.406 0.159625
## sp_ptbBM83 -2.07705 1.11411 -1.864 0.062279 .
## sp_ptbBM84 2.78661 1.26393 2.205 0.027474 *
## sp_ptbBM85 -3.68067 1.48194 -2.484 0.013003 *
## sp_ptbBM86 1.75661 1.28330 1.369 0.171055
## sp_ptbBM87 -0.92982 1.01077 -0.920 0.357617
## sp_ptbBM88 1.46878 1.10150 1.333 0.182390
## sp_ptbBM89 -2.17665 1.15866 -1.879 0.060300 .
## sp_ptbBM90 0.29806 1.10976 0.269 0.788251
## sp_ptbBM91 1.90442 2.07870 0.916 0.359584
## sp_ptbBM92 2.38891 1.53859 1.553 0.120503
## sp_ptbBM93 2.16096 1.70926 1.264 0.206133
## sp_ptbBM94 -0.58094 1.42665 -0.407 0.683857
## sp_ptbBM95 1.29215 1.38785 0.931 0.351832
## sp_ptbBM96 2.45357 1.54477 1.588 0.112217
## sp_ptbBM97 1.37540 1.78596 0.770 0.441230
## sp_ptbBM98 -1.99197 1.67186 -1.191 0.233470
## sp_ptbBM99 2.86035 1.49837 1.909 0.056265 .
## sp_ptbBM100 1.65780 1.30500 1.270 0.203963
## sp_ptbBM101 2.98836 1.30180 2.296 0.021702 *
## sp_ptbBM102 1.38793 1.20250 1.154 0.248417
## sp_ptbBM103 0.72179 1.15333 0.626 0.531426
## sp_ptbBM104 1.74407 1.12737 1.547 0.121859
## sp_ptbBM105 1.50226 1.17340 1.280 0.200451
## sp_ptbBM106 1.33848 1.35728 0.986 0.324059
## sp_ptbBM107 0.11996 1.02171 0.117 0.906533
## sp_ptbBM108 -1.36543 1.14053 -1.197 0.231233
## sp_ptbBM109 3.26138 0.88307 3.693 0.000221 ***
## sp_ptbBM110 -0.46485 0.90211 -0.515 0.606350
## sp_ptbBM111 4.14165 0.92546 4.475 7.63e-06 ***
## sp_ptbBM112 -2.86286 1.01531 -2.820 0.004807 **
## sp_ptbBM113 2.46979 1.13326 2.179 0.029304 *
## sp_ptbBM114 -0.05189 1.10151 -0.047 0.962425
## sp_ptbBM115 1.30729 1.12904 1.158 0.246912
## sp_ptbBM116 0.11971 1.02790 0.116 0.907290
## sp_ptbBM117 0.18346 1.29956 0.141 0.887734
## sp_ptbBM118 -1.87401 1.11199 -1.685 0.091936 .
## sp_ptbBM119 2.07727 1.44738 1.435 0.151232
## sp_ptbBM120 -1.79605 1.33366 -1.347 0.178074
## sp_ptbBM121 0.28388 1.21149 0.234 0.814732
## sp_ptbBM122 1.61285 1.06516 1.514 0.129978
## sp_ptbBM123 -0.78211 0.90689 -0.862 0.388465
## sp_ptbBM124 1.86183 1.09048 1.707 0.087757 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(SA2m5a.resid, 1) -0.45869 0.03505 -13.085 < 2e-16 ***
## lag(SA2m5a.resid, 2) -0.51139 0.03973 -12.870 < 2e-16 ***
## lag(SA2m5a.resid, 3) -0.58420 0.04193 -13.932 < 2e-16 ***
## lag(SA2m5a.resid, 4) -0.59920 0.04510 -13.287 < 2e-16 ***
## lag(SA2m5a.resid, 5) -0.60767 0.04522 -13.438 < 2e-16 ***
## lag(SA2m5a.resid, 6) -0.51920 0.04505 -11.525 < 2e-16 ***
## lag(SA2m5a.resid, 7) -0.44693 0.04209 -10.619 < 2e-16 ***
## lag(SA2m5a.resid, 8) -0.40417 0.03912 -10.331 < 2e-16 ***
## lag(SA2m5a.resid, 9) -0.31845 0.03618 -8.801 < 2e-16 ***
## lag(SA2m5a.resid, 10) -0.23531 0.03346 -7.033 2.03e-12 ***
## lag(SA2m5a.resid, 12) -0.09478 0.03047 -3.111 0.001865 **
## lag(SA2m5a.resid, 19) 0.01691 0.02984 0.567 0.570926
## lag(SA2m5a.resid, 25) -0.06204 0.02916 -2.128 0.033362 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(31275.64) family taken to be 1)
##
## Null deviance: 1033.68 on 861 degrees of freedom
## Residual deviance: 575.95 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2509.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 31276
## Std. Err.: 119480
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2233.373SA2m5a.resid_ac<-residuals(SA2m5a.ac, type="deviance")
SA2m5a.pred_ac<-predict(SA2m5a.ac, type="response")
pacf(SA2m5a.resid_ac,na.action = na.omit) 
length(SA2m5a.pred_ac); length(SA2m5a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m5a.pred,lwd=1, col="blue")
plot(week$time,SA2m5a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m5a.pred, week$smptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m5a.pred_ac,lwd=1, col="blue")
plot(week$time,SA2m5a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m5a.pred_ac, week$smptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA2m5a <- crosspred(cb5.minRH, SA2m5a.ac, cen = 63, by=0.1,cumul=TRUE)
##for SA2m9a minT ######
summary(SA2m9a)##
## Call:
## glm.nb(formula = smptbBM ~ cb9.minT + sp_ptbBM, data = week,
## init.theta = 17244.58218, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2806 -0.8281 -0.1674 0.5151 3.4436
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.655e+00 1.181e+01 -0.563 0.57320
## cb9.minTv1.l1 1.717e-01 2.921e-01 0.588 0.55677
## cb9.minTv1.l2 -3.204e-01 2.064e-01 -1.552 0.12061
## cb9.minTv2.l1 1.055e+00 1.003e+00 1.052 0.29277
## cb9.minTv2.l2 -1.871e+00 7.590e-01 -2.465 0.01368 *
## cb9.minTv3.l1 9.312e-01 5.899e-01 1.579 0.11440
## cb9.minTv3.l2 -2.211e-01 4.271e-01 -0.518 0.60467
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.192e+06 2.875e+06 -0.762 0.44583
## sp_ptbBM5 9.556e+00 1.293e+01 0.739 0.45989
## sp_ptbBM6 -1.237e+00 2.136e+00 -0.579 0.56256
## sp_ptbBM7 9.743e-01 1.303e+00 0.748 0.45459
## sp_ptbBM8 -1.159e+00 1.262e+00 -0.918 0.35854
## sp_ptbBM9 1.203e+00 1.206e+00 0.997 0.31860
## sp_ptbBM10 -2.838e+00 1.528e+00 -1.857 0.06334 .
## sp_ptbBM11 1.716e+00 1.066e+00 1.609 0.10760
## sp_ptbBM12 -8.127e-01 1.139e+00 -0.713 0.47564
## sp_ptbBM13 2.837e-01 1.085e+00 0.262 0.79368
## sp_ptbBM14 -5.042e-01 1.161e+00 -0.434 0.66396
## sp_ptbBM15 1.789e-01 1.094e+00 0.163 0.87016
## sp_ptbBM16 -1.238e-01 1.301e+00 -0.095 0.92421
## sp_ptbBM17 -2.990e-01 1.183e+00 -0.253 0.80045
## sp_ptbBM18 -2.622e-01 1.271e+00 -0.206 0.83658
## sp_ptbBM19 -9.826e-01 1.317e+00 -0.746 0.45573
## sp_ptbBM20 -7.331e-01 1.312e+00 -0.559 0.57637
## sp_ptbBM21 1.608e-01 1.139e+00 0.141 0.88771
## sp_ptbBM22 7.798e-02 1.117e+00 0.070 0.94434
## sp_ptbBM23 -1.166e-01 1.116e+00 -0.104 0.91681
## sp_ptbBM24 4.975e-01 1.004e+00 0.495 0.62041
## sp_ptbBM25 5.006e-01 8.988e-01 0.557 0.57754
## sp_ptbBM26 7.711e-01 9.535e-01 0.809 0.41865
## sp_ptbBM27 -1.738e+00 1.249e+00 -1.391 0.16418
## sp_ptbBM28 6.916e-01 1.095e+00 0.632 0.52753
## sp_ptbBM29 1.133e+00 9.108e-01 1.244 0.21354
## sp_ptbBM30 3.826e-01 1.019e+00 0.376 0.70725
## sp_ptbBM31 1.562e+00 1.079e+00 1.448 0.14770
## sp_ptbBM32 8.492e-01 1.085e+00 0.783 0.43374
## sp_ptbBM33 7.951e-01 1.216e+00 0.654 0.51320
## sp_ptbBM34 2.631e+00 1.621e+00 1.622 0.10471
## sp_ptbBM35 3.228e+00 1.655e+00 1.950 0.05115 .
## sp_ptbBM36 3.409e+00 1.896e+00 1.798 0.07216 .
## sp_ptbBM37 2.641e+00 2.287e+00 1.155 0.24826
## sp_ptbBM38 2.817e+00 2.161e+00 1.304 0.19240
## sp_ptbBM39 5.310e-01 2.047e+00 0.259 0.79538
## sp_ptbBM40 1.662e+00 2.038e+00 0.816 0.41469
## sp_ptbBM41 -6.515e-01 1.571e+00 -0.415 0.67843
## sp_ptbBM42 7.416e-01 1.393e+00 0.533 0.59434
## sp_ptbBM43 3.800e-01 1.305e+00 0.291 0.77092
## sp_ptbBM44 4.347e-01 9.476e-01 0.459 0.64645
## sp_ptbBM45 1.429e+00 9.538e-01 1.498 0.13420
## sp_ptbBM46 8.901e-01 1.002e+00 0.888 0.37436
## sp_ptbBM47 1.304e+00 9.612e-01 1.356 0.17497
## sp_ptbBM48 3.732e-01 1.152e+00 0.324 0.74602
## sp_ptbBM49 2.506e+00 1.049e+00 2.390 0.01684 *
## sp_ptbBM50 8.726e-01 1.220e+00 0.715 0.47453
## sp_ptbBM51 3.290e+00 1.145e+00 2.873 0.00407 **
## sp_ptbBM52 1.112e+00 1.442e+00 0.771 0.44045
## sp_ptbBM53 2.467e+00 1.411e+00 1.749 0.08025 .
## sp_ptbBM54 1.032e+00 1.449e+00 0.712 0.47633
## sp_ptbBM55 -2.079e-01 1.328e+00 -0.157 0.87555
## sp_ptbBM56 1.319e+00 1.386e+00 0.952 0.34128
## sp_ptbBM57 1.307e+00 1.159e+00 1.127 0.25956
## sp_ptbBM58 9.991e-01 1.151e+00 0.868 0.38521
## sp_ptbBM59 2.573e-01 9.622e-01 0.267 0.78919
## sp_ptbBM60 8.643e-01 9.156e-01 0.944 0.34517
## sp_ptbBM61 5.587e-01 8.883e-01 0.629 0.52941
## sp_ptbBM62 -1.903e-01 9.764e-01 -0.195 0.84547
## sp_ptbBM63 1.250e-01 1.040e+00 0.120 0.90433
## sp_ptbBM64 -4.573e-01 1.486e+00 -0.308 0.75830
## sp_ptbBM65 4.506e-01 1.574e+00 0.286 0.77461
## sp_ptbBM66 -1.368e+00 1.532e+00 -0.893 0.37172
## sp_ptbBM67 1.182e-01 1.413e+00 0.084 0.93334
## sp_ptbBM68 -1.629e+00 1.493e+00 -1.090 0.27551
## sp_ptbBM69 -1.079e+00 1.504e+00 -0.717 0.47317
## sp_ptbBM70 -2.806e+00 1.576e+00 -1.780 0.07509 .
## sp_ptbBM71 4.137e-02 1.221e+00 0.034 0.97296
## sp_ptbBM72 -1.140e+00 1.235e+00 -0.923 0.35621
## sp_ptbBM73 6.324e-01 1.080e+00 0.586 0.55813
## sp_ptbBM74 5.359e-01 1.006e+00 0.533 0.59412
## sp_ptbBM75 8.929e-01 1.055e+00 0.846 0.39758
## sp_ptbBM76 3.291e-01 1.009e+00 0.326 0.74427
## sp_ptbBM77 -5.638e-02 1.056e+00 -0.053 0.95741
## sp_ptbBM78 7.911e-01 1.123e+00 0.705 0.48109
## sp_ptbBM79 -2.908e-01 1.157e+00 -0.251 0.80160
## sp_ptbBM80 9.298e-01 1.096e+00 0.848 0.39619
## sp_ptbBM81 -6.246e-01 1.168e+00 -0.535 0.59279
## sp_ptbBM82 -4.184e-01 1.183e+00 -0.354 0.72360
## sp_ptbBM83 -2.222e-01 1.206e+00 -0.184 0.85377
## sp_ptbBM84 4.044e-01 1.329e+00 0.304 0.76094
## sp_ptbBM85 -2.666e+00 1.659e+00 -1.607 0.10804
## sp_ptbBM86 7.108e-01 1.626e+00 0.437 0.66194
## sp_ptbBM87 -1.082e+00 1.627e+00 -0.665 0.50606
## sp_ptbBM88 -5.881e-02 1.562e+00 -0.038 0.96997
## sp_ptbBM89 -3.142e+00 1.666e+00 -1.886 0.05934 .
## sp_ptbBM90 -1.254e+00 1.683e+00 -0.745 0.45632
## sp_ptbBM91 -6.237e-01 1.338e+00 -0.466 0.64101
## sp_ptbBM92 5.943e-01 1.256e+00 0.473 0.63604
## sp_ptbBM93 -1.049e+00 1.488e+00 -0.705 0.48091
## sp_ptbBM94 2.211e-01 1.484e+00 0.149 0.88154
## sp_ptbBM95 -1.067e+00 1.463e+00 -0.729 0.46586
## sp_ptbBM96 1.020e-01 1.418e+00 0.072 0.94265
## sp_ptbBM97 -2.585e+00 1.635e+00 -1.581 0.11382
## sp_ptbBM98 -1.779e+00 1.688e+00 -1.054 0.29185
## sp_ptbBM99 -1.191e+00 1.645e+00 -0.724 0.46904
## sp_ptbBM100 -1.260e-01 1.658e+00 -0.076 0.93944
## sp_ptbBM101 -7.338e-01 1.817e+00 -0.404 0.68624
## sp_ptbBM102 -1.010e+00 1.688e+00 -0.598 0.54977
## sp_ptbBM103 -1.443e+00 1.874e+00 -0.770 0.44118
## sp_ptbBM104 -8.012e-01 1.814e+00 -0.442 0.65869
## sp_ptbBM105 -3.293e+00 2.800e+00 -1.176 0.23962
## sp_ptbBM106 -5.380e+00 3.910e+00 -1.376 0.16883
## sp_ptbBM107 -6.159e+00 4.737e+00 -1.300 0.19348
## sp_ptbBM108 -5.147e+00 4.158e+00 -1.238 0.21576
## sp_ptbBM109 -6.187e+00 4.475e+00 -1.383 0.16679
## sp_ptbBM110 -6.522e+00 4.291e+00 -1.520 0.12855
## sp_ptbBM111 -6.321e+00 4.814e+00 -1.313 0.18917
## sp_ptbBM112 -6.662e+00 4.074e+00 -1.635 0.10205
## sp_ptbBM113 -2.352e+00 2.668e+00 -0.882 0.37799
## sp_ptbBM114 -1.463e+00 1.782e+00 -0.821 0.41163
## sp_ptbBM115 -2.146e+00 1.667e+00 -1.288 0.19785
## sp_ptbBM116 2.054e-01 1.623e+00 0.127 0.89928
## sp_ptbBM117 -2.752e+00 1.449e+00 -1.899 0.05752 .
## sp_ptbBM118 4.002e-01 1.189e+00 0.337 0.73633
## sp_ptbBM119 -1.210e+00 1.185e+00 -1.021 0.30704
## sp_ptbBM120 3.803e-01 1.100e+00 0.346 0.72967
## sp_ptbBM121 -4.307e-01 9.751e-01 -0.442 0.65867
## sp_ptbBM122 1.917e+00 9.272e-01 2.068 0.03867 *
## sp_ptbBM123 -3.913e-01 9.475e-01 -0.413 0.67958
## sp_ptbBM124 2.574e+00 1.134e+00 2.270 0.02319 *
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17244.58) family taken to be 1)
##
## Null deviance: 1073.12 on 886 degrees of freedom
## Residual deviance: 919.41 on 759 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2880.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17245
## Std. Err.: 95888
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2622.532scatter.smooth(predict(SA2m9a, type='response'), rstandard(SA2m9a, type='deviance'), col='gray')
SA2m9a.resid<-residuals(SA2m9a, type="deviance")
SA2m9a.pred<-predict(SA2m9a, type="response")
length(SA2m9a.resid); length(SA2m9a.pred)## [1] 939## [1] 939pacf(SA2m9a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-10, 12, 19 & 25
#ensure that the lags are dplyr lags
SA2m9a.ac<-update(SA2m9a,.~.+lag(SA2m9a.resid,1)+lag(SA2m9a.resid,2)+lag(SA2m9a.resid,3)+lag(SA2m9a.resid,4)+
lag(SA2m9a.resid,5)+lag(SA2m9a.resid,6)+lag(SA2m9a.resid,7)+lag(SA2m9a.resid,8)+
lag(SA2m9a.resid,9)+lag(SA2m9a.resid,10)+lag(SA2m9a.resid,12)+lag(SA2m9a.resid,19)+
lag(SA2m9a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(SA2m9a.ac)##
## Call:
## glm.nb(formula = smptbBM ~ cb9.minT + sp_ptbBM + lag(SA2m9a.resid,
## 1) + lag(SA2m9a.resid, 2) + lag(SA2m9a.resid, 3) + lag(SA2m9a.resid,
## 4) + lag(SA2m9a.resid, 5) + lag(SA2m9a.resid, 6) + lag(SA2m9a.resid,
## 7) + lag(SA2m9a.resid, 8) + lag(SA2m9a.resid, 9) + lag(SA2m9a.resid,
## 10) + lag(SA2m9a.resid, 12) + lag(SA2m9a.resid, 19) + lag(SA2m9a.resid,
## 25), data = week, init.theta = 31407.68312, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0835 -0.7860 -0.0962 0.4706 2.1814
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -27.63115 12.83361 -2.153 0.03132 *
## cb9.minTv1.l1 0.26842 0.31295 0.858 0.39106
## cb9.minTv1.l2 -0.20454 0.21813 -0.938 0.34840
## cb9.minTv2.l1 2.71474 1.07995 2.514 0.01195 *
## cb9.minTv2.l2 -0.25883 0.79829 -0.324 0.74577
## cb9.minTv3.l1 1.47517 0.61149 2.412 0.01585 *
## cb9.minTv3.l2 0.37115 0.45347 0.818 0.41309
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -42.21952 53.00113 -0.797 0.42570
## sp_ptbBM9 5.54218 4.83132 1.147 0.25132
## sp_ptbBM10 -4.90431 2.40821 -2.037 0.04170 *
## sp_ptbBM11 0.97309 1.19386 0.815 0.41503
## sp_ptbBM12 -1.07425 1.21612 -0.883 0.37705
## sp_ptbBM13 1.82672 1.09908 1.662 0.09650 .
## sp_ptbBM14 -2.17002 1.18458 -1.832 0.06697 .
## sp_ptbBM15 2.23664 1.13427 1.972 0.04862 *
## sp_ptbBM16 -2.01471 1.31235 -1.535 0.12474
## sp_ptbBM17 0.44305 1.19980 0.369 0.71192
## sp_ptbBM18 -2.24344 1.32654 -1.691 0.09080 .
## sp_ptbBM19 0.63513 1.31512 0.483 0.62913
## sp_ptbBM20 -2.37431 1.35150 -1.757 0.07895 .
## sp_ptbBM21 0.90559 1.16799 0.775 0.43814
## sp_ptbBM22 -1.12650 1.12503 -1.001 0.31668
## sp_ptbBM23 -0.91082 1.13042 -0.806 0.42039
## sp_ptbBM24 1.02739 0.98615 1.042 0.29750
## sp_ptbBM25 -1.08733 0.86913 -1.251 0.21091
## sp_ptbBM26 2.92658 0.94825 3.086 0.00203 **
## sp_ptbBM27 -3.38850 1.19710 -2.831 0.00465 **
## sp_ptbBM28 1.39800 1.08981 1.283 0.19957
## sp_ptbBM29 -0.00411 0.91288 -0.005 0.99641
## sp_ptbBM30 1.42912 1.02516 1.394 0.16330
## sp_ptbBM31 -0.44587 1.10842 -0.402 0.68750
## sp_ptbBM32 2.26242 1.09846 2.060 0.03943 *
## sp_ptbBM33 -1.51856 1.21579 -1.249 0.21166
## sp_ptbBM34 4.83904 1.64456 2.942 0.00326 **
## sp_ptbBM35 2.02800 1.67184 1.213 0.22512
## sp_ptbBM36 4.94920 1.95447 2.532 0.01133 *
## sp_ptbBM37 3.53731 2.45072 1.443 0.14891
## sp_ptbBM38 4.84119 2.29560 2.109 0.03495 *
## sp_ptbBM39 2.14444 2.18682 0.981 0.32678
## sp_ptbBM40 3.29974 2.18067 1.513 0.13023
## sp_ptbBM41 1.83259 1.69343 1.082 0.27918
## sp_ptbBM42 -1.24203 1.49992 -0.828 0.40763
## sp_ptbBM43 2.77991 1.43095 1.943 0.05205 .
## sp_ptbBM44 -1.67413 0.92129 -1.817 0.06919 .
## sp_ptbBM45 1.71290 0.96705 1.771 0.07652 .
## sp_ptbBM46 -0.40481 0.99249 -0.408 0.68337
## sp_ptbBM47 1.01691 0.93443 1.088 0.27648
## sp_ptbBM48 -0.55666 1.17693 -0.473 0.63623
## sp_ptbBM49 1.98901 1.04303 1.907 0.05653 .
## sp_ptbBM50 -0.49013 1.20663 -0.406 0.68460
## sp_ptbBM51 2.95159 1.17624 2.509 0.01210 *
## sp_ptbBM52 0.41549 1.50314 0.276 0.78223
## sp_ptbBM53 2.26031 1.43001 1.581 0.11396
## sp_ptbBM54 0.22365 1.44474 0.155 0.87698
## sp_ptbBM55 1.13292 1.42761 0.794 0.42744
## sp_ptbBM56 -0.44398 1.40779 -0.315 0.75248
## sp_ptbBM57 1.72019 1.17002 1.470 0.14150
## sp_ptbBM58 0.02893 1.16862 0.025 0.98025
## sp_ptbBM59 0.04244 0.96823 0.044 0.96503
## sp_ptbBM60 0.72677 0.90107 0.807 0.41992
## sp_ptbBM61 -0.96536 0.90904 -1.062 0.28825
## sp_ptbBM62 1.74951 0.97474 1.795 0.07268 .
## sp_ptbBM63 -2.79709 1.10148 -2.539 0.01110 *
## sp_ptbBM64 2.88936 1.54276 1.873 0.06109 .
## sp_ptbBM65 -2.86405 1.66387 -1.721 0.08519 .
## sp_ptbBM66 2.15825 1.57870 1.367 0.17159
## sp_ptbBM67 -1.67730 1.44240 -1.163 0.24489
## sp_ptbBM68 0.24199 1.52124 0.159 0.87361
## sp_ptbBM69 -1.44373 1.51758 -0.951 0.34143
## sp_ptbBM70 -1.84984 1.59205 -1.162 0.24527
## sp_ptbBM71 -1.56992 1.31464 -1.194 0.23241
## sp_ptbBM72 -0.04705 1.21596 -0.039 0.96914
## sp_ptbBM73 -3.88327 1.19369 -3.253 0.00114 **
## sp_ptbBM74 1.75454 1.03961 1.688 0.09147 .
## sp_ptbBM75 -4.89613 1.09250 -4.482 7.41e-06 ***
## sp_ptbBM76 2.34893 1.09866 2.138 0.03252 *
## sp_ptbBM77 -2.28400 1.06589 -2.143 0.03213 *
## sp_ptbBM78 2.09622 1.16011 1.807 0.07078 .
## sp_ptbBM79 -1.15420 1.23340 -0.936 0.34938
## sp_ptbBM80 0.72235 1.07833 0.670 0.50294
## sp_ptbBM81 -3.48017 1.26825 -2.744 0.00607 **
## sp_ptbBM82 0.71755 1.22792 0.584 0.55898
## sp_ptbBM83 -2.93828 1.24795 -2.354 0.01855 *
## sp_ptbBM84 1.62060 1.40852 1.151 0.24991
## sp_ptbBM85 -4.61338 1.77395 -2.601 0.00931 **
## sp_ptbBM86 1.04476 1.62728 0.642 0.52085
## sp_ptbBM87 -1.52219 1.65821 -0.918 0.35863
## sp_ptbBM88 0.63063 1.61467 0.391 0.69612
## sp_ptbBM89 -2.99904 1.71765 -1.746 0.08081 .
## sp_ptbBM90 -1.59319 1.71796 -0.927 0.35373
## sp_ptbBM91 0.17881 1.32582 0.135 0.89272
## sp_ptbBM92 0.76631 1.25667 0.610 0.54200
## sp_ptbBM93 0.36066 1.57121 0.230 0.81845
## sp_ptbBM94 -1.84323 1.49010 -1.237 0.21609
## sp_ptbBM95 -0.26121 1.46587 -0.178 0.85857
## sp_ptbBM96 1.02369 1.48271 0.690 0.48993
## sp_ptbBM97 -1.07362 1.71734 -0.625 0.53186
## sp_ptbBM98 -4.66739 1.78758 -2.611 0.00903 **
## sp_ptbBM99 0.24701 1.69898 0.145 0.88441
## sp_ptbBM100 0.08491 1.71818 0.049 0.96059
## sp_ptbBM101 0.85848 1.86033 0.461 0.64446
## sp_ptbBM102 -0.75386 1.73565 -0.434 0.66404
## sp_ptbBM103 -1.52594 1.91889 -0.795 0.42648
## sp_ptbBM104 -0.84313 1.87005 -0.451 0.65209
## sp_ptbBM105 -1.62220 2.88817 -0.562 0.57434
## sp_ptbBM106 -4.96673 3.99992 -1.242 0.21434
## sp_ptbBM107 -6.05040 4.80259 -1.260 0.20773
## sp_ptbBM108 -7.48538 4.29621 -1.742 0.08145 .
## sp_ptbBM109 -4.76713 4.58620 -1.039 0.29860
## sp_ptbBM110 -9.02316 4.41051 -2.046 0.04077 *
## sp_ptbBM111 -6.80190 4.98816 -1.364 0.17269
## sp_ptbBM112 -10.92573 4.22236 -2.588 0.00967 **
## sp_ptbBM113 -3.40441 2.81048 -1.211 0.22577
## sp_ptbBM114 -1.50255 1.80577 -0.832 0.40536
## sp_ptbBM115 -0.51182 1.72029 -0.298 0.76607
## sp_ptbBM116 -0.45349 1.67075 -0.271 0.78606
## sp_ptbBM117 -0.58345 1.43354 -0.407 0.68401
## sp_ptbBM118 -2.28986 1.22528 -1.869 0.06164 .
## sp_ptbBM119 0.68019 1.30386 0.522 0.60190
## sp_ptbBM120 -2.52609 1.12048 -2.254 0.02417 *
## sp_ptbBM121 -0.70983 0.96999 -0.732 0.46429
## sp_ptbBM122 1.00859 0.88946 1.134 0.25682
## sp_ptbBM123 -1.23130 1.00583 -1.224 0.22089
## sp_ptbBM124 1.64907 1.09122 1.511 0.13073
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(SA2m9a.resid, 1) -0.47271 0.03580 -13.204 < 2e-16 ***
## lag(SA2m9a.resid, 2) -0.53314 0.04146 -12.860 < 2e-16 ***
## lag(SA2m9a.resid, 3) -0.60432 0.04380 -13.799 < 2e-16 ***
## lag(SA2m9a.resid, 4) -0.61955 0.04682 -13.233 < 2e-16 ***
## lag(SA2m9a.resid, 5) -0.62310 0.04676 -13.326 < 2e-16 ***
## lag(SA2m9a.resid, 6) -0.53458 0.04685 -11.411 < 2e-16 ***
## lag(SA2m9a.resid, 7) -0.45406 0.04355 -10.426 < 2e-16 ***
## lag(SA2m9a.resid, 8) -0.40718 0.03998 -10.184 < 2e-16 ***
## lag(SA2m9a.resid, 9) -0.32267 0.03677 -8.775 < 2e-16 ***
## lag(SA2m9a.resid, 10) -0.23151 0.03402 -6.805 1.01e-11 ***
## lag(SA2m9a.resid, 12) -0.09235 0.03078 -3.000 0.00270 **
## lag(SA2m9a.resid, 19) 0.01972 0.02977 0.662 0.50765
## lag(SA2m9a.resid, 25) -0.06634 0.02923 -2.269 0.02324 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(31407.68) family taken to be 1)
##
## Null deviance: 1033.68 on 861 degrees of freedom
## Residual deviance: 568.79 on 725 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2502.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 31408
## Std. Err.: 119252
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2226.217SA2m9a.resid_ac<-residuals(SA2m9a.ac, type="deviance")
SA2m9a.pred_ac<-predict(SA2m9a.ac, type="response")
pacf(SA2m9a.resid_ac,na.action = na.omit) 
length(SA2m9a.pred_ac); length(SA2m9a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m9a.pred,lwd=1, col="blue")
plot(week$time,SA2m9a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m9a.pred, week$smptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,SA2m9a.pred_ac,lwd=1, col="blue")
plot(week$time,SA2m9a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA2m9a.pred_ac, week$smptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA2m9a <- crosspred(cb9.minT, SA2m9a.ac, cen = 24.0, by=0.1,cumul=TRUE)
###final SA #2 model #####
mod_fullSA2bm<-glm.nb(smptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun + cb1.avgWindSp + sp_ptbBM, data = week)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullSA2bm) ##
## Call:
## glm.nb(formula = smptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBM, data = week, init.theta = 18565.20813,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3728 -0.8366 -0.1217 0.5363 3.3675
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.678e+01 2.388e+01 -0.703 0.4822
## cb3.RFv1.l1 3.959e-02 3.422e-01 0.116 0.9079
## cb3.RFv1.l2 2.275e-01 2.520e-01 0.903 0.3665
## cb3.RFv2.l1 4.213e-01 5.470e-01 0.770 0.4411
## cb3.RFv2.l2 7.258e-01 3.812e-01 1.904 0.0570 .
## cb3.RFv3.l1 6.001e-01 8.430e-01 0.712 0.4766
## cb3.RFv3.l2 6.941e-01 6.034e-01 1.150 0.2501
## cb9.minTv1.l1 2.230e-01 3.315e-01 0.673 0.5012
## cb9.minTv1.l2 -1.368e-01 2.427e-01 -0.564 0.5728
## cb9.minTv2.l1 1.280e+00 1.102e+00 1.161 0.2456
## cb9.minTv2.l2 -1.653e+00 8.462e-01 -1.954 0.0507 .
## cb9.minTv3.l1 1.093e+00 6.504e-01 1.681 0.0927 .
## cb9.minTv3.l2 -5.759e-02 4.697e-01 -0.123 0.9024
## cb5.minRHv1.l1 4.187e-02 3.253e-01 0.129 0.8976
## cb5.minRHv1.l2 -5.912e-02 2.235e-01 -0.264 0.7914
## cb5.minRHv2.l1 -1.096e+00 1.175e+00 -0.933 0.3508
## cb5.minRHv2.l2 -7.188e-01 8.666e-01 -0.829 0.4069
## cb5.minRHv3.l1 -1.121e+00 8.395e-01 -1.336 0.1817
## cb5.minRHv3.l2 -9.411e-01 6.323e-01 -1.488 0.1366
## cb2.sunv1.l1 1.029e-01 2.709e-01 0.380 0.7040
## cb2.sunv1.l2 -2.525e-01 1.957e-01 -1.290 0.1971
## cb2.sunv2.l1 1.607e-01 1.112e+00 0.145 0.8851
## cb2.sunv2.l2 -4.048e-01 7.562e-01 -0.535 0.5925
## cb2.sunv3.l1 3.488e-01 4.166e-01 0.837 0.4025
## cb2.sunv3.l2 3.654e-01 2.737e-01 1.335 0.1818
## cb1.avgWindSpv1.l1 2.409e-01 3.878e-01 0.621 0.5345
## cb1.avgWindSpv1.l2 3.244e-01 2.684e-01 1.208 0.2269
## cb1.avgWindSpv2.l1 1.251e+00 8.585e-01 1.457 0.1450
## cb1.avgWindSpv2.l2 1.938e-01 5.959e-01 0.325 0.7451
## cb1.avgWindSpv3.l1 6.656e-01 9.992e-01 0.666 0.5053
## cb1.avgWindSpv3.l2 5.458e-02 6.132e-01 0.089 0.9291
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 -2.086e+06 2.936e+06 -0.710 0.4774
## sp_ptbBM5 1.162e+01 1.406e+01 0.827 0.4084
## sp_ptbBM6 -2.824e+00 2.815e+00 -1.003 0.3158
## sp_ptbBM7 -2.470e-01 2.800e+00 -0.088 0.9297
## sp_ptbBM8 -3.578e+00 2.901e+00 -1.234 0.2174
## sp_ptbBM9 -4.141e-01 2.612e+00 -0.159 0.8740
## sp_ptbBM10 -5.245e+00 2.520e+00 -2.082 0.0374 *
## sp_ptbBM11 -1.090e-01 2.223e+00 -0.049 0.9609
## sp_ptbBM12 -2.903e+00 2.198e+00 -1.321 0.1865
## sp_ptbBM13 -1.927e+00 2.514e+00 -0.766 0.4435
## sp_ptbBM14 -3.557e+00 2.360e+00 -1.507 0.1318
## sp_ptbBM15 -1.687e+00 1.999e+00 -0.844 0.3987
## sp_ptbBM16 -7.819e-01 2.216e+00 -0.353 0.7242
## sp_ptbBM17 -2.073e+00 1.975e+00 -1.049 0.2940
## sp_ptbBM18 -7.235e-01 1.991e+00 -0.363 0.7163
## sp_ptbBM19 -3.008e+00 1.954e+00 -1.540 0.1237
## sp_ptbBM20 -2.049e+00 2.132e+00 -0.961 0.3364
## sp_ptbBM21 -1.361e+00 2.359e+00 -0.577 0.5639
## sp_ptbBM22 -1.287e+00 2.011e+00 -0.640 0.5221
## sp_ptbBM23 -3.226e-01 1.966e+00 -0.164 0.8697
## sp_ptbBM24 -8.270e-01 1.841e+00 -0.449 0.6533
## sp_ptbBM25 3.762e-01 1.573e+00 0.239 0.8111
## sp_ptbBM26 5.213e-01 1.782e+00 0.293 0.7698
## sp_ptbBM27 -1.818e+00 1.981e+00 -0.918 0.3588
## sp_ptbBM28 1.910e-01 2.124e+00 0.090 0.9283
## sp_ptbBM29 -5.580e-01 2.214e+00 -0.252 0.8010
## sp_ptbBM30 1.565e-01 2.052e+00 0.076 0.9392
## sp_ptbBM31 1.119e+00 2.043e+00 0.548 0.5839
## sp_ptbBM32 1.384e-01 2.197e+00 0.063 0.9498
## sp_ptbBM33 8.688e-01 2.215e+00 0.392 0.6949
## sp_ptbBM34 2.650e+00 2.659e+00 0.997 0.3189
## sp_ptbBM35 2.413e+00 2.844e+00 0.849 0.3961
## sp_ptbBM36 3.641e+00 3.195e+00 1.140 0.2545
## sp_ptbBM37 1.736e+00 3.322e+00 0.523 0.6012
## sp_ptbBM38 2.258e+00 3.117e+00 0.725 0.4687
## sp_ptbBM39 9.985e-02 3.089e+00 0.032 0.9742
## sp_ptbBM40 1.581e+00 2.988e+00 0.529 0.5968
## sp_ptbBM41 -1.367e+00 2.752e+00 -0.497 0.6193
## sp_ptbBM42 4.770e-01 2.585e+00 0.185 0.8536
## sp_ptbBM43 -1.148e+00 2.553e+00 -0.450 0.6531
## sp_ptbBM44 -9.788e-01 2.383e+00 -0.411 0.6812
## sp_ptbBM45 -1.225e+00 2.314e+00 -0.530 0.5964
## sp_ptbBM46 -1.470e+00 2.351e+00 -0.625 0.5317
## sp_ptbBM47 -2.709e+00 2.561e+00 -1.058 0.2902
## sp_ptbBM48 -2.173e+00 2.639e+00 -0.824 0.4102
## sp_ptbBM49 -7.541e-01 2.926e+00 -0.258 0.7966
## sp_ptbBM50 -2.469e+00 2.563e+00 -0.963 0.3355
## sp_ptbBM51 5.326e-01 2.555e+00 0.208 0.8349
## sp_ptbBM52 -1.621e+00 2.441e+00 -0.664 0.5067
## sp_ptbBM53 7.631e-01 2.327e+00 0.328 0.7429
## sp_ptbBM54 -1.085e+00 2.298e+00 -0.472 0.6368
## sp_ptbBM55 -1.111e+00 2.106e+00 -0.527 0.5980
## sp_ptbBM56 3.050e-01 2.601e+00 0.117 0.9066
## sp_ptbBM57 3.379e-01 2.211e+00 0.153 0.8785
## sp_ptbBM58 -1.620e-01 2.070e+00 -0.078 0.9376
## sp_ptbBM59 -1.661e+00 1.884e+00 -0.882 0.3780
## sp_ptbBM60 -1.833e+00 1.959e+00 -0.935 0.3496
## sp_ptbBM61 -9.158e-01 1.855e+00 -0.494 0.6215
## sp_ptbBM62 -9.320e-01 2.173e+00 -0.429 0.6680
## sp_ptbBM63 -1.200e+00 2.675e+00 -0.449 0.6537
## sp_ptbBM64 -2.397e+00 3.054e+00 -0.785 0.4326
## sp_ptbBM65 -7.039e-01 3.114e+00 -0.226 0.8212
## sp_ptbBM66 -4.165e+00 3.158e+00 -1.319 0.1872
## sp_ptbBM67 -2.815e+00 3.038e+00 -0.926 0.3542
## sp_ptbBM68 -5.568e+00 3.184e+00 -1.749 0.0803 .
## sp_ptbBM69 -5.490e+00 3.234e+00 -1.698 0.0896 .
## sp_ptbBM70 -7.364e+00 3.104e+00 -2.372 0.0177 *
## sp_ptbBM71 -3.873e+00 2.924e+00 -1.325 0.1853
## sp_ptbBM72 -4.418e+00 2.755e+00 -1.604 0.1088
## sp_ptbBM73 -2.930e+00 2.628e+00 -1.115 0.2649
## sp_ptbBM74 -1.783e+00 2.495e+00 -0.714 0.4750
## sp_ptbBM75 -9.793e-01 2.506e+00 -0.391 0.6960
## sp_ptbBM76 -1.029e-01 2.456e+00 -0.042 0.9666
## sp_ptbBM77 -2.185e+00 2.722e+00 -0.803 0.4221
## sp_ptbBM78 -9.868e-01 2.805e+00 -0.352 0.7249
## sp_ptbBM79 -2.085e+00 2.705e+00 -0.771 0.4408
## sp_ptbBM80 -1.111e+00 2.673e+00 -0.416 0.6777
## sp_ptbBM81 -3.750e+00 2.768e+00 -1.355 0.1754
## sp_ptbBM82 -3.057e+00 2.607e+00 -1.172 0.2410
## sp_ptbBM83 -2.704e+00 2.602e+00 -1.039 0.2987
## sp_ptbBM84 -2.693e+00 2.995e+00 -0.899 0.3686
## sp_ptbBM85 -5.971e+00 3.269e+00 -1.826 0.0678 .
## sp_ptbBM86 -2.903e+00 3.233e+00 -0.898 0.3692
## sp_ptbBM87 -4.038e+00 3.029e+00 -1.333 0.1825
## sp_ptbBM88 -3.022e+00 2.942e+00 -1.027 0.3043
## sp_ptbBM89 -6.263e+00 3.089e+00 -2.028 0.0426 *
## sp_ptbBM90 -3.864e+00 3.135e+00 -1.232 0.2178
## sp_ptbBM91 -2.251e+00 3.201e+00 -0.703 0.4818
## sp_ptbBM92 -1.511e+00 3.117e+00 -0.485 0.6278
## sp_ptbBM93 -1.263e+00 3.132e+00 -0.403 0.6866
## sp_ptbBM94 -4.735e-01 2.994e+00 -0.158 0.8744
## sp_ptbBM95 -1.274e+00 3.030e+00 -0.420 0.6742
## sp_ptbBM96 -3.534e-02 2.985e+00 -0.012 0.9906
## sp_ptbBM97 -1.925e+00 3.340e+00 -0.576 0.5644
## sp_ptbBM98 -2.953e+00 3.499e+00 -0.844 0.3987
## sp_ptbBM99 -2.899e+00 3.414e+00 -0.849 0.3957
## sp_ptbBM100 -1.220e+00 3.448e+00 -0.354 0.7236
## sp_ptbBM101 -1.025e+00 3.243e+00 -0.316 0.7518
## sp_ptbBM102 -8.784e-01 2.923e+00 -0.300 0.7638
## sp_ptbBM103 -1.191e+00 2.927e+00 -0.407 0.6840
## sp_ptbBM104 -3.033e-01 2.714e+00 -0.112 0.9110
## sp_ptbBM105 -5.065e+00 3.598e+00 -1.407 0.1593
## sp_ptbBM106 -7.719e+00 4.569e+00 -1.690 0.0911 .
## sp_ptbBM107 -8.684e+00 5.292e+00 -1.641 0.1008
## sp_ptbBM108 -6.500e+00 4.670e+00 -1.392 0.1640
## sp_ptbBM109 -8.567e+00 5.003e+00 -1.712 0.0868 .
## sp_ptbBM110 -9.737e+00 4.910e+00 -1.983 0.0474 *
## sp_ptbBM111 -8.618e+00 5.436e+00 -1.585 0.1129
## sp_ptbBM112 -7.915e+00 4.583e+00 -1.727 0.0842 .
## sp_ptbBM113 -3.853e+00 3.417e+00 -1.127 0.2596
## sp_ptbBM114 -1.087e+00 2.324e+00 -0.468 0.6398
## sp_ptbBM115 -1.815e+00 2.239e+00 -0.811 0.4176
## sp_ptbBM116 8.398e-01 2.071e+00 0.406 0.6850
## sp_ptbBM117 -2.287e+00 1.974e+00 -1.159 0.2466
## sp_ptbBM118 1.504e+00 1.709e+00 0.880 0.3789
## sp_ptbBM119 -7.013e-01 1.781e+00 -0.394 0.6938
## sp_ptbBM120 -1.444e-01 1.771e+00 -0.082 0.9350
## sp_ptbBM121 3.949e-02 1.483e+00 0.027 0.9788
## sp_ptbBM122 1.648e+00 1.285e+00 1.283 0.1996
## sp_ptbBM123 -4.599e-02 1.238e+00 -0.037 0.9704
## sp_ptbBM124 2.292e+00 1.283e+00 1.786 0.0741 .
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(18565.21) family taken to be 1)
##
## Null deviance: 1073.13 on 886 degrees of freedom
## Residual deviance: 898.46 on 735 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 2907.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 18565
## Std. Err.: 98707
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2601.578pred.fullbm<-predict(mod_fullSA2bm, type="response") #fitted
resid.fullbm<-residuals(mod_fullSA2bm, type="deviance") #residuals deviance
length(pred.fullbm)## [1] 939length(week$smptbBM)## [1] 939length(resid.fullbm)## [1] 939pacf(resid.fullbm,na.action=na.omit) #PACF for residuals, sig lags from 1-6,8-10,12,13,19,25
# SET THE DEFAULT ACTION FOR MISSING DATA TO na.exclude
#ensure that the lags are dplyr lags
mod_fullSA2bm.ac<-update(mod_fullSA2bm,.~.+lag(resid.fullbm,1)+lag(resid.fullbm,2)+lag(resid.fullbm,3)+lag(resid.fullbm,4)+
lag(resid.fullbm,5)+lag(resid.fullbm,6)+lag(resid.fullbm,8)+lag(resid.fullbm,9)+
lag(resid.fullbm,10)+lag(resid.fullbm,12)+lag(resid.fullbm,13)+lag(resid.fullbm,19)+
lag(resid.fullbm,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullSA2bm.ac)##aic=3045##
## Call:
## glm.nb(formula = smptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBM + lag(resid.fullbm, 1) + lag(resid.fullbm,
## 2) + lag(resid.fullbm, 3) + lag(resid.fullbm, 4) + lag(resid.fullbm,
## 5) + lag(resid.fullbm, 6) + lag(resid.fullbm, 8) + lag(resid.fullbm,
## 9) + lag(resid.fullbm, 10) + lag(resid.fullbm, 12) + lag(resid.fullbm,
## 13) + lag(resid.fullbm, 19) + lag(resid.fullbm, 25), data = week,
## init.theta = 27550.77346, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.18529 -0.84253 -0.09727 0.47832 2.25899
##
## Coefficients: (9 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -48.181070 25.484311 -1.891 0.058675 .
## cb3.RFv1.l1 0.162425 0.360768 0.450 0.652551
## cb3.RFv1.l2 0.265317 0.262993 1.009 0.313054
## cb3.RFv2.l1 0.169895 0.577268 0.294 0.768522
## cb3.RFv2.l2 0.944378 0.409483 2.306 0.021096 *
## cb3.RFv3.l1 0.519691 0.891860 0.583 0.560092
## cb3.RFv3.l2 1.296176 0.659989 1.964 0.049537 *
## cb9.minTv1.l1 0.415468 0.343624 1.209 0.226634
## cb9.minTv1.l2 -0.140108 0.253925 -0.552 0.581107
## cb9.minTv2.l1 3.081881 1.179307 2.613 0.008967 **
## cb9.minTv2.l2 -1.222156 0.874914 -1.397 0.162447
## cb9.minTv3.l1 2.013377 0.678649 2.967 0.003010 **
## cb9.minTv3.l2 0.026239 0.479025 0.055 0.956317
## cb5.minRHv1.l1 -0.010066 0.342847 -0.029 0.976577
## cb5.minRHv1.l2 -0.153049 0.229750 -0.666 0.505312
## cb5.minRHv2.l1 -0.472653 1.233013 -0.383 0.701474
## cb5.minRHv2.l2 -1.432145 0.905582 -1.581 0.113772
## cb5.minRHv3.l1 -0.744005 0.897367 -0.829 0.407049
## cb5.minRHv3.l2 -1.159184 0.663796 -1.746 0.080760 .
## cb2.sunv1.l1 0.114230 0.283815 0.402 0.687331
## cb2.sunv1.l2 -0.177473 0.203215 -0.873 0.382487
## cb2.sunv2.l1 0.548884 1.160404 0.473 0.636205
## cb2.sunv2.l2 0.330696 0.793167 0.417 0.676729
## cb2.sunv3.l1 0.058327 0.436878 0.134 0.893791
## cb2.sunv3.l2 0.630874 0.283590 2.225 0.026108 *
## cb1.avgWindSpv1.l1 0.028008 0.409515 0.068 0.945472
## cb1.avgWindSpv1.l2 -0.100858 0.283750 -0.355 0.722255
## cb1.avgWindSpv2.l1 1.026160 0.932998 1.100 0.271397
## cb1.avgWindSpv2.l2 0.214544 0.627634 0.342 0.732479
## cb1.avgWindSpv3.l1 -0.200827 1.120104 -0.179 0.857708
## cb1.avgWindSpv3.l2 0.381423 0.674185 0.566 0.571561
## sp_ptbBM1 NA NA NA NA
## sp_ptbBM2 NA NA NA NA
## sp_ptbBM3 NA NA NA NA
## sp_ptbBM4 NA NA NA NA
## sp_ptbBM5 NA NA NA NA
## sp_ptbBM6 NA NA NA NA
## sp_ptbBM7 NA NA NA NA
## sp_ptbBM8 -72.902465 53.844635 -1.354 0.175755
## sp_ptbBM9 7.630543 5.348970 1.427 0.153711
## sp_ptbBM10 -4.973577 3.099977 -1.604 0.108628
## sp_ptbBM11 2.205507 2.377977 0.927 0.353681
## sp_ptbBM12 -1.427734 2.327162 -0.614 0.539540
## sp_ptbBM13 1.343153 2.628511 0.511 0.609355
## sp_ptbBM14 -3.909740 2.453250 -1.594 0.111004
## sp_ptbBM15 0.197869 2.078662 0.095 0.924164
## sp_ptbBM16 -2.071956 2.266914 -0.914 0.360718
## sp_ptbBM17 -1.130099 2.032135 -0.556 0.578133
## sp_ptbBM18 -1.960556 2.077200 -0.944 0.345249
## sp_ptbBM19 -1.853467 2.023124 -0.916 0.359593
## sp_ptbBM20 -2.632233 2.211641 -1.190 0.233979
## sp_ptbBM21 -1.029910 2.475231 -0.416 0.677347
## sp_ptbBM22 -2.616598 2.068789 -1.265 0.205944
## sp_ptbBM23 0.558112 2.031651 0.275 0.783540
## sp_ptbBM24 -0.403290 1.891997 -0.213 0.831205
## sp_ptbBM25 0.782148 1.609429 0.486 0.626982
## sp_ptbBM26 2.350403 1.867231 1.259 0.208116
## sp_ptbBM27 -1.351471 2.000100 -0.676 0.499230
## sp_ptbBM28 0.354681 2.188543 0.162 0.871257
## sp_ptbBM29 -1.302725 2.245000 -0.580 0.561727
## sp_ptbBM30 1.054521 2.127991 0.496 0.620213
## sp_ptbBM31 0.639975 2.071596 0.309 0.757376
## sp_ptbBM32 2.363726 2.254411 1.048 0.294413
## sp_ptbBM33 -0.559390 2.245877 -0.249 0.803303
## sp_ptbBM34 5.863624 2.736069 2.143 0.032106 *
## sp_ptbBM35 1.759664 2.914306 0.604 0.545975
## sp_ptbBM36 4.254669 3.254094 1.307 0.191049
## sp_ptbBM37 2.066005 3.402133 0.607 0.543673
## sp_ptbBM38 3.245319 3.168202 1.024 0.305674
## sp_ptbBM39 1.169974 3.174042 0.369 0.712421
## sp_ptbBM40 1.898271 3.051714 0.622 0.533919
## sp_ptbBM41 0.247695 2.834378 0.087 0.930362
## sp_ptbBM42 -1.169345 2.633579 -0.444 0.657033
## sp_ptbBM43 1.090852 2.665147 0.409 0.682318
## sp_ptbBM44 -2.417338 2.471679 -0.978 0.328067
## sp_ptbBM45 0.684454 2.416145 0.283 0.776960
## sp_ptbBM46 -0.920028 2.433584 -0.378 0.705390
## sp_ptbBM47 -1.806644 2.645863 -0.683 0.494722
## sp_ptbBM48 -1.180475 2.744390 -0.430 0.667093
## sp_ptbBM49 0.678813 3.007626 0.226 0.821437
## sp_ptbBM50 -1.811419 2.652722 -0.683 0.494700
## sp_ptbBM51 1.705275 2.638470 0.646 0.518077
## sp_ptbBM52 -0.031938 2.494615 -0.013 0.989785
## sp_ptbBM53 3.170827 2.392268 1.325 0.185023
## sp_ptbBM54 0.537332 2.353202 0.228 0.819381
## sp_ptbBM55 0.031687 2.168390 0.015 0.988341
## sp_ptbBM56 0.523768 2.677393 0.196 0.844903
## sp_ptbBM57 0.572895 2.274641 0.252 0.801148
## sp_ptbBM58 -0.243966 2.095753 -0.116 0.907328
## sp_ptbBM59 -1.235466 1.905733 -0.648 0.516798
## sp_ptbBM60 -2.187043 1.995321 -1.096 0.273041
## sp_ptbBM61 -2.905163 1.918133 -1.515 0.129879
## sp_ptbBM62 -1.594016 2.219742 -0.718 0.472690
## sp_ptbBM63 -2.860058 2.754020 -1.039 0.299036
## sp_ptbBM64 -1.190376 3.131381 -0.380 0.703839
## sp_ptbBM65 -3.525827 3.217493 -1.096 0.273153
## sp_ptbBM66 -1.650171 3.292132 -0.501 0.616197
## sp_ptbBM67 -2.900124 3.185430 -0.910 0.362594
## sp_ptbBM68 -3.389714 3.355758 -1.010 0.312438
## sp_ptbBM69 -4.519968 3.393550 -1.332 0.182884
## sp_ptbBM70 -6.513216 3.252324 -2.003 0.045217 *
## sp_ptbBM71 -3.414458 3.126802 -1.092 0.274835
## sp_ptbBM72 -4.277693 2.899422 -1.475 0.140116
## sp_ptbBM73 -5.063093 2.789908 -1.815 0.069556 .
## sp_ptbBM74 -1.849250 2.603373 -0.710 0.477500
## sp_ptbBM75 -4.526854 2.599918 -1.741 0.081657 .
## sp_ptbBM76 1.089706 2.574481 0.423 0.672097
## sp_ptbBM77 -3.548714 2.844098 -1.248 0.212124
## sp_ptbBM78 0.365005 2.947429 0.124 0.901443
## sp_ptbBM79 -2.565489 2.826780 -0.908 0.364108
## sp_ptbBM80 0.323631 2.763539 0.117 0.906775
## sp_ptbBM81 -4.600392 2.895963 -1.589 0.112161
## sp_ptbBM82 -0.974199 2.732078 -0.357 0.721408
## sp_ptbBM83 -3.898726 2.743298 -1.421 0.155264
## sp_ptbBM84 -0.094396 3.128933 -0.030 0.975932
## sp_ptbBM85 -7.577812 3.480545 -2.177 0.029466 *
## sp_ptbBM86 -1.581088 3.366128 -0.470 0.638566
## sp_ptbBM87 -3.987372 3.178286 -1.255 0.209636
## sp_ptbBM88 -2.213552 3.106084 -0.713 0.476062
## sp_ptbBM89 -6.349281 3.274351 -1.939 0.052490 .
## sp_ptbBM90 -3.366317 3.315806 -1.015 0.309995
## sp_ptbBM91 -2.462161 3.373325 -0.730 0.465457
## sp_ptbBM92 -1.998064 3.267782 -0.611 0.540906
## sp_ptbBM93 -1.350303 3.285855 -0.411 0.681113
## sp_ptbBM94 -1.621404 3.133461 -0.517 0.604843
## sp_ptbBM95 -1.414587 3.168411 -0.446 0.655261
## sp_ptbBM96 0.078464 3.148489 0.025 0.980118
## sp_ptbBM97 -2.022037 3.520271 -0.574 0.565698
## sp_ptbBM98 -4.281357 3.694658 -1.159 0.246539
## sp_ptbBM99 -2.054982 3.597578 -0.571 0.567856
## sp_ptbBM100 -1.642418 3.600805 -0.456 0.648300
## sp_ptbBM101 -0.190844 3.374334 -0.057 0.954898
## sp_ptbBM102 -0.229850 3.049239 -0.075 0.939913
## sp_ptbBM103 -0.886489 3.071424 -0.289 0.772869
## sp_ptbBM104 1.497373 2.868321 0.522 0.601644
## sp_ptbBM105 -3.814511 3.765883 -1.013 0.311102
## sp_ptbBM106 -8.643063 4.741660 -1.823 0.068335 .
## sp_ptbBM107 -11.186552 5.359864 -2.087 0.036879 *
## sp_ptbBM108 -10.240159 4.853194 -2.110 0.034860 *
## sp_ptbBM109 -10.208898 5.181304 -1.970 0.048800 *
## sp_ptbBM110 -14.476870 5.101054 -2.838 0.004540 **
## sp_ptbBM111 -9.991480 5.675495 -1.760 0.078330 .
## sp_ptbBM112 -12.850885 4.736910 -2.713 0.006669 **
## sp_ptbBM113 -6.424704 3.606748 -1.781 0.074863 .
## sp_ptbBM114 -1.768848 2.403259 -0.736 0.461718
## sp_ptbBM115 -2.276006 2.348567 -0.969 0.332493
## sp_ptbBM116 -0.065414 2.146390 -0.030 0.975687
## sp_ptbBM117 -1.890932 2.028551 -0.932 0.351254
## sp_ptbBM118 -0.438758 1.762664 -0.249 0.803425
## sp_ptbBM119 0.238460 1.911190 0.125 0.900705
## sp_ptbBM120 -2.548563 1.885565 -1.352 0.176498
## sp_ptbBM121 -0.315440 1.521684 -0.207 0.835778
## sp_ptbBM122 1.305012 1.307032 0.998 0.318059
## sp_ptbBM123 -0.801052 1.271655 -0.630 0.528741
## sp_ptbBM124 2.025362 1.296089 1.563 0.118130
## sp_ptbBM125 NA NA NA NA
## sp_ptbBM126 NA NA NA NA
## lag(resid.fullbm, 1) -0.392552 0.035638 -11.015 < 2e-16 ***
## lag(resid.fullbm, 2) -0.384397 0.037582 -10.228 < 2e-16 ***
## lag(resid.fullbm, 3) -0.422358 0.039157 -10.786 < 2e-16 ***
## lag(resid.fullbm, 4) -0.381769 0.039737 -9.608 < 2e-16 ***
## lag(resid.fullbm, 5) -0.353130 0.038900 -9.078 < 2e-16 ***
## lag(resid.fullbm, 6) -0.241832 0.036367 -6.650 2.93e-11 ***
## lag(resid.fullbm, 8) -0.118254 0.033262 -3.555 0.000378 ***
## lag(resid.fullbm, 9) -0.085983 0.033680 -2.553 0.010683 *
## lag(resid.fullbm, 10) -0.056761 0.032390 -1.752 0.079699 .
## lag(resid.fullbm, 12) 0.010290 0.032327 0.318 0.750237
## lag(resid.fullbm, 13) 0.063879 0.032138 1.988 0.046853 *
## lag(resid.fullbm, 19) -0.005607 0.030209 -0.186 0.852742
## lag(resid.fullbm, 25) -0.038620 0.029812 -1.295 0.195174
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(27550.77) family taken to be 1)
##
## Null deviance: 1033.68 on 861 degrees of freedom
## Residual deviance: 639.45 on 701 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2620.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 27551
## Std. Err.: 106971
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2296.887resid.fullbm.ac<-residuals(mod_fullSA2bm.ac, type="deviance")
pred.fullbm.ac<-predict(mod_fullSA2bm.ac, type="response")
pacf(resid.fullbm.ac,na.action = na.omit) 
length(pred.fullbm.ac)## [1] 939length(resid.fullbm.ac)## [1] 939##plot b4 ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,pred.fullbm,lwd=1, col="dark blue")
plot(week$time,resid.fullbm)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm, week$smptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$smptbBM,type="l")
lines(week$time,pred.fullbm.ac,lwd=1, col="dark blue")
plot(week$time,resid.fullbm.ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm.ac, week$smptbBM)
abline(coef = c(0,1), col="red")
##checking general model fit plot
plot(mod_fullSA2bm)## Warning: not plotting observations with leverage one:
## 53
plot(mod_fullSA2bm.ac)
summary(week$minT)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 21.84 23.49 23.96 23.97 24.40 26.33#1. plotting the dose reponse and slices now for min temperature
SA2predbm.temp <- crosspred(cb9.minT, mod_fullSA2bm.ac,cen = 24.0, by=0.1,cumul=TRUE)
#cumulative effect
plot(SA2predbm.temp, "overall", xlab="Min temperature (?C)",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of min temperature on ptb")
##getting est from SA2predbm.temp
#for 5th % - mat
SA2predbm.temp$matRRfit["22.9","lag0"]; SA2predbm.temp$matRRlow["22.9","lag0"]; SA2predbm.temp$matRRhigh["22.9","lag0"]## [1] 0.8212599## [1] 0.6570365## [1] 1.02653SA2predbm.temp$matRRfit["22.9","lag13"]; SA2predbm.temp$matRRlow["22.9","lag13"]; SA2predbm.temp$matRRhigh["22.9","lag13"]## [1] 0.8539718## [1] 0.7146072## [1] 1.020516SA2predbm.temp$matRRfit["22.9","lag26"]; SA2predbm.temp$matRRlow["22.9","lag26"]; SA2predbm.temp$matRRhigh["22.9","lag26"]## [1] 0.8879867## [1] 0.7553308## [1] 1.043941SA2predbm.temp$matRRfit["22.9","lag39"]; SA2predbm.temp$matRRlow["22.9","lag39"]; SA2predbm.temp$matRRhigh["22.9","lag39"]## [1] 0.9233564## [1] 0.7697961## [1] 1.107549SA2predbm.temp$matRRfit["22.9","lag52"]; SA2predbm.temp$matRRlow["22.9","lag52"]; SA2predbm.temp$matRRhigh["22.9","lag52"]## [1] 0.960135## [1] 0.7635984## [1] 1.207257#for 95th % - mat
SA2predbm.temp$matRRfit["25.1","lag0"]; SA2predbm.temp$matRRlow["25.1","lag0"]; SA2predbm.temp$matRRhigh["25.1","lag0"]## [1] 1.072408## [1] 0.8419419## [1] 1.365961SA2predbm.temp$matRRfit["25.1","lag13"]; SA2predbm.temp$matRRlow["25.1","lag13"]; SA2predbm.temp$matRRhigh["25.1","lag13"]## [1] 1.131484## [1] 0.929412## [1] 1.377491SA2predbm.temp$matRRfit["25.1","lag26"]; SA2predbm.temp$matRRlow["25.1","lag26"]; SA2predbm.temp$matRRhigh["25.1","lag26"]## [1] 1.193815## [1] 0.9968997## [1] 1.429626SA2predbm.temp$matRRfit["25.1","lag27"]; SA2predbm.temp$matRRlow["25.1","lag27"]; SA2predbm.temp$matRRhigh["25.1","lag27"]## [1] 1.198749## [1] 1.00078## [1] 1.43588SA2predbm.temp$matRRfit["25.1","lag28"]; SA2predbm.temp$matRRlow["25.1","lag28"]; SA2predbm.temp$matRRhigh["25.1","lag28"]## [1] 1.203704## [1] 1.004452## [1] 1.442482SA2predbm.temp$matRRfit["25.1","lag29"]; SA2predbm.temp$matRRlow["25.1","lag29"]; SA2predbm.temp$matRRhigh["25.1","lag29"]## [1] 1.20868## [1] 1.007914## [1] 1.449435SA2predbm.temp$matRRfit["25.1","lag30"]; SA2predbm.temp$matRRlow["25.1","lag30"]; SA2predbm.temp$matRRhigh["25.1","lag30"]## [1] 1.213676## [1] 1.011168## [1] 1.45674SA2predbm.temp$matRRfit["25.1","lag31"]; SA2predbm.temp$matRRlow["25.1","lag31"]; SA2predbm.temp$matRRhigh["25.1","lag31"]## [1] 1.218692## [1] 1.014214## [1] 1.464396SA2predbm.temp$matRRfit["25.1","lag32"]; SA2predbm.temp$matRRlow["25.1","lag32"]; SA2predbm.temp$matRRhigh["25.1","lag32"]## [1] 1.22373## [1] 1.017054## [1] 1.472404SA2predbm.temp$matRRfit["25.1","lag33"]; SA2predbm.temp$matRRlow["25.1","lag33"]; SA2predbm.temp$matRRhigh["25.1","lag33"]## [1] 1.228788## [1] 1.019691## [1] 1.480761SA2predbm.temp$matRRfit["25.1","lag34"]; SA2predbm.temp$matRRlow["25.1","lag34"]; SA2predbm.temp$matRRhigh["25.1","lag34"]## [1] 1.233867## [1] 1.022129## [1] 1.489467SA2predbm.temp$matRRfit["25.1","lag35"]; SA2predbm.temp$matRRlow["25.1","lag35"]; SA2predbm.temp$matRRhigh["25.1","lag35"]## [1] 1.238967## [1] 1.024372## [1] 1.498517SA2predbm.temp$matRRfit["25.1","lag36"]; SA2predbm.temp$matRRlow["25.1","lag36"]; SA2predbm.temp$matRRhigh["25.1","lag36"]## [1] 1.244088## [1] 1.026424## [1] 1.50791SA2predbm.temp$matRRfit["25.1","lag37"]; SA2predbm.temp$matRRlow["25.1","lag37"]; SA2predbm.temp$matRRhigh["25.1","lag37"]## [1] 1.24923## [1] 1.028291## [1] 1.51764SA2predbm.temp$matRRfit["25.1","lag38"]; SA2predbm.temp$matRRlow["25.1","lag38"]; SA2predbm.temp$matRRhigh["25.1","lag38"]## [1] 1.254394## [1] 1.029979## [1] 1.527705SA2predbm.temp$matRRfit["25.1","lag39"]; SA2predbm.temp$matRRlow["25.1","lag39"]; SA2predbm.temp$matRRhigh["25.1","lag39"]## [1] 1.259579## [1] 1.031493## [1] 1.538099SA2predbm.temp$matRRfit["25.1","lag52"]; SA2predbm.temp$matRRlow["25.1","lag52"]; SA2predbm.temp$matRRhigh["25.1","lag52"]## [1] 1.328966## [1] 1.038237## [1] 1.701105#2. plotting the dose reponse and slices now for RF
SA2predbm.rf <- crosspred(cb3.RF, mod_fullSA2bm.ac,cen = 44.9, by=0.1,cumul=TRUE)
#cumulative effect of RF
plot(SA2predbm.rf, "overall", xlab="Total rainfall",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of rainfall on ptb")
##getting est from SA2predbm.rf
##10th %
SA2predbm.rf$allRRfit["4.5"]; cbind(SA2predbm.rf$allRRlow,SA2predbm.rf$allRRhigh)["4.5",]## 4.5
## 8.584097## [1] 1.339303e-03 5.501869e+04##90th %
SA2predbm.rf$allRRfit["134.6"]; cbind(SA2predbm.rf$allRRlow,SA2predbm.rf$allRRhigh)["134.6",]## 134.6
## 63.89346## [1] 1.912297e-02 2.134802e+05#for 95th % - mat
SA2predbm.rf$matRRfit["160.7","lag0"]; SA2predbm.rf$matRRlow["160.7","lag0"]; SA2predbm.rf$matRRhigh["160.7","lag0"]## [1] 0.9411438## [1] 0.7396032## [1] 1.197604SA2predbm.rf$matRRfit["160.7","lag13"]; SA2predbm.rf$matRRlow["160.7","lag13"]; SA2predbm.rf$matRRhigh["160.7","lag13"]## [1] 1.02117## [1] 0.84108## [1] 1.239821SA2predbm.rf$matRRfit["160.7","lag26"]; SA2predbm.rf$matRRlow["160.7","lag26"]; SA2predbm.rf$matRRhigh["160.7","lag26"]## [1] 1.108002## [1] 0.9310641## [1] 1.318564SA2predbm.rf$matRRfit["160.7","lag39"]; SA2predbm.rf$matRRlow["160.7","lag39"]; SA2predbm.rf$matRRhigh["160.7","lag39"]## [1] 1.202216## [1] 0.9945605## [1] 1.453228SA2predbm.rf$matRRfit["160.7","lag40"]; SA2predbm.rf$matRRlow["160.7","lag40"]; SA2predbm.rf$matRRhigh["160.7","lag40"]## [1] 1.209787## [1] 0.9982628## [1] 1.466131SA2predbm.rf$matRRfit["160.7","lag41"]; SA2predbm.rf$matRRlow["160.7","lag41"]; SA2predbm.rf$matRRhigh["160.7","lag41"]## [1] 1.217405## [1] 1.001814## [1] 1.479392SA2predbm.rf$matRRfit["160.7","lag42"]; SA2predbm.rf$matRRlow["160.7","lag42"]; SA2predbm.rf$matRRhigh["160.7","lag42"]## [1] 1.225072## [1] 1.005219## [1] 1.493008SA2predbm.rf$matRRfit["160.7","lag52"]; SA2predbm.rf$matRRlow["160.7","lag52"]; SA2predbm.rf$matRRhigh["160.7","lag52"]## [1] 1.304442## [1] 1.032412## [1] 1.648149#3. plotting the dose reponse and slices now for wind
SA2predbm.wind <- crosspred(cb1.avgWindSp, mod_fullSA2bm.ac,cen = 4.5, by=0.1,cumul=TRUE)
#cumulative effect of wind
plot(SA2predbm.wind, "overall", xlab="Av wind speed",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of wind speed on ptb")
#4. plotting the dose reponse and slices now for sun
SA2predbm.sun <- crosspred(cb2.sun, mod_fullSA2bm.ac,cen = 50.7, by=0.1,cumul=TRUE)
#cumulative effect of sun
plot(SA2predbm.sun, "overall", xlab="Total sunshine hours",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of sunshine on ptb")
#5. plotting the dose reponse and slices now for minRH
SA2predbm.minRH <- crosspred(cb5.minRH, mod_fullSA2bm.ac,cen = 63, by=0.1, cumul=TRUE)
#cumulative effect
plot(SA2predbm.minRH, "overall", xlab="Min RH",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of minRH on ptb")
##3d plots for smptbBM version ------
#tiff("fig_3dUniMar22smptbBM.tiff", units="in", width=7, height=4, res=300)
par(mfrow=c(2,3),mar = c(1,1,1,1))
plot(pred.SA2m1a, xlab="Average wind speed", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(pred.SA2m9a, xlab="Minimum Temp", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(pred.SA2m3a, xlab="Total rainfall", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(pred.SA2m2a, xlab="Total sunshine hours", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(pred.SA2m5a, xlab="Min RH", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
dev.off()## null device
## 1##2. the 3d plots for all mod_full model smptbBM SA2 version
#tiff("fig_3dFullMar22smptbBM.tiff", units="in", width=7, height=4, res=300)
par(mfrow=c(2,3),mar = c(1,1,1,1))
plot(SA2predbm.wind, xlab="Average wind speed", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(SA2predbm.temp, xlab="Minimum Temp", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(SA2predbm.rf, xlab="Total rainfall", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(SA2predbm.sun, xlab="Total sunshine hours", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
plot(SA2predbm.minRH, xlab="Min RH", zlab="RR", theta=200, phi=40, lphi=30,cex.axis=0.8,cex.lab=0.9)
dev.off()## null device
## 1###to make lag plots for uni models, smptbBM version SA2 -----
##order: minT, rf, sun, minRH & avgWindSp
#tiff("fig_lagUnismptbBM_SA1_Apr10.tiff", units="in", width=8, height=6, res=300)
par(mfrow=c(5,5),mar=c(2.5,4,2.5,2))
for (i in c(0,13,26,39,52)){
x = crosspred(cb1.avgWindSp, SA2m1a.ac, cen=4.5)
title = paste(c("Lag",i),collapse="-")
plot(x,lag=i,main=title,ylab="RR (Av wind speed)", xlab="", col=4,cex.lab=0.9,
ylim=c(0.9,1.1),xaxt = "n")
axis(1, at= c(3.0,3.6,4.5,6.1,10.2), labels = c("3.0","5th","50th","95th","10.2"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb9.minT, SA2m9a.ac, cen=24.0)
title = paste(c("Lag",i),collapse="-")
plot(x,lag=i,ylab="RR (Min Temp)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.9,1.1),xaxt = "n")
axis(1, at= c(21.8,22.9,24.0,25.1,26.3), labels = c("21.8","5th","50th","95th","26.3"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb3.RF, SA2m3a.ac, cen=44.9)
plot(x,lag=i,ylab="RR (Rainfall)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.9,1.1),xaxt = "n")
axis(1, at= c(0.0,0.8,44.9,160.7,414.5), labels = c("0.0","5th","50th","95th","414.5"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb2.sun, SA2m2a.ac, cen=50.7)
plot(x,lag=i,ylab="RR (Sunshine hours)", xlab="",col=4,cex.lab=0.9,
ylim=c(0.9,1.1),xaxt = "n")
axis(1, at= c(11.2,30.8,50.7,66.1,76.0), labels = c("11.2","5th","50th","95th","76.0"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb5.minRH, SA2m5a.ac, cen=63)
plot(x,lag=i,ylab="RR (Min RH)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.9,1.1),xaxt = "n")
axis(1, at= c(45.0,53.7,63.0,70.3,80.6), labels = c("45.0","5th","50th","95th","80.6"),
cex.axis=0.8)
}
dev.off()## null device
## 1###to make lag plots for full model, smptbBM version, SA2 -----
##order: minT, rf, sun, minRH & avgWindSp
#tiff("fig_lagFullsmptbBM_SA2_Apr7.tiff", units="in", width=10, height=6, res=400)
par(mfrow=c(5,5),mar=c(2.5,4,2.5,2))
#axis(1, at= c(3,3.7,4.5,5.4,10.2)
for (i in c(0,13,26,39,52)){
x = crosspred(cb1.avgWindSp, mod_fullSA2bm.ac, cen=4.5)
title = paste(c(i,"week lag"),collapse=" ")
plot(x,lag=i,main=title,ylab="RR (Av wind speed)", xlab="", col=4,cex.lab=0.9,
ylim=c(0.6,1.1),xaxt = "n")
axis(1, at= c(3.0,3.6,4.5,6.1,10.2), labels = c("3.0","5th","50th","95th","10.2"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb9.minT, mod_fullSA2bm.ac, cen=24.0)
plot(x,lag=i,ylab="RR (Min Temp)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(21.8,22.9,24.0,25.1,26.3), labels = c("21.8","5th","50th","95th","26.3"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb3.RF, mod_fullSA2bm.ac, cen=44.9)
plot(x,lag=i,ylab="RR (Rainfall)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(0.0,0.8,44.9,160.7,414.5), labels = c("0.0","5th","50th","95th","414.5"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb2.sun, mod_fullSA2bm.ac, cen=50.7)
plot(x,lag=i,ylab="RR (Sunshine hours)", xlab="",col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(11.2,30.8,50.7,66.1,76.0), labels = c("11.2","5th","50th","95th","76.0"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb5.minRH, mod_fullSA2bm.ac, cen=63.0)
plot(x,lag=i,ylab="RR (Min RH)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.6,1.2),xaxt = "n")
axis(1, at= c(45.0,53.7,63.0,70.3,80.6), labels = c("45.0","5th","50th","95th","80.6"),
cex.axis=0.8)
}
dev.off()## null device
## 1Sensitivity analysis 1 - using ptbBM & ns5 for long term trend
sp_ptbBMns5 <-ns(week$time,df=18*5)
options(na.action="na.exclude")
SA3m1a <- glm.nb(ptbBM ~ cb1.avgWindSp + sp_ptbBMns5,data=week); summary(SA3m1a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb1.avgWindSp + sp_ptbBMns5, data = week,
## init.theta = 15998.72081, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3731 -0.8041 -0.1177 0.5606 2.8827
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.410e+00 5.482e+00 -1.169 0.2423
## cb1.avgWindSpv1.l1 -1.828e-01 2.455e-01 -0.745 0.4565
## cb1.avgWindSpv1.l2 1.055e-01 1.903e-01 0.554 0.5794
## cb1.avgWindSpv2.l1 9.247e-01 5.839e-01 1.584 0.1133
## cb1.avgWindSpv2.l2 -5.022e-02 3.943e-01 -0.127 0.8986
## cb1.avgWindSpv3.l1 9.489e-01 6.639e-01 1.429 0.1529
## cb1.avgWindSpv3.l2 -2.170e-01 3.726e-01 -0.582 0.5603
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 9.238e+05 5.316e+06 0.174 0.8621
## sp_ptbBMns53 4.988e+00 1.126e+01 0.443 0.6579
## sp_ptbBMns54 -4.860e-01 1.828e+00 -0.266 0.7903
## sp_ptbBMns55 -9.223e-01 1.712e+00 -0.539 0.5901
## sp_ptbBMns56 -2.959e-01 1.450e+00 -0.204 0.8383
## sp_ptbBMns57 -2.155e+00 1.334e+00 -1.616 0.1061
## sp_ptbBMns58 -7.291e-02 1.175e+00 -0.062 0.9505
## sp_ptbBMns59 -4.196e-01 1.292e+00 -0.325 0.7453
## sp_ptbBMns510 -2.212e-01 1.046e+00 -0.211 0.8326
## sp_ptbBMns511 -3.404e-01 7.859e-01 -0.433 0.6649
## sp_ptbBMns512 -5.409e-01 7.636e-01 -0.708 0.4787
## sp_ptbBMns513 5.441e-02 8.107e-01 0.067 0.9465
## sp_ptbBMns514 -5.448e-01 8.370e-01 -0.651 0.5151
## sp_ptbBMns515 9.119e-01 1.114e+00 0.818 0.4131
## sp_ptbBMns516 -5.360e-01 8.456e-01 -0.634 0.5261
## sp_ptbBMns517 -1.529e-01 8.494e-01 -0.180 0.8571
## sp_ptbBMns518 4.412e-01 7.474e-01 0.590 0.5550
## sp_ptbBMns519 -1.697e+00 9.404e-01 -1.805 0.0711 .
## sp_ptbBMns520 2.846e-01 1.160e+00 0.245 0.8063
## sp_ptbBMns521 -1.527e-01 8.581e-01 -0.178 0.8588
## sp_ptbBMns522 4.859e-01 7.500e-01 0.648 0.5171
## sp_ptbBMns523 -1.099e+00 8.500e-01 -1.293 0.1961
## sp_ptbBMns524 -2.742e-01 8.225e-01 -0.333 0.7389
## sp_ptbBMns525 5.687e-01 7.327e-01 0.776 0.4376
## sp_ptbBMns526 -1.091e-02 7.309e-01 -0.015 0.9881
## sp_ptbBMns527 -3.092e-01 9.652e-01 -0.320 0.7487
## sp_ptbBMns528 -1.449e-01 9.208e-01 -0.157 0.8749
## sp_ptbBMns529 -6.590e-01 1.092e+00 -0.603 0.5463
## sp_ptbBMns530 -1.181e+00 1.272e+00 -0.929 0.3531
## sp_ptbBMns531 -9.326e-02 1.297e+00 -0.072 0.9427
## sp_ptbBMns532 7.121e-02 1.241e+00 0.057 0.9542
## sp_ptbBMns533 -7.669e-01 1.342e+00 -0.571 0.5677
## sp_ptbBMns534 6.535e-01 1.541e+00 0.424 0.6715
## sp_ptbBMns535 -6.095e-01 1.394e+00 -0.437 0.6620
## sp_ptbBMns536 4.044e-01 1.212e+00 0.334 0.7386
## sp_ptbBMns537 -6.041e-01 9.923e-01 -0.609 0.5427
## sp_ptbBMns538 1.284e-01 1.026e+00 0.125 0.9004
## sp_ptbBMns539 -8.682e-01 8.979e-01 -0.967 0.3336
## sp_ptbBMns540 -2.882e-02 9.259e-01 -0.031 0.9752
## sp_ptbBMns541 -4.852e-02 8.070e-01 -0.060 0.9521
## sp_ptbBMns542 -1.207e+00 9.043e-01 -1.335 0.1820
## sp_ptbBMns543 5.631e-02 8.879e-01 0.063 0.9494
## sp_ptbBMns544 -7.584e-01 9.488e-01 -0.799 0.4241
## sp_ptbBMns545 1.363e-01 1.271e+00 0.107 0.9147
## sp_ptbBMns546 4.405e-01 1.341e+00 0.329 0.7425
## sp_ptbBMns547 1.372e-01 1.548e+00 0.089 0.9294
## sp_ptbBMns548 1.032e+00 1.661e+00 0.622 0.5342
## sp_ptbBMns549 4.627e-01 1.712e+00 0.270 0.7869
## sp_ptbBMns550 -1.658e-01 1.496e+00 -0.111 0.9118
## sp_ptbBMns551 -5.275e-01 1.536e+00 -0.343 0.7313
## sp_ptbBMns552 -1.093e-01 1.398e+00 -0.078 0.9377
## sp_ptbBMns553 3.088e-01 1.341e+00 0.230 0.8179
## sp_ptbBMns554 -2.315e-01 1.332e+00 -0.174 0.8621
## sp_ptbBMns555 4.619e-01 1.427e+00 0.324 0.7462
## sp_ptbBMns556 5.951e-01 1.308e+00 0.455 0.6490
## sp_ptbBMns557 -1.515e-01 1.403e+00 -0.108 0.9140
## sp_ptbBMns558 3.126e-01 1.410e+00 0.222 0.8246
## sp_ptbBMns559 4.736e-02 1.398e+00 0.034 0.9730
## sp_ptbBMns560 -3.734e-01 1.628e+00 -0.229 0.8187
## sp_ptbBMns561 2.058e-01 1.477e+00 0.139 0.8892
## sp_ptbBMns562 2.632e-01 1.412e+00 0.186 0.8521
## sp_ptbBMns563 -7.274e-01 1.469e+00 -0.495 0.6205
## sp_ptbBMns564 2.430e-02 1.491e+00 0.016 0.9870
## sp_ptbBMns565 -7.381e-02 1.467e+00 -0.050 0.9599
## sp_ptbBMns566 6.050e-01 1.485e+00 0.407 0.6837
## sp_ptbBMns567 -1.299e+00 1.416e+00 -0.917 0.3589
## sp_ptbBMns568 6.827e-01 1.354e+00 0.504 0.6140
## sp_ptbBMns569 -5.432e-01 1.400e+00 -0.388 0.6980
## sp_ptbBMns570 -9.005e-02 1.504e+00 -0.060 0.9522
## sp_ptbBMns571 8.192e-02 1.552e+00 0.053 0.9579
## sp_ptbBMns572 8.998e-01 1.474e+00 0.610 0.5415
## sp_ptbBMns573 1.785e-01 1.125e+00 0.159 0.8740
## sp_ptbBMns574 6.036e-01 1.058e+00 0.570 0.5685
## sp_ptbBMns575 -6.015e-01 1.275e+00 -0.472 0.6371
## sp_ptbBMns576 -9.125e-01 1.173e+00 -0.778 0.4364
## sp_ptbBMns577 -5.685e-01 1.010e+00 -0.563 0.5735
## sp_ptbBMns578 -3.479e-01 1.074e+00 -0.324 0.7461
## sp_ptbBMns579 -7.763e-01 1.263e+00 -0.615 0.5387
## sp_ptbBMns580 -2.881e-01 7.291e-01 -0.395 0.6928
## sp_ptbBMns581 1.915e-01 5.948e-01 0.322 0.7474
## sp_ptbBMns582 -4.676e-01 6.583e-01 -0.710 0.4775
## sp_ptbBMns583 2.895e-01 5.897e-01 0.491 0.6235
## sp_ptbBMns584 1.048e-02 6.081e-01 0.017 0.9863
## sp_ptbBMns585 -3.118e-01 7.502e-01 -0.416 0.6777
## sp_ptbBMns586 2.731e-01 6.453e-01 0.423 0.6722
## sp_ptbBMns587 2.057e-01 4.933e-01 0.417 0.6766
## sp_ptbBMns588 8.003e-01 6.609e-01 1.211 0.2259
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(15998.72) family taken to be 1)
##
## Null deviance: 1101.23 on 886 degrees of freedom
## Residual deviance: 983.15 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3206.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15999
## Std. Err.: 132658
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3016.051SA3m2a <- glm.nb(ptbBM ~ cb2.sun + sp_ptbBMns5,data=week); summary(SA3m2a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb2.sun + sp_ptbBMns5, data = week,
## init.theta = 17573.9797, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3727 -0.8197 -0.1328 0.5694 2.9565
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.466e-01 7.208e+00 0.076 0.93955
## cb2.sunv1.l1 3.279e-02 1.591e-01 0.206 0.83669
## cb2.sunv1.l2 -1.493e-01 1.163e-01 -1.284 0.19914
## cb2.sunv2.l1 2.104e-01 6.288e-01 0.335 0.73785
## cb2.sunv2.l2 2.844e-01 4.338e-01 0.656 0.51209
## cb2.sunv3.l1 6.067e-01 2.355e-01 2.576 0.01000 *
## cb2.sunv3.l2 2.388e-01 1.645e-01 1.452 0.14652
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.212e+06 5.346e+06 0.414 0.67907
## sp_ptbBMns53 -1.541e-01 9.688e+00 -0.016 0.98731
## sp_ptbBMns54 9.027e-01 1.605e+00 0.562 0.57385
## sp_ptbBMns55 4.112e-01 9.267e-01 0.444 0.65726
## sp_ptbBMns56 -7.939e-03 7.519e-01 -0.011 0.99158
## sp_ptbBMns57 -2.044e+00 7.747e-01 -2.639 0.00833 **
## sp_ptbBMns58 7.074e-01 6.737e-01 1.050 0.29370
## sp_ptbBMns59 -1.377e-01 7.843e-01 -0.176 0.86059
## sp_ptbBMns510 -6.452e-01 7.399e-01 -0.872 0.38318
## sp_ptbBMns511 -7.617e-01 7.663e-01 -0.994 0.32023
## sp_ptbBMns512 -5.489e-01 7.468e-01 -0.735 0.46239
## sp_ptbBMns513 -3.688e-01 7.418e-01 -0.497 0.61907
## sp_ptbBMns514 -3.228e-01 7.175e-01 -0.450 0.65282
## sp_ptbBMns515 1.081e+00 7.763e-01 1.392 0.16377
## sp_ptbBMns516 -1.006e-01 7.674e-01 -0.131 0.89571
## sp_ptbBMns517 7.785e-02 7.510e-01 0.104 0.91744
## sp_ptbBMns518 5.355e-01 7.658e-01 0.699 0.48438
## sp_ptbBMns519 -1.444e+00 9.396e-01 -1.537 0.12438
## sp_ptbBMns520 -6.901e-01 8.207e-01 -0.841 0.40044
## sp_ptbBMns521 -2.008e-01 7.900e-01 -0.254 0.79937
## sp_ptbBMns522 1.690e-01 7.562e-01 0.224 0.82311
## sp_ptbBMns523 -9.989e-01 8.239e-01 -1.212 0.22539
## sp_ptbBMns524 4.373e-01 7.997e-01 0.547 0.58455
## sp_ptbBMns525 1.522e+00 8.542e-01 1.782 0.07480 .
## sp_ptbBMns526 1.085e+00 8.463e-01 1.282 0.19981
## sp_ptbBMns527 8.456e-01 7.990e-01 1.058 0.28992
## sp_ptbBMns528 1.188e+00 8.714e-01 1.363 0.17296
## sp_ptbBMns529 7.675e-01 8.081e-01 0.950 0.34222
## sp_ptbBMns530 -1.263e-01 8.160e-01 -0.155 0.87696
## sp_ptbBMns531 -6.384e-02 6.613e-01 -0.097 0.92309
## sp_ptbBMns532 -2.672e-01 6.477e-01 -0.412 0.67999
## sp_ptbBMns533 -1.145e+00 6.847e-01 -1.672 0.09444 .
## sp_ptbBMns534 3.953e-01 6.925e-01 0.571 0.56813
## sp_ptbBMns535 -6.465e-01 8.014e-01 -0.807 0.41985
## sp_ptbBMns536 8.034e-01 6.995e-01 1.149 0.25072
## sp_ptbBMns537 -3.413e-01 6.871e-01 -0.497 0.61937
## sp_ptbBMns538 3.942e-01 6.580e-01 0.599 0.54906
## sp_ptbBMns539 -5.521e-01 6.729e-01 -0.820 0.41195
## sp_ptbBMns540 1.110e+00 8.786e-01 1.263 0.20661
## sp_ptbBMns541 -1.339e-01 7.262e-01 -0.184 0.85367
## sp_ptbBMns542 -5.081e-01 7.239e-01 -0.702 0.48276
## sp_ptbBMns543 2.770e-01 7.407e-01 0.374 0.70846
## sp_ptbBMns544 -6.001e-01 7.665e-01 -0.783 0.43366
## sp_ptbBMns545 -1.194e+00 8.621e-01 -1.386 0.16590
## sp_ptbBMns546 -6.228e-02 7.106e-01 -0.088 0.93016
## sp_ptbBMns547 -8.362e-01 6.801e-01 -1.230 0.21888
## sp_ptbBMns548 4.391e-01 6.161e-01 0.713 0.47604
## sp_ptbBMns549 -2.848e-01 7.340e-01 -0.388 0.69799
## sp_ptbBMns550 8.935e-01 9.347e-01 0.956 0.33907
## sp_ptbBMns551 8.199e-01 8.302e-01 0.988 0.32337
## sp_ptbBMns552 1.049e+00 8.662e-01 1.212 0.22569
## sp_ptbBMns553 1.459e+00 8.053e-01 1.812 0.07003 .
## sp_ptbBMns554 8.031e-01 8.269e-01 0.971 0.33145
## sp_ptbBMns555 1.324e+00 8.233e-01 1.608 0.10776
## sp_ptbBMns556 1.214e+00 7.321e-01 1.658 0.09733 .
## sp_ptbBMns557 -3.406e-01 8.187e-01 -0.416 0.67743
## sp_ptbBMns558 -2.044e-01 7.356e-01 -0.278 0.78113
## sp_ptbBMns559 -1.811e-01 7.130e-01 -0.254 0.79949
## sp_ptbBMns560 -9.123e-01 7.533e-01 -1.211 0.22587
## sp_ptbBMns561 -3.871e-01 7.996e-01 -0.484 0.62836
## sp_ptbBMns562 1.216e+00 7.331e-01 1.660 0.09701 .
## sp_ptbBMns563 8.952e-03 8.632e-01 0.010 0.99173
## sp_ptbBMns564 1.174e+00 8.815e-01 1.332 0.18294
## sp_ptbBMns565 8.050e-01 8.764e-01 0.918 0.35837
## sp_ptbBMns566 1.238e+00 7.411e-01 1.670 0.09492 .
## sp_ptbBMns567 -4.519e-01 7.913e-01 -0.571 0.56796
## sp_ptbBMns568 1.766e+00 6.697e-01 2.637 0.00836 **
## sp_ptbBMns569 7.331e-01 7.185e-01 1.020 0.30753
## sp_ptbBMns570 1.288e+00 8.950e-01 1.439 0.15025
## sp_ptbBMns571 6.955e-01 7.233e-01 0.962 0.33626
## sp_ptbBMns572 5.884e-01 6.804e-01 0.865 0.38716
## sp_ptbBMns573 2.847e-01 6.928e-01 0.411 0.68109
## sp_ptbBMns574 -1.214e-01 6.682e-01 -0.182 0.85583
## sp_ptbBMns575 -1.218e+00 8.415e-01 -1.447 0.14793
## sp_ptbBMns576 -1.260e+00 7.272e-01 -1.732 0.08324 .
## sp_ptbBMns577 -1.630e+00 8.056e-01 -2.024 0.04301 *
## sp_ptbBMns578 -7.450e-01 8.249e-01 -0.903 0.36647
## sp_ptbBMns579 -8.901e-01 7.907e-01 -1.126 0.26026
## sp_ptbBMns580 9.987e-01 7.041e-01 1.418 0.15608
## sp_ptbBMns581 6.099e-01 6.560e-01 0.930 0.35246
## sp_ptbBMns582 7.729e-01 7.250e-01 1.066 0.28637
## sp_ptbBMns583 8.030e-01 6.669e-01 1.204 0.22854
## sp_ptbBMns584 1.098e+00 7.561e-01 1.452 0.14658
## sp_ptbBMns585 -1.908e-02 8.283e-01 -0.023 0.98162
## sp_ptbBMns586 3.675e-01 6.717e-01 0.547 0.58431
## sp_ptbBMns587 6.609e-01 5.501e-01 1.201 0.22963
## sp_ptbBMns588 1.401e+00 6.712e-01 2.088 0.03682 *
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17573.98) family taken to be 1)
##
## Null deviance: 1101.24 on 886 degrees of freedom
## Residual deviance: 972.32 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3195.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17574
## Std. Err.: 134180
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3005.203SA3m3a <- glm.nb(ptbBM ~ cb3.RF + sp_ptbBMns5,data=week); summary(SA3m3a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + sp_ptbBMns5, data = week, init.theta = 15595.28843,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3932 -0.8221 -0.1343 0.5622 2.8897
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.847e+00 2.141e+00 0.863 0.3884
## cb3.RFv1.l1 8.706e-02 2.229e-01 0.391 0.6961
## cb3.RFv1.l2 1.256e-01 1.419e-01 0.885 0.3760
## cb3.RFv2.l1 -1.372e-01 3.107e-01 -0.442 0.6588
## cb3.RFv2.l2 1.504e-01 2.034e-01 0.739 0.4598
## cb3.RFv3.l1 -5.048e-02 5.084e-01 -0.099 0.9209
## cb3.RFv3.l2 7.432e-02 3.526e-01 0.211 0.8331
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.303e+06 5.361e+06 0.430 0.6675
## sp_ptbBMns53 -2.913e+00 9.759e+00 -0.298 0.7653
## sp_ptbBMns54 1.060e+00 1.609e+00 0.659 0.5102
## sp_ptbBMns55 -4.793e-01 9.862e-01 -0.486 0.6269
## sp_ptbBMns56 5.063e-01 7.873e-01 0.643 0.5201
## sp_ptbBMns57 -1.552e+00 8.352e-01 -1.858 0.0632 .
## sp_ptbBMns58 7.621e-01 6.759e-01 1.128 0.2595
## sp_ptbBMns59 7.744e-01 8.081e-01 0.958 0.3379
## sp_ptbBMns510 -1.267e-01 7.456e-01 -0.170 0.8650
## sp_ptbBMns511 9.749e-02 7.648e-01 0.127 0.8986
## sp_ptbBMns512 1.376e-01 7.872e-01 0.175 0.8612
## sp_ptbBMns513 2.145e-01 7.341e-01 0.292 0.7701
## sp_ptbBMns514 4.553e-01 8.508e-01 0.535 0.5926
## sp_ptbBMns515 9.638e-01 7.553e-01 1.276 0.2019
## sp_ptbBMns516 -1.417e-01 7.573e-01 -0.187 0.8516
## sp_ptbBMns517 -7.015e-02 8.071e-01 -0.087 0.9307
## sp_ptbBMns518 7.806e-01 7.900e-01 0.988 0.3231
## sp_ptbBMns519 -1.026e+00 9.774e-01 -1.050 0.2938
## sp_ptbBMns520 2.553e-01 8.305e-01 0.307 0.7585
## sp_ptbBMns521 2.482e-01 8.192e-01 0.303 0.7619
## sp_ptbBMns522 6.761e-01 6.885e-01 0.982 0.3261
## sp_ptbBMns523 -7.674e-01 8.009e-01 -0.958 0.3380
## sp_ptbBMns524 4.873e-01 7.673e-01 0.635 0.5254
## sp_ptbBMns525 5.423e-01 6.345e-01 0.855 0.3927
## sp_ptbBMns526 5.333e-01 7.360e-01 0.725 0.4687
## sp_ptbBMns527 2.610e-01 6.860e-01 0.381 0.7036
## sp_ptbBMns528 2.669e-01 7.288e-01 0.366 0.7142
## sp_ptbBMns529 4.667e-01 7.230e-01 0.646 0.5186
## sp_ptbBMns530 -4.013e-01 7.624e-01 -0.526 0.5986
## sp_ptbBMns531 5.814e-01 6.638e-01 0.876 0.3811
## sp_ptbBMns532 6.771e-01 7.563e-01 0.895 0.3706
## sp_ptbBMns533 -4.642e-01 6.570e-01 -0.707 0.4799
## sp_ptbBMns534 8.015e-01 6.543e-01 1.225 0.2206
## sp_ptbBMns535 7.315e-02 7.319e-01 0.100 0.9204
## sp_ptbBMns536 9.969e-01 6.782e-01 1.470 0.1416
## sp_ptbBMns537 2.986e-01 7.405e-01 0.403 0.6868
## sp_ptbBMns538 5.245e-01 6.884e-01 0.762 0.4461
## sp_ptbBMns539 -1.791e-01 7.238e-01 -0.247 0.8046
## sp_ptbBMns540 6.900e-01 1.022e+00 0.675 0.4996
## sp_ptbBMns541 1.643e-01 7.149e-01 0.230 0.8183
## sp_ptbBMns542 -1.034e+00 8.198e-01 -1.261 0.2072
## sp_ptbBMns543 1.627e-01 7.484e-01 0.217 0.8279
## sp_ptbBMns544 -3.629e-01 8.775e-01 -0.414 0.6792
## sp_ptbBMns545 3.725e-01 1.044e+00 0.357 0.7213
## sp_ptbBMns546 7.102e-01 7.885e-01 0.901 0.3677
## sp_ptbBMns547 6.216e-01 7.737e-01 0.803 0.4217
## sp_ptbBMns548 9.988e-01 7.273e-01 1.373 0.1697
## sp_ptbBMns549 9.101e-01 7.441e-01 1.223 0.2213
## sp_ptbBMns550 -4.083e-02 8.740e-01 -0.047 0.9627
## sp_ptbBMns551 -4.631e-01 7.978e-01 -0.580 0.5616
## sp_ptbBMns552 -2.041e-01 6.354e-01 -0.321 0.7480
## sp_ptbBMns553 4.695e-01 6.637e-01 0.707 0.4794
## sp_ptbBMns554 4.446e-03 7.074e-01 0.006 0.9950
## sp_ptbBMns555 9.562e-01 7.475e-01 1.279 0.2008
## sp_ptbBMns556 1.237e+00 8.063e-01 1.534 0.1251
## sp_ptbBMns557 1.856e-01 7.755e-01 0.239 0.8109
## sp_ptbBMns558 4.637e-01 7.323e-01 0.633 0.5266
## sp_ptbBMns559 4.951e-01 6.804e-01 0.728 0.4668
## sp_ptbBMns560 -1.756e-01 7.515e-01 -0.234 0.8152
## sp_ptbBMns561 6.378e-01 6.420e-01 0.993 0.3205
## sp_ptbBMns562 7.228e-01 6.668e-01 1.084 0.2784
## sp_ptbBMns563 -6.946e-01 6.992e-01 -0.993 0.3205
## sp_ptbBMns564 8.190e-01 7.479e-01 1.095 0.2735
## sp_ptbBMns565 -2.495e-02 7.457e-01 -0.033 0.9733
## sp_ptbBMns566 7.215e-01 6.955e-01 1.037 0.2996
## sp_ptbBMns567 -6.392e-01 8.307e-01 -0.769 0.4416
## sp_ptbBMns568 8.859e-01 6.168e-01 1.436 0.1509
## sp_ptbBMns569 1.281e-01 7.461e-01 0.172 0.8637
## sp_ptbBMns570 7.129e-01 8.445e-01 0.844 0.3986
## sp_ptbBMns571 1.857e-01 7.798e-01 0.238 0.8117
## sp_ptbBMns572 8.178e-01 6.616e-01 1.236 0.2164
## sp_ptbBMns573 -9.975e-03 7.012e-01 -0.014 0.9887
## sp_ptbBMns574 6.764e-01 6.670e-01 1.014 0.3105
## sp_ptbBMns575 1.357e-01 9.146e-01 0.148 0.8821
## sp_ptbBMns576 -3.685e-01 6.712e-01 -0.549 0.5830
## sp_ptbBMns577 3.210e-01 7.836e-01 0.410 0.6821
## sp_ptbBMns578 3.797e-01 6.372e-01 0.596 0.5512
## sp_ptbBMns579 3.907e-01 8.332e-01 0.469 0.6391
## sp_ptbBMns580 4.818e-01 7.383e-01 0.653 0.5140
## sp_ptbBMns581 8.058e-01 6.633e-01 1.215 0.2244
## sp_ptbBMns582 4.439e-01 7.217e-01 0.615 0.5384
## sp_ptbBMns583 6.840e-01 6.367e-01 1.074 0.2827
## sp_ptbBMns584 8.207e-01 7.229e-01 1.135 0.2563
## sp_ptbBMns585 -1.039e-01 8.041e-01 -0.129 0.8972
## sp_ptbBMns586 4.408e-01 6.461e-01 0.682 0.4951
## sp_ptbBMns587 2.259e-01 5.833e-01 0.387 0.6986
## sp_ptbBMns588 4.589e-01 6.659e-01 0.689 0.4907
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(15595.29) family taken to be 1)
##
## Null deviance: 1101.22 on 886 degrees of freedom
## Residual deviance: 983.64 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3206.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15595
## Std. Err.: 130089
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3016.537SA3m5a <- glm.nb(ptbBM ~ cb5.minRH + sp_ptbBMns5,data=week); summary(SA3m5a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBMns5, data = week,
## init.theta = 15825.53811, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4178 -0.8148 -0.1139 0.5813 2.9998
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.414e+01 9.125e+00 1.550 0.12121
## cb5.minRHv1.l1 -3.329e-01 2.014e-01 -1.654 0.09822 .
## cb5.minRHv1.l2 -9.099e-03 1.201e-01 -0.076 0.93963
## cb5.minRHv2.l1 -1.161e+00 7.819e-01 -1.485 0.13743
## cb5.minRHv2.l2 -3.286e-01 5.247e-01 -0.626 0.53108
## cb5.minRHv3.l1 -6.686e-01 4.523e-01 -1.478 0.13939
## cb5.minRHv3.l2 -1.229e-01 2.934e-01 -0.419 0.67518
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.227e+06 5.339e+06 0.417 0.67667
## sp_ptbBMns53 -1.952e+00 9.664e+00 -0.202 0.83996
## sp_ptbBMns54 8.671e-01 1.617e+00 0.536 0.59184
## sp_ptbBMns55 -2.462e-01 9.454e-01 -0.260 0.79458
## sp_ptbBMns56 3.776e-01 8.730e-01 0.433 0.66534
## sp_ptbBMns57 -2.197e+00 8.272e-01 -2.656 0.00791 **
## sp_ptbBMns58 -2.096e-01 7.209e-01 -0.291 0.77119
## sp_ptbBMns59 -8.091e-01 8.039e-01 -1.006 0.31421
## sp_ptbBMns510 -1.143e+00 8.936e-01 -1.279 0.20086
## sp_ptbBMns511 -1.552e+00 1.051e+00 -1.477 0.13962
## sp_ptbBMns512 -1.075e+00 8.438e-01 -1.274 0.20255
## sp_ptbBMns513 -7.352e-01 8.327e-01 -0.883 0.37726
## sp_ptbBMns514 -9.332e-01 9.105e-01 -1.025 0.30542
## sp_ptbBMns515 1.000e+00 8.129e-01 1.230 0.21863
## sp_ptbBMns516 -3.255e-01 7.961e-01 -0.409 0.68260
## sp_ptbBMns517 -4.119e-01 7.143e-01 -0.577 0.56414
## sp_ptbBMns518 3.106e-01 6.959e-01 0.446 0.65538
## sp_ptbBMns519 -1.767e+00 7.707e-01 -2.293 0.02186 *
## sp_ptbBMns520 -2.331e-02 8.041e-01 -0.029 0.97687
## sp_ptbBMns521 -8.950e-01 7.965e-01 -1.124 0.26111
## sp_ptbBMns522 -8.213e-01 1.000e+00 -0.821 0.41148
## sp_ptbBMns523 -2.865e+00 1.178e+00 -2.432 0.01502 *
## sp_ptbBMns524 -1.965e+00 1.059e+00 -1.856 0.06344 .
## sp_ptbBMns525 -1.951e+00 1.358e+00 -1.437 0.15079
## sp_ptbBMns526 -1.340e+00 1.098e+00 -1.220 0.22249
## sp_ptbBMns527 -1.014e+00 9.507e-01 -1.067 0.28603
## sp_ptbBMns528 -5.400e-01 8.572e-01 -0.630 0.52871
## sp_ptbBMns529 -6.991e-01 8.506e-01 -0.822 0.41113
## sp_ptbBMns530 -1.033e+00 8.364e-01 -1.235 0.21700
## sp_ptbBMns531 -3.802e-01 7.927e-01 -0.480 0.63148
## sp_ptbBMns532 -4.809e-01 7.305e-01 -0.658 0.51035
## sp_ptbBMns533 -1.364e+00 8.241e-01 -1.655 0.09783 .
## sp_ptbBMns534 -4.898e-01 7.179e-01 -0.682 0.49509
## sp_ptbBMns535 -1.190e+00 9.089e-01 -1.309 0.19060
## sp_ptbBMns536 -4.386e-01 8.215e-01 -0.534 0.59337
## sp_ptbBMns537 -1.064e+00 8.104e-01 -1.313 0.18926
## sp_ptbBMns538 -2.890e-01 7.425e-01 -0.389 0.69712
## sp_ptbBMns539 -1.218e+00 7.792e-01 -1.563 0.11795
## sp_ptbBMns540 3.989e-01 9.292e-01 0.429 0.66769
## sp_ptbBMns541 -3.427e-01 7.371e-01 -0.465 0.64193
## sp_ptbBMns542 -1.668e+00 9.568e-01 -1.743 0.08127 .
## sp_ptbBMns543 -8.418e-01 9.407e-01 -0.895 0.37088
## sp_ptbBMns544 -1.592e+00 8.472e-01 -1.879 0.06028 .
## sp_ptbBMns545 -1.548e+00 1.239e+00 -1.250 0.21135
## sp_ptbBMns546 -1.195e+00 1.012e+00 -1.181 0.23742
## sp_ptbBMns547 -4.381e-01 7.003e-01 -0.626 0.53152
## sp_ptbBMns548 2.059e-01 5.566e-01 0.370 0.71142
## sp_ptbBMns549 -3.266e-01 6.453e-01 -0.506 0.61273
## sp_ptbBMns550 -2.425e-01 8.999e-01 -0.269 0.78757
## sp_ptbBMns551 -5.284e-01 7.463e-01 -0.708 0.47892
## sp_ptbBMns552 -2.186e-01 6.778e-01 -0.322 0.74710
## sp_ptbBMns553 6.017e-02 7.031e-01 0.086 0.93181
## sp_ptbBMns554 -2.981e-01 5.869e-01 -0.508 0.61151
## sp_ptbBMns555 1.993e-01 8.621e-01 0.231 0.81722
## sp_ptbBMns556 6.383e-01 7.610e-01 0.839 0.40160
## sp_ptbBMns557 -1.050e+00 8.360e-01 -1.256 0.20926
## sp_ptbBMns558 -2.767e-01 6.268e-01 -0.441 0.65886
## sp_ptbBMns559 -4.447e-01 7.019e-01 -0.633 0.52641
## sp_ptbBMns560 -6.508e-01 8.602e-01 -0.757 0.44927
## sp_ptbBMns561 -3.764e-02 9.179e-01 -0.041 0.96730
## sp_ptbBMns562 7.099e-01 6.727e-01 1.055 0.29125
## sp_ptbBMns563 -7.556e-01 7.202e-01 -1.049 0.29408
## sp_ptbBMns564 5.117e-01 7.303e-01 0.701 0.48357
## sp_ptbBMns565 9.909e-01 1.245e+00 0.796 0.42608
## sp_ptbBMns566 1.493e+00 8.836e-01 1.689 0.09118 .
## sp_ptbBMns567 8.977e-03 9.261e-01 0.010 0.99227
## sp_ptbBMns568 2.039e+00 8.855e-01 2.303 0.02127 *
## sp_ptbBMns569 1.211e+00 1.061e+00 1.141 0.25396
## sp_ptbBMns570 1.079e+00 1.091e+00 0.989 0.32256
## sp_ptbBMns571 1.525e+00 8.927e-01 1.708 0.08767 .
## sp_ptbBMns572 1.554e+00 8.162e-01 1.904 0.05696 .
## sp_ptbBMns573 1.139e+00 8.290e-01 1.374 0.16955
## sp_ptbBMns574 9.574e-01 6.830e-01 1.402 0.16095
## sp_ptbBMns575 8.485e-01 8.559e-01 0.991 0.32149
## sp_ptbBMns576 4.891e-01 6.837e-01 0.715 0.47443
## sp_ptbBMns577 6.335e-01 7.051e-01 0.899 0.36892
## sp_ptbBMns578 4.844e-01 5.988e-01 0.809 0.41858
## sp_ptbBMns579 4.633e-01 5.886e-01 0.787 0.43118
## sp_ptbBMns580 1.226e-01 6.534e-01 0.188 0.85113
## sp_ptbBMns581 8.279e-01 7.745e-01 1.069 0.28507
## sp_ptbBMns582 1.979e-01 6.703e-01 0.295 0.76783
## sp_ptbBMns583 1.039e+00 7.758e-01 1.339 0.18051
## sp_ptbBMns584 7.137e-01 6.704e-01 1.065 0.28709
## sp_ptbBMns585 4.546e-01 8.681e-01 0.524 0.60051
## sp_ptbBMns586 9.236e-01 8.327e-01 1.109 0.26738
## sp_ptbBMns587 8.453e-01 6.350e-01 1.331 0.18318
## sp_ptbBMns588 5.890e-01 6.610e-01 0.891 0.37288
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(15825.54) family taken to be 1)
##
## Null deviance: 1101.22 on 886 degrees of freedom
## Residual deviance: 981.24 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3204.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15826
## Std. Err.: 130488
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3014.141SA3m6a <- glm.nb(ptbBM ~ cb6.meanRH + sp_ptbBMns5,data=week); summary(SA3m6a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb6.meanRH + sp_ptbBMns5, data = week,
## init.theta = 15555.69816, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4008 -0.8285 -0.1313 0.5538 2.9451
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.053e+01 1.139e+01 0.925 0.3552
## cb6.meanRHv1.l1 -2.417e-01 2.494e-01 -0.969 0.3326
## cb6.meanRHv1.l2 -3.576e-02 1.509e-01 -0.237 0.8127
## cb6.meanRHv2.l1 -8.063e-01 9.213e-01 -0.875 0.3815
## cb6.meanRHv2.l2 6.489e-04 5.680e-01 0.001 0.9991
## cb6.meanRHv3.l1 -4.197e-01 3.665e-01 -1.145 0.2521
## cb6.meanRHv3.l2 1.985e-01 2.421e-01 0.820 0.4122
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.328e+06 5.326e+06 0.437 0.6620
## sp_ptbBMns53 -2.466e+00 9.638e+00 -0.256 0.7981
## sp_ptbBMns54 9.266e-01 1.654e+00 0.560 0.5754
## sp_ptbBMns55 -3.479e-01 9.083e-01 -0.383 0.7017
## sp_ptbBMns56 1.706e-01 8.755e-01 0.195 0.8455
## sp_ptbBMns57 -1.961e+00 8.408e-01 -2.332 0.0197 *
## sp_ptbBMns58 -2.259e-02 6.945e-01 -0.033 0.9741
## sp_ptbBMns59 -6.700e-02 9.457e-01 -0.071 0.9435
## sp_ptbBMns510 -5.417e-01 8.698e-01 -0.623 0.5334
## sp_ptbBMns511 -7.369e-01 1.010e+00 -0.730 0.4656
## sp_ptbBMns512 -5.666e-01 8.461e-01 -0.670 0.5031
## sp_ptbBMns513 -4.559e-01 9.009e-01 -0.506 0.6128
## sp_ptbBMns514 -1.656e-01 1.000e+00 -0.166 0.8685
## sp_ptbBMns515 5.524e-01 7.680e-01 0.719 0.4720
## sp_ptbBMns516 -1.460e+00 1.221e+00 -1.196 0.2317
## sp_ptbBMns517 -1.916e+00 1.782e+00 -1.075 0.2824
## sp_ptbBMns518 -2.029e+00 2.236e+00 -0.907 0.3644
## sp_ptbBMns519 -4.159e+00 2.429e+00 -1.712 0.0869 .
## sp_ptbBMns520 -2.417e+00 2.431e+00 -0.994 0.3201
## sp_ptbBMns521 -2.528e+00 2.397e+00 -1.055 0.2916
## sp_ptbBMns522 -2.116e+00 2.409e+00 -0.878 0.3798
## sp_ptbBMns523 -3.448e+00 2.486e+00 -1.387 0.1654
## sp_ptbBMns524 -2.652e+00 2.264e+00 -1.171 0.2416
## sp_ptbBMns525 -2.615e+00 2.658e+00 -0.984 0.3253
## sp_ptbBMns526 -1.849e+00 1.964e+00 -0.941 0.3466
## sp_ptbBMns527 -1.548e+00 1.741e+00 -0.889 0.3742
## sp_ptbBMns528 -9.276e-01 1.328e+00 -0.698 0.4850
## sp_ptbBMns529 -9.305e-01 1.228e+00 -0.758 0.4486
## sp_ptbBMns530 -1.262e+00 9.497e-01 -1.329 0.1840
## sp_ptbBMns531 -6.492e-01 1.121e+00 -0.579 0.5626
## sp_ptbBMns532 -6.795e-01 1.144e+00 -0.594 0.5524
## sp_ptbBMns533 -1.625e+00 1.270e+00 -1.280 0.2005
## sp_ptbBMns534 -7.177e-01 1.240e+00 -0.579 0.5629
## sp_ptbBMns535 -1.074e+00 1.266e+00 -0.849 0.3960
## sp_ptbBMns536 1.456e-02 1.027e+00 0.014 0.9887
## sp_ptbBMns537 -7.781e-01 9.545e-01 -0.815 0.4149
## sp_ptbBMns538 -1.078e-02 8.560e-01 -0.013 0.9899
## sp_ptbBMns539 -9.242e-01 7.565e-01 -1.222 0.2218
## sp_ptbBMns540 8.336e-01 9.224e-01 0.904 0.3661
## sp_ptbBMns541 2.656e-02 8.141e-01 0.033 0.9740
## sp_ptbBMns542 -1.474e+00 1.144e+00 -1.288 0.1977
## sp_ptbBMns543 -6.796e-01 1.107e+00 -0.614 0.5391
## sp_ptbBMns544 -1.715e+00 1.016e+00 -1.688 0.0914 .
## sp_ptbBMns545 -9.547e-01 1.432e+00 -0.667 0.5049
## sp_ptbBMns546 -8.544e-01 1.229e+00 -0.695 0.4871
## sp_ptbBMns547 -1.446e-01 8.955e-01 -0.161 0.8717
## sp_ptbBMns548 2.928e-01 6.982e-01 0.419 0.6750
## sp_ptbBMns549 9.591e-02 7.801e-01 0.123 0.9022
## sp_ptbBMns550 2.763e-01 9.408e-01 0.294 0.7690
## sp_ptbBMns551 -2.779e-01 8.194e-01 -0.339 0.7345
## sp_ptbBMns552 -5.170e-02 6.754e-01 -0.077 0.9390
## sp_ptbBMns553 6.262e-02 6.838e-01 0.092 0.9270
## sp_ptbBMns554 -4.149e-01 5.980e-01 -0.694 0.4878
## sp_ptbBMns555 6.089e-01 8.224e-01 0.740 0.4590
## sp_ptbBMns556 7.200e-01 8.096e-01 0.889 0.3738
## sp_ptbBMns557 -6.353e-01 8.844e-01 -0.718 0.4725
## sp_ptbBMns558 -2.297e-01 7.100e-01 -0.324 0.7463
## sp_ptbBMns559 -2.937e-01 7.671e-01 -0.383 0.7018
## sp_ptbBMns560 -7.680e-01 8.682e-01 -0.885 0.3764
## sp_ptbBMns561 -1.720e-01 9.941e-01 -0.173 0.8626
## sp_ptbBMns562 5.260e-01 7.328e-01 0.718 0.4729
## sp_ptbBMns563 -1.079e+00 8.672e-01 -1.245 0.2133
## sp_ptbBMns564 6.760e-01 8.255e-01 0.819 0.4129
## sp_ptbBMns565 1.112e+00 1.020e+00 1.090 0.2758
## sp_ptbBMns566 1.018e+00 6.937e-01 1.468 0.1422
## sp_ptbBMns567 -5.427e-01 7.234e-01 -0.750 0.4531
## sp_ptbBMns568 8.305e-01 7.132e-01 1.164 0.2443
## sp_ptbBMns569 2.612e-01 7.396e-01 0.353 0.7239
## sp_ptbBMns570 1.160e+00 9.630e-01 1.205 0.2284
## sp_ptbBMns571 9.090e-01 7.093e-01 1.282 0.2000
## sp_ptbBMns572 1.034e+00 6.531e-01 1.584 0.1133
## sp_ptbBMns573 7.660e-02 6.923e-01 0.111 0.9119
## sp_ptbBMns574 7.141e-01 6.301e-01 1.133 0.2571
## sp_ptbBMns575 6.818e-01 7.739e-01 0.881 0.3784
## sp_ptbBMns576 -1.559e-01 5.990e-01 -0.260 0.7947
## sp_ptbBMns577 3.829e-01 6.860e-01 0.558 0.5767
## sp_ptbBMns578 2.542e-01 5.928e-01 0.429 0.6681
## sp_ptbBMns579 4.495e-02 7.044e-01 0.064 0.9491
## sp_ptbBMns580 3.918e-01 7.714e-01 0.508 0.6115
## sp_ptbBMns581 6.709e-01 7.486e-01 0.896 0.3701
## sp_ptbBMns582 1.400e-01 6.944e-01 0.202 0.8403
## sp_ptbBMns583 7.093e-01 7.672e-01 0.925 0.3552
## sp_ptbBMns584 8.695e-01 7.843e-01 1.109 0.2676
## sp_ptbBMns585 5.544e-01 9.246e-01 0.600 0.5488
## sp_ptbBMns586 8.757e-01 7.547e-01 1.160 0.2459
## sp_ptbBMns587 8.265e-01 6.222e-01 1.328 0.1841
## sp_ptbBMns588 5.018e-01 6.979e-01 0.719 0.4721
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(15555.7) family taken to be 1)
##
## Null deviance: 1101.22 on 886 degrees of freedom
## Residual deviance: 983.29 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3206.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15556
## Std. Err.: 131004
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3016.188SA3m7a <- glm.nb(ptbBM ~ cb7.maxRH + sp_ptbBMns5,data=week); summary(SA3m7a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb7.maxRH + sp_ptbBMns5, data = week,
## init.theta = 15534.75875, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4135 -0.8018 -0.1179 0.5469 2.7803
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.974e+00 9.388e+00 0.210 0.833
## cb7.maxRHv1.l1 -2.680e-02 2.341e-01 -0.114 0.909
## cb7.maxRHv1.l2 2.456e-01 1.672e-01 1.469 0.142
## cb7.maxRHv2.l1 -1.710e-01 7.281e-01 -0.235 0.814
## cb7.maxRHv2.l2 5.635e-01 5.483e-01 1.028 0.304
## cb7.maxRHv3.l1 4.095e-02 2.261e-01 0.181 0.856
## cb7.maxRHv3.l2 2.147e-01 1.445e-01 1.486 0.137
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 1.929e+06 5.313e+06 0.363 0.717
## sp_ptbBMns53 -1.524e+00 9.731e+00 -0.157 0.876
## sp_ptbBMns54 1.098e+00 1.677e+00 0.655 0.513
## sp_ptbBMns55 -9.971e-03 1.172e+00 -0.009 0.993
## sp_ptbBMns56 7.533e-01 8.582e-01 0.878 0.380
## sp_ptbBMns57 -1.239e+00 8.409e-01 -1.474 0.141
## sp_ptbBMns58 3.143e-01 6.752e-01 0.465 0.642
## sp_ptbBMns59 7.303e-01 8.345e-01 0.875 0.381
## sp_ptbBMns510 -5.108e-01 7.035e-01 -0.726 0.468
## sp_ptbBMns511 -7.763e-02 6.301e-01 -0.123 0.902
## sp_ptbBMns512 -2.829e-01 6.366e-01 -0.444 0.657
## sp_ptbBMns513 -1.649e-01 7.221e-01 -0.228 0.819
## sp_ptbBMns514 3.802e-01 9.520e-01 0.399 0.690
## sp_ptbBMns515 4.922e-01 6.209e-01 0.793 0.428
## sp_ptbBMns516 -4.861e-01 7.558e-01 -0.643 0.520
## sp_ptbBMns517 -2.487e-01 6.452e-01 -0.385 0.700
## sp_ptbBMns518 -9.247e-02 9.582e-01 -0.097 0.923
## sp_ptbBMns519 -1.240e+00 1.217e+00 -1.018 0.309
## sp_ptbBMns520 -1.310e+00 1.327e+00 -0.987 0.324
## sp_ptbBMns521 -1.813e+00 2.019e+00 -0.898 0.369
## sp_ptbBMns522 -1.861e+00 2.937e+00 -0.634 0.526
## sp_ptbBMns523 -3.488e+00 4.344e+00 -0.803 0.422
## sp_ptbBMns524 -3.450e-01 5.072e+00 -0.068 0.946
## sp_ptbBMns525 -2.912e-01 5.825e+00 -0.050 0.960
## sp_ptbBMns526 1.437e+00 4.507e+00 0.319 0.750
## sp_ptbBMns527 1.998e+00 4.188e+00 0.477 0.633
## sp_ptbBMns528 1.774e+00 2.830e+00 0.627 0.531
## sp_ptbBMns529 1.924e+00 2.465e+00 0.781 0.435
## sp_ptbBMns530 -4.676e-01 1.583e+00 -0.295 0.768
## sp_ptbBMns531 6.778e-01 1.932e+00 0.351 0.726
## sp_ptbBMns532 6.917e-01 2.012e+00 0.344 0.731
## sp_ptbBMns533 -5.795e-01 2.334e+00 -0.248 0.804
## sp_ptbBMns534 1.275e+00 2.317e+00 0.550 0.582
## sp_ptbBMns535 1.027e+00 2.041e+00 0.503 0.615
## sp_ptbBMns536 2.055e+00 1.655e+00 1.241 0.214
## sp_ptbBMns537 1.138e+00 1.546e+00 0.736 0.462
## sp_ptbBMns538 8.160e-01 1.189e+00 0.686 0.493
## sp_ptbBMns539 -2.969e-01 1.071e+00 -0.277 0.782
## sp_ptbBMns540 7.982e-01 1.027e+00 0.777 0.437
## sp_ptbBMns541 6.474e-01 1.044e+00 0.620 0.535
## sp_ptbBMns542 -9.488e-01 1.213e+00 -0.782 0.434
## sp_ptbBMns543 1.259e-01 1.414e+00 0.089 0.929
## sp_ptbBMns544 -3.245e-01 1.312e+00 -0.247 0.805
## sp_ptbBMns545 1.003e+00 1.689e+00 0.594 0.553
## sp_ptbBMns546 1.405e+00 1.609e+00 0.873 0.383
## sp_ptbBMns547 1.009e+00 1.300e+00 0.776 0.438
## sp_ptbBMns548 1.073e+00 1.104e+00 0.972 0.331
## sp_ptbBMns549 1.170e+00 1.009e+00 1.159 0.246
## sp_ptbBMns550 2.177e-01 9.460e-01 0.230 0.818
## sp_ptbBMns551 -3.172e-01 9.111e-01 -0.348 0.728
## sp_ptbBMns552 5.820e-02 8.543e-01 0.068 0.946
## sp_ptbBMns553 2.128e-01 9.030e-01 0.236 0.814
## sp_ptbBMns554 -7.948e-02 8.811e-01 -0.090 0.928
## sp_ptbBMns555 8.290e-01 8.523e-01 0.973 0.331
## sp_ptbBMns556 1.271e+00 8.825e-01 1.441 0.150
## sp_ptbBMns557 -1.600e-01 9.122e-01 -0.175 0.861
## sp_ptbBMns558 1.492e-01 9.656e-01 0.154 0.877
## sp_ptbBMns559 3.494e-01 7.781e-01 0.449 0.653
## sp_ptbBMns560 -6.017e-02 1.049e+00 -0.057 0.954
## sp_ptbBMns561 1.163e+00 1.122e+00 1.037 0.300
## sp_ptbBMns562 7.993e-01 9.433e-01 0.847 0.397
## sp_ptbBMns563 -6.739e-01 1.099e+00 -0.613 0.540
## sp_ptbBMns564 8.112e-01 9.359e-01 0.867 0.386
## sp_ptbBMns565 1.739e-01 7.922e-01 0.220 0.826
## sp_ptbBMns566 7.451e-01 7.460e-01 0.999 0.318
## sp_ptbBMns567 -9.468e-01 7.597e-01 -1.246 0.213
## sp_ptbBMns568 7.327e-01 7.499e-01 0.977 0.329
## sp_ptbBMns569 6.729e-03 6.853e-01 0.010 0.992
## sp_ptbBMns570 5.825e-01 8.576e-01 0.679 0.497
## sp_ptbBMns571 3.859e-01 6.075e-01 0.635 0.525
## sp_ptbBMns572 6.155e-01 5.877e-01 1.047 0.295
## sp_ptbBMns573 -9.145e-02 6.723e-01 -0.136 0.892
## sp_ptbBMns574 6.879e-01 6.502e-01 1.058 0.290
## sp_ptbBMns575 1.223e-01 7.982e-01 0.153 0.878
## sp_ptbBMns576 -1.592e-01 9.001e-01 -0.177 0.860
## sp_ptbBMns577 2.154e-01 7.727e-01 0.279 0.780
## sp_ptbBMns578 4.911e-01 8.799e-01 0.558 0.577
## sp_ptbBMns579 1.113e+00 1.081e+00 1.030 0.303
## sp_ptbBMns580 6.543e-01 8.400e-01 0.779 0.436
## sp_ptbBMns581 2.649e-01 6.805e-01 0.389 0.697
## sp_ptbBMns582 2.131e-01 6.537e-01 0.326 0.744
## sp_ptbBMns583 5.150e-02 7.214e-01 0.071 0.943
## sp_ptbBMns584 2.554e-01 9.340e-01 0.273 0.785
## sp_ptbBMns585 -7.986e-01 8.914e-01 -0.896 0.370
## sp_ptbBMns586 4.673e-02 6.901e-01 0.068 0.946
## sp_ptbBMns587 -7.754e-02 6.513e-01 -0.119 0.905
## sp_ptbBMns588 3.875e-01 7.684e-01 0.504 0.614
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
##
## (Dispersion parameter for Negative Binomial(15534.76) family taken to be 1)
##
## Null deviance: 1101.22 on 886 degrees of freedom
## Residual deviance: 983.52 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3206.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15535
## Std. Err.: 130957
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3016.422SA3m8a <- glm.nb(ptbBM ~ cb8.AH + sp_ptbBMns5,data=week); summary(SA3m8a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb8.AH + sp_ptbBMns5, data = week, init.theta = 16491.29833,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5551 -0.8036 -0.1209 0.5460 2.8761
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -5.482e+00 1.061e+01 -0.516 0.6055
## cb8.AHv1.l1 1.216e-01 2.550e-01 0.477 0.6335
## cb8.AHv1.l2 -1.548e-01 1.804e-01 -0.858 0.3908
## cb8.AHv2.l1 6.262e-01 8.445e-01 0.741 0.4584
## cb8.AHv2.l2 -3.246e-01 6.087e-01 -0.533 0.5939
## cb8.AHv3.l1 5.600e-01 4.266e-01 1.313 0.1893
## cb8.AHv3.l2 2.452e-01 2.389e-01 1.027 0.3046
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.468e+06 5.383e+06 0.459 0.6465
## sp_ptbBMns53 -1.387e+00 9.756e+00 -0.142 0.8869
## sp_ptbBMns54 1.877e+00 1.958e+00 0.959 0.3377
## sp_ptbBMns55 9.504e-01 1.668e+00 0.570 0.5688
## sp_ptbBMns56 1.804e+00 1.337e+00 1.349 0.1772
## sp_ptbBMns57 -6.218e-02 1.308e+00 -0.048 0.9621
## sp_ptbBMns58 1.884e+00 1.454e+00 1.296 0.1951
## sp_ptbBMns59 1.219e+00 1.173e+00 1.039 0.2989
## sp_ptbBMns510 6.585e-01 1.355e+00 0.486 0.6270
## sp_ptbBMns511 1.153e+00 1.250e+00 0.922 0.3563
## sp_ptbBMns512 1.025e+00 1.338e+00 0.766 0.4439
## sp_ptbBMns513 8.705e-01 1.298e+00 0.671 0.5025
## sp_ptbBMns514 7.281e-01 1.430e+00 0.509 0.6106
## sp_ptbBMns515 1.923e+00 1.638e+00 1.174 0.2405
## sp_ptbBMns516 1.691e+00 2.094e+00 0.807 0.4195
## sp_ptbBMns517 3.062e+00 2.747e+00 1.115 0.2650
## sp_ptbBMns518 4.213e+00 3.417e+00 1.233 0.2176
## sp_ptbBMns519 1.388e+00 3.567e+00 0.389 0.6972
## sp_ptbBMns520 3.074e+00 3.536e+00 0.869 0.3847
## sp_ptbBMns521 2.000e+00 3.403e+00 0.588 0.5566
## sp_ptbBMns522 2.767e+00 3.363e+00 0.823 0.4107
## sp_ptbBMns523 1.860e+00 3.142e+00 0.592 0.5538
## sp_ptbBMns524 3.054e+00 3.515e+00 0.869 0.3848
## sp_ptbBMns525 3.840e+00 3.691e+00 1.040 0.2982
## sp_ptbBMns526 2.462e+00 3.370e+00 0.730 0.4651
## sp_ptbBMns527 2.008e+00 2.956e+00 0.679 0.4969
## sp_ptbBMns528 1.914e+00 2.853e+00 0.671 0.5023
## sp_ptbBMns529 1.536e+00 2.300e+00 0.668 0.5044
## sp_ptbBMns530 1.320e+00 2.257e+00 0.585 0.5586
## sp_ptbBMns531 1.903e+00 2.133e+00 0.892 0.3722
## sp_ptbBMns532 2.252e+00 2.312e+00 0.974 0.3301
## sp_ptbBMns533 1.880e+00 2.439e+00 0.771 0.4408
## sp_ptbBMns534 2.834e+00 2.598e+00 1.091 0.2754
## sp_ptbBMns535 1.893e+00 2.484e+00 0.762 0.4461
## sp_ptbBMns536 2.551e+00 2.525e+00 1.010 0.3124
## sp_ptbBMns537 1.656e+00 2.537e+00 0.653 0.5138
## sp_ptbBMns538 2.177e+00 2.347e+00 0.928 0.3536
## sp_ptbBMns539 1.211e+00 2.075e+00 0.584 0.5595
## sp_ptbBMns540 1.978e+00 1.999e+00 0.989 0.3225
## sp_ptbBMns541 1.802e+00 1.730e+00 1.042 0.2976
## sp_ptbBMns542 8.129e-01 1.781e+00 0.456 0.6481
## sp_ptbBMns543 2.085e+00 1.692e+00 1.232 0.2181
## sp_ptbBMns544 8.900e-01 1.708e+00 0.521 0.6023
## sp_ptbBMns545 1.647e+00 1.708e+00 0.964 0.3350
## sp_ptbBMns546 1.704e+00 1.256e+00 1.356 0.1749
## sp_ptbBMns547 1.105e+00 9.497e-01 1.164 0.2445
## sp_ptbBMns548 1.370e+00 8.593e-01 1.595 0.1107
## sp_ptbBMns549 1.020e+00 9.812e-01 1.040 0.2986
## sp_ptbBMns550 -2.983e-01 1.390e+00 -0.215 0.8301
## sp_ptbBMns551 7.316e-01 1.608e+00 0.455 0.6492
## sp_ptbBMns552 1.601e+00 1.637e+00 0.978 0.3279
## sp_ptbBMns553 1.916e+00 1.693e+00 1.132 0.2578
## sp_ptbBMns554 1.300e+00 1.472e+00 0.883 0.3771
## sp_ptbBMns555 1.668e+00 1.297e+00 1.287 0.1982
## sp_ptbBMns556 1.836e+00 1.210e+00 1.517 0.1293
## sp_ptbBMns557 1.363e+00 1.365e+00 0.999 0.3179
## sp_ptbBMns558 1.467e+00 1.422e+00 1.032 0.3022
## sp_ptbBMns559 1.553e+00 1.317e+00 1.179 0.2383
## sp_ptbBMns560 8.575e-01 1.306e+00 0.656 0.5115
## sp_ptbBMns561 1.492e+00 1.037e+00 1.438 0.1504
## sp_ptbBMns562 1.600e+00 9.505e-01 1.683 0.0923 .
## sp_ptbBMns563 -3.271e-02 1.158e+00 -0.028 0.9775
## sp_ptbBMns564 9.422e-01 8.690e-01 1.084 0.2782
## sp_ptbBMns565 6.041e-01 1.297e+00 0.466 0.6414
## sp_ptbBMns566 1.842e+00 1.075e+00 1.714 0.0866 .
## sp_ptbBMns567 7.389e-02 1.054e+00 0.070 0.9441
## sp_ptbBMns568 1.324e+00 8.518e-01 1.555 0.1200
## sp_ptbBMns569 3.388e-01 8.562e-01 0.396 0.6923
## sp_ptbBMns570 -2.655e-03 8.546e-01 -0.003 0.9975
## sp_ptbBMns571 8.322e-01 7.919e-01 1.051 0.2933
## sp_ptbBMns572 1.516e+00 7.269e-01 2.086 0.0370 *
## sp_ptbBMns573 2.490e-01 6.874e-01 0.362 0.7171
## sp_ptbBMns574 8.438e-01 6.702e-01 1.259 0.2080
## sp_ptbBMns575 -1.283e-01 7.418e-01 -0.173 0.8627
## sp_ptbBMns576 -1.112e+00 1.723e+00 -0.645 0.5187
## sp_ptbBMns577 -9.395e-01 1.467e+00 -0.641 0.5218
## sp_ptbBMns578 -2.086e+00 1.489e+00 -1.400 0.1614
## sp_ptbBMns579 -1.990e+00 1.136e+00 -1.751 0.0799 .
## sp_ptbBMns580 -2.027e+00 1.173e+00 -1.727 0.0841 .
## sp_ptbBMns581 4.482e-01 5.817e-01 0.770 0.4410
## sp_ptbBMns582 9.205e-01 7.248e-01 1.270 0.2041
## sp_ptbBMns583 3.505e-01 6.113e-01 0.573 0.5664
## sp_ptbBMns584 6.364e-01 6.570e-01 0.969 0.3327
## sp_ptbBMns585 -8.589e-01 7.474e-01 -1.149 0.2505
## sp_ptbBMns586 5.569e-01 6.195e-01 0.899 0.3686
## sp_ptbBMns587 5.605e-01 5.033e-01 1.114 0.2654
## sp_ptbBMns588 6.416e-01 6.700e-01 0.958 0.3382
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(16491.3) family taken to be 1)
##
## Null deviance: 1101.23 on 886 degrees of freedom
## Residual deviance: 979.78 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3202.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 16491
## Std. Err.: 131680
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3012.67SA3m9a <- glm.nb(ptbBM ~ cb9.minT + sp_ptbBMns5,data=week); summary(SA3m9a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBMns5, data = week,
## init.theta = 17448.55852, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3977 -0.8342 -0.1111 0.5435 2.8324
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.883e-01 8.973e+00 0.032 0.97437
## cb9.minTv1.l1 2.738e-01 2.171e-01 1.261 0.20724
## cb9.minTv1.l2 -3.795e-01 1.497e-01 -2.535 0.01123 *
## cb9.minTv2.l1 1.316e-01 7.587e-01 0.173 0.86226
## cb9.minTv2.l2 -8.424e-01 5.516e-01 -1.527 0.12675
## cb9.minTv3.l1 1.779e-01 4.041e-01 0.440 0.65973
## cb9.minTv3.l2 2.391e-01 3.077e-01 0.777 0.43720
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.288e+06 5.352e+06 0.427 0.66906
## sp_ptbBMns53 -3.215e+00 9.649e+00 -0.333 0.73902
## sp_ptbBMns54 1.517e-01 1.592e+00 0.095 0.92408
## sp_ptbBMns55 -1.351e+00 1.013e+00 -1.333 0.18249
## sp_ptbBMns56 -7.888e-01 8.786e-01 -0.898 0.36928
## sp_ptbBMns57 -2.564e+00 9.731e-01 -2.634 0.00843 **
## sp_ptbBMns58 2.176e-01 7.092e-01 0.307 0.75901
## sp_ptbBMns59 -5.288e-01 7.909e-01 -0.669 0.50374
## sp_ptbBMns510 -9.688e-01 7.792e-01 -1.243 0.21372
## sp_ptbBMns511 -8.698e-01 8.503e-01 -1.023 0.30632
## sp_ptbBMns512 -6.818e-01 8.364e-01 -0.815 0.41493
## sp_ptbBMns513 -1.559e+00 9.759e-01 -1.598 0.11015
## sp_ptbBMns514 -1.003e+00 8.471e-01 -1.184 0.23656
## sp_ptbBMns515 -1.378e-01 7.560e-01 -0.182 0.85539
## sp_ptbBMns516 -1.464e+00 7.654e-01 -1.913 0.05580 .
## sp_ptbBMns517 -5.405e-01 6.984e-01 -0.774 0.43897
## sp_ptbBMns518 2.708e-01 6.889e-01 0.393 0.69421
## sp_ptbBMns519 -1.722e+00 8.219e-01 -2.095 0.03620 *
## sp_ptbBMns520 8.761e-01 7.094e-01 1.235 0.21687
## sp_ptbBMns521 -2.945e-01 7.139e-01 -0.413 0.67996
## sp_ptbBMns522 8.915e-01 7.909e-01 1.127 0.25966
## sp_ptbBMns523 -2.045e-01 7.899e-01 -0.259 0.79576
## sp_ptbBMns524 1.445e+00 1.181e+00 1.223 0.22131
## sp_ptbBMns525 2.467e+00 1.185e+00 2.081 0.03740 *
## sp_ptbBMns526 2.202e+00 1.740e+00 1.266 0.20551
## sp_ptbBMns527 9.763e-01 1.538e+00 0.635 0.52570
## sp_ptbBMns528 5.225e-01 1.636e+00 0.319 0.74948
## sp_ptbBMns529 -5.243e-01 1.138e+00 -0.461 0.64496
## sp_ptbBMns530 -2.397e-01 1.058e+00 -0.227 0.82079
## sp_ptbBMns531 -2.175e-01 7.228e-01 -0.301 0.76345
## sp_ptbBMns532 4.706e-01 7.048e-01 0.668 0.50429
## sp_ptbBMns533 1.320e-01 6.889e-01 0.192 0.84806
## sp_ptbBMns534 5.734e-01 8.025e-01 0.715 0.47487
## sp_ptbBMns535 1.165e+00 8.056e-01 1.446 0.14826
## sp_ptbBMns536 1.612e+00 8.279e-01 1.947 0.05152 .
## sp_ptbBMns537 1.271e+00 1.011e+00 1.257 0.20883
## sp_ptbBMns538 1.718e+00 1.017e+00 1.690 0.09112 .
## sp_ptbBMns539 -1.008e+00 1.003e+00 -1.005 0.31489
## sp_ptbBMns540 1.304e+00 9.737e-01 1.339 0.18068
## sp_ptbBMns541 -6.513e-02 7.696e-01 -0.085 0.93256
## sp_ptbBMns542 -1.059e+00 7.072e-01 -1.498 0.13414
## sp_ptbBMns543 2.650e-01 6.359e-01 0.417 0.67694
## sp_ptbBMns544 -1.705e+00 7.212e-01 -2.364 0.01809 *
## sp_ptbBMns545 -6.838e-01 8.665e-01 -0.789 0.43003
## sp_ptbBMns546 -7.905e-01 1.126e+00 -0.702 0.48261
## sp_ptbBMns547 -1.378e+00 9.923e-01 -1.389 0.16497
## sp_ptbBMns548 -7.710e-01 9.973e-01 -0.773 0.43948
## sp_ptbBMns549 -1.865e+00 1.062e+00 -1.757 0.07898 .
## sp_ptbBMns550 -9.185e-01 9.479e-01 -0.969 0.33254
## sp_ptbBMns551 -2.664e-01 7.230e-01 -0.368 0.71257
## sp_ptbBMns552 -3.145e-01 6.704e-01 -0.469 0.63894
## sp_ptbBMns553 2.668e-01 6.800e-01 0.392 0.69478
## sp_ptbBMns554 -1.088e+00 7.108e-01 -1.530 0.12592
## sp_ptbBMns555 -8.039e-02 7.373e-01 -0.109 0.91318
## sp_ptbBMns556 -1.090e-01 7.574e-01 -0.144 0.88552
## sp_ptbBMns557 -6.145e-01 7.593e-01 -0.809 0.41831
## sp_ptbBMns558 -6.226e-01 7.609e-01 -0.818 0.41325
## sp_ptbBMns559 -8.698e-01 8.036e-01 -1.082 0.27906
## sp_ptbBMns560 -1.835e+00 9.901e-01 -1.854 0.06380 .
## sp_ptbBMns561 -1.411e+00 1.126e+00 -1.254 0.21002
## sp_ptbBMns562 -7.120e-01 1.093e+00 -0.652 0.51471
## sp_ptbBMns563 -2.494e+00 1.103e+00 -2.261 0.02375 *
## sp_ptbBMns564 -2.036e+00 1.144e+00 -1.779 0.07520 .
## sp_ptbBMns565 -1.048e+00 8.401e-01 -1.248 0.21202
## sp_ptbBMns566 -3.034e-01 9.312e-01 -0.326 0.74453
## sp_ptbBMns567 -2.010e+00 9.807e-01 -2.050 0.04037 *
## sp_ptbBMns568 -8.636e-01 9.836e-01 -0.878 0.37992
## sp_ptbBMns569 -2.665e+00 1.079e+00 -2.470 0.01351 *
## sp_ptbBMns570 -2.376e+00 1.231e+00 -1.931 0.05354 .
## sp_ptbBMns571 -1.093e+00 1.148e+00 -0.952 0.34091
## sp_ptbBMns572 -7.583e-02 1.203e+00 -0.063 0.94974
## sp_ptbBMns573 -1.847e+00 1.242e+00 -1.487 0.13700
## sp_ptbBMns574 -1.941e+00 1.273e+00 -1.524 0.12740
## sp_ptbBMns575 -1.930e+00 2.179e+00 -0.886 0.37583
## sp_ptbBMns576 -3.890e-01 3.155e+00 -0.123 0.90187
## sp_ptbBMns577 -1.156e+00 2.827e+00 -0.409 0.68263
## sp_ptbBMns578 -2.669e+00 2.857e+00 -0.934 0.35010
## sp_ptbBMns579 -5.529e+00 3.295e+00 -1.678 0.09335 .
## sp_ptbBMns580 -4.665e+00 2.548e+00 -1.831 0.06714 .
## sp_ptbBMns581 -2.028e+00 1.200e+00 -1.691 0.09093 .
## sp_ptbBMns582 -2.046e+00 1.160e+00 -1.763 0.07793 .
## sp_ptbBMns583 -8.936e-01 9.637e-01 -0.927 0.35380
## sp_ptbBMns584 -3.209e-01 7.035e-01 -0.456 0.64835
## sp_ptbBMns585 -1.636e-01 7.235e-01 -0.226 0.82115
## sp_ptbBMns586 -3.060e-01 6.221e-01 -0.492 0.62279
## sp_ptbBMns587 1.331e-02 5.364e-01 0.025 0.98020
## sp_ptbBMns588 5.988e-01 6.864e-01 0.872 0.38300
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17448.56) family taken to be 1)
##
## Null deviance: 1101.2 on 886 degrees of freedom
## Residual deviance: 974.2 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3197.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17449
## Std. Err.: 134101
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3007.088SA3m10a <- glm.nb(ptbBM ~ cb10.aveT + sp_ptbBMns5,data=week); summary(SA3m10a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb10.aveT + sp_ptbBMns5, data = week,
## init.theta = 16664.08935, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4185 -0.8330 -0.1246 0.5577 2.9186
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.055e+01 1.213e+01 -0.870 0.3845
## cb10.aveTv1.l1 3.155e-01 2.555e-01 1.234 0.2170
## cb10.aveTv1.l2 -3.431e-01 1.858e-01 -1.847 0.0648 .
## cb10.aveTv2.l1 1.016e+00 9.708e-01 1.047 0.2953
## cb10.aveTv2.l2 -1.039e+00 7.298e-01 -1.424 0.1545
## cb10.aveTv3.l1 6.342e-01 3.938e-01 1.611 0.1073
## cb10.aveTv3.l2 -6.740e-02 2.956e-01 -0.228 0.8196
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.528e+06 5.368e+06 0.471 0.6377
## sp_ptbBMns53 -1.550e+00 9.694e+00 -0.160 0.8730
## sp_ptbBMns54 1.995e+00 1.709e+00 1.167 0.2431
## sp_ptbBMns55 8.601e-01 1.131e+00 0.761 0.4469
## sp_ptbBMns56 8.134e-01 8.472e-01 0.960 0.3370
## sp_ptbBMns57 -8.182e-01 8.393e-01 -0.975 0.3296
## sp_ptbBMns58 1.384e+00 8.386e-01 1.650 0.0989 .
## sp_ptbBMns59 7.719e-01 7.485e-01 1.031 0.3024
## sp_ptbBMns510 3.565e-01 8.944e-01 0.399 0.6902
## sp_ptbBMns511 1.464e-01 7.403e-01 0.198 0.8433
## sp_ptbBMns512 6.891e-01 7.827e-01 0.880 0.3786
## sp_ptbBMns513 6.057e-01 7.437e-01 0.814 0.4154
## sp_ptbBMns514 6.810e-01 8.797e-01 0.774 0.4389
## sp_ptbBMns515 1.660e+00 9.738e-01 1.704 0.0883 .
## sp_ptbBMns516 4.135e-01 8.275e-01 0.500 0.6172
## sp_ptbBMns517 9.833e-01 9.645e-01 1.020 0.3080
## sp_ptbBMns518 1.816e+00 1.039e+00 1.748 0.0804 .
## sp_ptbBMns519 -3.014e-01 1.098e+00 -0.274 0.7837
## sp_ptbBMns520 1.557e+00 1.109e+00 1.404 0.1604
## sp_ptbBMns521 1.180e+00 8.661e-01 1.362 0.1732
## sp_ptbBMns522 1.509e+00 8.502e-01 1.775 0.0759 .
## sp_ptbBMns523 -3.196e-01 8.564e-01 -0.373 0.7090
## sp_ptbBMns524 1.466e+00 1.028e+00 1.425 0.1542
## sp_ptbBMns525 1.514e+00 9.922e-01 1.526 0.1270
## sp_ptbBMns526 1.772e+00 1.132e+00 1.565 0.1175
## sp_ptbBMns527 1.455e+00 9.400e-01 1.548 0.1217
## sp_ptbBMns528 1.721e+00 1.173e+00 1.467 0.1423
## sp_ptbBMns529 1.221e+00 9.595e-01 1.272 0.2033
## sp_ptbBMns530 1.150e+00 1.169e+00 0.984 0.3250
## sp_ptbBMns531 1.138e+00 8.168e-01 1.394 0.1634
## sp_ptbBMns532 1.354e+00 8.999e-01 1.505 0.1324
## sp_ptbBMns533 8.623e-01 9.035e-01 0.954 0.3399
## sp_ptbBMns534 1.875e+00 9.930e-01 1.888 0.0590 .
## sp_ptbBMns535 1.961e+00 1.209e+00 1.622 0.1048
## sp_ptbBMns536 2.752e+00 1.167e+00 2.358 0.0184 *
## sp_ptbBMns537 1.502e+00 1.147e+00 1.310 0.1901
## sp_ptbBMns538 1.829e+00 1.184e+00 1.544 0.1226
## sp_ptbBMns539 4.643e-01 1.102e+00 0.421 0.6735
## sp_ptbBMns540 2.693e+00 1.369e+00 1.967 0.0492 *
## sp_ptbBMns541 1.636e+00 8.821e-01 1.855 0.0636 .
## sp_ptbBMns542 3.551e-01 8.663e-01 0.410 0.6819
## sp_ptbBMns543 6.761e-01 6.631e-01 1.020 0.3079
## sp_ptbBMns544 -6.078e-01 7.034e-01 -0.864 0.3876
## sp_ptbBMns545 -2.292e-01 8.567e-01 -0.268 0.7890
## sp_ptbBMns546 -2.664e-01 7.244e-01 -0.368 0.7131
## sp_ptbBMns547 3.913e-01 6.360e-01 0.615 0.5383
## sp_ptbBMns548 8.092e-01 5.972e-01 1.355 0.1754
## sp_ptbBMns549 5.210e-01 7.498e-01 0.695 0.4872
## sp_ptbBMns550 1.154e+00 1.180e+00 0.978 0.3281
## sp_ptbBMns551 1.325e+00 1.179e+00 1.124 0.2609
## sp_ptbBMns552 1.438e+00 8.992e-01 1.599 0.1099
## sp_ptbBMns553 1.512e+00 9.252e-01 1.635 0.1021
## sp_ptbBMns554 4.473e-01 8.992e-01 0.497 0.6189
## sp_ptbBMns555 1.472e+00 9.490e-01 1.551 0.1209
## sp_ptbBMns556 1.352e+00 8.492e-01 1.592 0.1114
## sp_ptbBMns557 7.890e-01 7.126e-01 1.107 0.2682
## sp_ptbBMns558 1.046e+00 7.225e-01 1.448 0.1475
## sp_ptbBMns559 7.627e-01 7.576e-01 1.007 0.3141
## sp_ptbBMns560 -4.514e-02 8.312e-01 -0.054 0.9567
## sp_ptbBMns561 3.702e-01 8.292e-01 0.446 0.6553
## sp_ptbBMns562 1.334e+00 8.022e-01 1.663 0.0963 .
## sp_ptbBMns563 1.823e-01 8.936e-01 0.204 0.8383
## sp_ptbBMns564 9.337e-01 8.796e-01 1.062 0.2884
## sp_ptbBMns565 2.783e+00 1.442e+00 1.930 0.0536 .
## sp_ptbBMns566 1.892e+00 8.477e-01 2.232 0.0256 *
## sp_ptbBMns567 4.436e-01 9.724e-01 0.456 0.6482
## sp_ptbBMns568 1.717e+00 7.957e-01 2.158 0.0310 *
## sp_ptbBMns569 1.493e-01 1.043e+00 0.143 0.8861
## sp_ptbBMns570 1.343e+00 1.022e+00 1.314 0.1887
## sp_ptbBMns571 1.248e+00 8.972e-01 1.390 0.1644
## sp_ptbBMns572 1.680e+00 7.333e-01 2.291 0.0220 *
## sp_ptbBMns573 7.671e-01 6.949e-01 1.104 0.2696
## sp_ptbBMns574 4.395e-01 5.755e-01 0.764 0.4450
## sp_ptbBMns575 1.825e-01 8.648e-01 0.211 0.8329
## sp_ptbBMns576 -1.297e+00 1.044e+00 -1.243 0.2140
## sp_ptbBMns577 -1.908e-01 9.135e-01 -0.209 0.8345
## sp_ptbBMns578 -6.138e-01 9.801e-01 -0.626 0.5311
## sp_ptbBMns579 -1.165e+00 8.489e-01 -1.372 0.1700
## sp_ptbBMns580 -1.223e+00 1.212e+00 -1.009 0.3131
## sp_ptbBMns581 4.412e-01 7.560e-01 0.584 0.5594
## sp_ptbBMns582 5.724e-01 7.262e-01 0.788 0.4306
## sp_ptbBMns583 1.301e+00 8.398e-01 1.549 0.1213
## sp_ptbBMns584 1.058e+00 7.546e-01 1.402 0.1609
## sp_ptbBMns585 9.858e-01 9.598e-01 1.027 0.3044
## sp_ptbBMns586 9.466e-01 8.005e-01 1.183 0.2370
## sp_ptbBMns587 1.119e+00 5.918e-01 1.890 0.0587 .
## sp_ptbBMns588 9.123e-01 6.969e-01 1.309 0.1905
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(16664.09) family taken to be 1)
##
## Null deviance: 1101.23 on 886 degrees of freedom
## Residual deviance: 977.59 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3200.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 16664
## Std. Err.: 132857
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3010.481SA3m11a <- glm.nb(ptbBM ~ cb11.maxT + sp_ptbBMns5,data=week); summary(SA3m11a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb11.maxT + sp_ptbBMns5, data = week,
## init.theta = 16847.67071, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4238 -0.8216 -0.1175 0.5601 2.9815
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.038e+01 1.290e+01 -1.580 0.11412
## cb11.maxTv1.l1 4.711e-01 2.823e-01 1.669 0.09511 .
## cb11.maxTv1.l2 -4.667e-02 1.869e-01 -0.250 0.80277
## cb11.maxTv2.l1 1.865e+00 1.020e+00 1.829 0.06737 .
## cb11.maxTv2.l2 -7.508e-01 6.681e-01 -1.124 0.26112
## cb11.maxTv3.l1 9.807e-01 4.262e-01 2.301 0.02138 *
## cb11.maxTv3.l2 -1.134e-01 2.929e-01 -0.387 0.69856
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.615e+06 5.386e+06 0.486 0.62722
## sp_ptbBMns53 -2.337e+00 9.729e+00 -0.240 0.81013
## sp_ptbBMns54 1.460e+00 1.695e+00 0.861 0.38900
## sp_ptbBMns55 -2.540e-01 1.213e+00 -0.209 0.83412
## sp_ptbBMns56 8.048e-01 8.394e-01 0.959 0.33769
## sp_ptbBMns57 -1.153e+00 7.585e-01 -1.519 0.12865
## sp_ptbBMns58 1.421e+00 6.810e-01 2.086 0.03698 *
## sp_ptbBMns59 -4.214e-02 6.165e-01 -0.068 0.94550
## sp_ptbBMns510 -9.182e-01 9.809e-01 -0.936 0.34926
## sp_ptbBMns511 -9.238e-01 8.500e-01 -1.087 0.27712
## sp_ptbBMns512 -4.254e-01 8.060e-01 -0.528 0.59769
## sp_ptbBMns513 -2.772e-02 7.876e-01 -0.035 0.97193
## sp_ptbBMns514 -6.928e-01 8.212e-01 -0.844 0.39885
## sp_ptbBMns515 5.933e-01 1.026e+00 0.579 0.56288
## sp_ptbBMns516 -2.096e-01 9.007e-01 -0.233 0.81598
## sp_ptbBMns517 2.084e-01 7.907e-01 0.264 0.79208
## sp_ptbBMns518 1.332e+00 8.563e-01 1.555 0.11990
## sp_ptbBMns519 -9.888e-01 8.767e-01 -1.128 0.25938
## sp_ptbBMns520 3.632e-01 1.008e+00 0.360 0.71861
## sp_ptbBMns521 1.674e-01 6.909e-01 0.242 0.80853
## sp_ptbBMns522 1.008e+00 7.532e-01 1.338 0.18075
## sp_ptbBMns523 -7.045e-01 7.163e-01 -0.984 0.32533
## sp_ptbBMns524 2.761e-02 7.656e-01 0.036 0.97124
## sp_ptbBMns525 -5.736e-01 9.929e-01 -0.578 0.56344
## sp_ptbBMns526 3.681e-01 8.262e-01 0.445 0.65598
## sp_ptbBMns527 4.027e-01 7.104e-01 0.567 0.57081
## sp_ptbBMns528 8.377e-01 7.745e-01 1.082 0.27945
## sp_ptbBMns529 1.621e-01 7.389e-01 0.219 0.82641
## sp_ptbBMns530 -5.449e-01 1.007e+00 -0.541 0.58852
## sp_ptbBMns531 4.044e-01 7.510e-01 0.538 0.59027
## sp_ptbBMns532 6.507e-01 6.768e-01 0.961 0.33633
## sp_ptbBMns533 -4.015e-01 7.215e-01 -0.556 0.57789
## sp_ptbBMns534 6.272e-01 6.805e-01 0.922 0.35669
## sp_ptbBMns535 -2.485e-01 1.025e+00 -0.243 0.80838
## sp_ptbBMns536 1.265e+00 7.436e-01 1.701 0.08901 .
## sp_ptbBMns537 7.391e-01 7.312e-01 1.011 0.31210
## sp_ptbBMns538 1.325e+00 8.099e-01 1.636 0.10193
## sp_ptbBMns539 -1.248e-01 7.897e-01 -0.158 0.87442
## sp_ptbBMns540 7.177e-01 1.101e+00 0.652 0.51428
## sp_ptbBMns541 1.782e-01 7.203e-01 0.247 0.80460
## sp_ptbBMns542 -1.664e+00 1.077e+00 -1.546 0.12214
## sp_ptbBMns543 -7.234e-01 9.826e-01 -0.736 0.46160
## sp_ptbBMns544 -1.779e+00 8.986e-01 -1.980 0.04772 *
## sp_ptbBMns545 -2.154e+00 1.247e+00 -1.727 0.08422 .
## sp_ptbBMns546 -1.171e+00 1.099e+00 -1.066 0.28641
## sp_ptbBMns547 -6.490e-01 7.319e-01 -0.887 0.37522
## sp_ptbBMns548 9.081e-01 6.719e-01 1.352 0.17652
## sp_ptbBMns549 -1.569e-03 8.033e-01 -0.002 0.99844
## sp_ptbBMns550 -5.132e-01 1.162e+00 -0.441 0.65887
## sp_ptbBMns551 8.770e-02 9.602e-01 0.091 0.92722
## sp_ptbBMns552 6.598e-01 7.837e-01 0.842 0.39984
## sp_ptbBMns553 1.220e+00 7.094e-01 1.720 0.08538 .
## sp_ptbBMns554 6.393e-01 7.190e-01 0.889 0.37388
## sp_ptbBMns555 8.968e-02 9.234e-01 0.097 0.92263
## sp_ptbBMns556 3.033e-01 8.349e-01 0.363 0.71643
## sp_ptbBMns557 -5.844e-01 7.286e-01 -0.802 0.42252
## sp_ptbBMns558 3.570e-01 6.693e-01 0.533 0.59380
## sp_ptbBMns559 -3.114e-02 6.844e-01 -0.045 0.96371
## sp_ptbBMns560 -9.617e-01 1.009e+00 -0.954 0.34032
## sp_ptbBMns561 1.643e-01 7.536e-01 0.218 0.82746
## sp_ptbBMns562 9.832e-01 7.617e-01 1.291 0.19673
## sp_ptbBMns563 1.736e-01 8.419e-01 0.206 0.83669
## sp_ptbBMns564 5.285e-01 8.261e-01 0.640 0.52233
## sp_ptbBMns565 2.316e+00 1.587e+00 1.459 0.14463
## sp_ptbBMns566 1.841e+00 1.022e+00 1.801 0.07173 .
## sp_ptbBMns567 1.073e+00 1.084e+00 0.990 0.32234
## sp_ptbBMns568 2.540e+00 9.707e-01 2.617 0.00887 **
## sp_ptbBMns569 1.281e+00 1.268e+00 1.010 0.31246
## sp_ptbBMns570 1.402e+00 1.306e+00 1.074 0.28295
## sp_ptbBMns571 1.372e+00 8.990e-01 1.527 0.12688
## sp_ptbBMns572 2.209e+00 8.401e-01 2.630 0.00855 **
## sp_ptbBMns573 1.404e+00 7.928e-01 1.771 0.07656 .
## sp_ptbBMns574 1.216e+00 7.533e-01 1.614 0.10651
## sp_ptbBMns575 2.326e-02 1.015e+00 0.023 0.98172
## sp_ptbBMns576 -5.348e-01 6.773e-01 -0.790 0.42981
## sp_ptbBMns577 -3.553e-01 7.412e-01 -0.479 0.63172
## sp_ptbBMns578 -2.359e-01 6.824e-01 -0.346 0.72957
## sp_ptbBMns579 -4.266e-01 6.034e-01 -0.707 0.47960
## sp_ptbBMns580 -1.110e+00 9.360e-01 -1.186 0.23565
## sp_ptbBMns581 2.544e-01 7.861e-01 0.324 0.74623
## sp_ptbBMns582 1.398e-01 7.063e-01 0.198 0.84313
## sp_ptbBMns583 1.105e+00 9.184e-01 1.204 0.22875
## sp_ptbBMns584 9.475e-01 7.732e-01 1.225 0.22042
## sp_ptbBMns585 3.827e-01 1.018e+00 0.376 0.70698
## sp_ptbBMns586 9.041e-01 8.790e-01 1.029 0.30370
## sp_ptbBMns587 8.319e-01 6.344e-01 1.311 0.18972
## sp_ptbBMns588 5.694e-01 7.100e-01 0.802 0.42258
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(16847.67) family taken to be 1)
##
## Null deviance: 1101.23 on 886 degrees of freedom
## Residual deviance: 977.15 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3200
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 16848
## Std. Err.: 135055
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3010.041##to check model diag for univariate models
options(na.action="na.exclude")
library(dplyr) ##make sure lags are dplyr lags
##for SA3m1a avgWindSp ######
scatter.smooth(predict(SA3m1a, type='response'), rstandard(SA3m1a, type='deviance'), col='gray')
SA3m1a.resid<-residuals(SA3m1a, type="deviance")
SA3m1a.pred<-predict(SA3m1a, type="response")
length(SA3m1a.resid); length(SA3m1a.pred)## [1] 939## [1] 939pacf(SA3m1a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1,3-12,15,18 & 29
library(dplyr)
#ensure that the lags are dplyr lags
SA3m1a.ac<-update(SA3m1a,.~.+lag(SA3m1a.resid,1)+lag(SA3m1a.resid,3)+ lag(SA3m1a.resid,4)+
lag(SA3m1a.resid,5)+lag(SA3m1a.resid,6)+lag(SA3m1a.resid,7)+ lag(SA3m1a.resid,8)+
lag(SA3m1a.resid,9)+lag(SA3m1a.resid,10)+lag(SA3m1a.resid,11)+ lag(SA3m1a.resid,12)+
lag(SA3m1a.resid,15)+lag(SA3m1a.resid,18)+lag(SA3m1a.resid,29))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA3m1a.resid_ac<-residuals(SA3m1a.ac, type="deviance")
SA3m1a.pred_ac<-predict(SA3m1a.ac, type="response")
pacf(SA3m1a.resid_ac,na.action = na.omit) 
length(SA3m1a.pred_ac)## [1] 939length(SA3m1a.resid_ac)## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m1a.pred,lwd=1, col="blue")
plot(week$time,SA3m1a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m1a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m1a.pred_ac,lwd=1, col="blue")
plot(week$time,SA3m1a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m1a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA3m1a <- crosspred(cb1.avgWindSp, SA3m1a.ac, cen = 4.5, by=0.1,cumul=TRUE)
##for SA3m2a sun ######
summary(SA3m2a)##
## Call:
## glm.nb(formula = ptbBM ~ cb2.sun + sp_ptbBMns5, data = week,
## init.theta = 17573.9797, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3727 -0.8197 -0.1328 0.5694 2.9565
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 5.466e-01 7.208e+00 0.076 0.93955
## cb2.sunv1.l1 3.279e-02 1.591e-01 0.206 0.83669
## cb2.sunv1.l2 -1.493e-01 1.163e-01 -1.284 0.19914
## cb2.sunv2.l1 2.104e-01 6.288e-01 0.335 0.73785
## cb2.sunv2.l2 2.844e-01 4.338e-01 0.656 0.51209
## cb2.sunv3.l1 6.067e-01 2.355e-01 2.576 0.01000 *
## cb2.sunv3.l2 2.388e-01 1.645e-01 1.452 0.14652
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.212e+06 5.346e+06 0.414 0.67907
## sp_ptbBMns53 -1.541e-01 9.688e+00 -0.016 0.98731
## sp_ptbBMns54 9.027e-01 1.605e+00 0.562 0.57385
## sp_ptbBMns55 4.112e-01 9.267e-01 0.444 0.65726
## sp_ptbBMns56 -7.939e-03 7.519e-01 -0.011 0.99158
## sp_ptbBMns57 -2.044e+00 7.747e-01 -2.639 0.00833 **
## sp_ptbBMns58 7.074e-01 6.737e-01 1.050 0.29370
## sp_ptbBMns59 -1.377e-01 7.843e-01 -0.176 0.86059
## sp_ptbBMns510 -6.452e-01 7.399e-01 -0.872 0.38318
## sp_ptbBMns511 -7.617e-01 7.663e-01 -0.994 0.32023
## sp_ptbBMns512 -5.489e-01 7.468e-01 -0.735 0.46239
## sp_ptbBMns513 -3.688e-01 7.418e-01 -0.497 0.61907
## sp_ptbBMns514 -3.228e-01 7.175e-01 -0.450 0.65282
## sp_ptbBMns515 1.081e+00 7.763e-01 1.392 0.16377
## sp_ptbBMns516 -1.006e-01 7.674e-01 -0.131 0.89571
## sp_ptbBMns517 7.785e-02 7.510e-01 0.104 0.91744
## sp_ptbBMns518 5.355e-01 7.658e-01 0.699 0.48438
## sp_ptbBMns519 -1.444e+00 9.396e-01 -1.537 0.12438
## sp_ptbBMns520 -6.901e-01 8.207e-01 -0.841 0.40044
## sp_ptbBMns521 -2.008e-01 7.900e-01 -0.254 0.79937
## sp_ptbBMns522 1.690e-01 7.562e-01 0.224 0.82311
## sp_ptbBMns523 -9.989e-01 8.239e-01 -1.212 0.22539
## sp_ptbBMns524 4.373e-01 7.997e-01 0.547 0.58455
## sp_ptbBMns525 1.522e+00 8.542e-01 1.782 0.07480 .
## sp_ptbBMns526 1.085e+00 8.463e-01 1.282 0.19981
## sp_ptbBMns527 8.456e-01 7.990e-01 1.058 0.28992
## sp_ptbBMns528 1.188e+00 8.714e-01 1.363 0.17296
## sp_ptbBMns529 7.675e-01 8.081e-01 0.950 0.34222
## sp_ptbBMns530 -1.263e-01 8.160e-01 -0.155 0.87696
## sp_ptbBMns531 -6.384e-02 6.613e-01 -0.097 0.92309
## sp_ptbBMns532 -2.672e-01 6.477e-01 -0.412 0.67999
## sp_ptbBMns533 -1.145e+00 6.847e-01 -1.672 0.09444 .
## sp_ptbBMns534 3.953e-01 6.925e-01 0.571 0.56813
## sp_ptbBMns535 -6.465e-01 8.014e-01 -0.807 0.41985
## sp_ptbBMns536 8.034e-01 6.995e-01 1.149 0.25072
## sp_ptbBMns537 -3.413e-01 6.871e-01 -0.497 0.61937
## sp_ptbBMns538 3.942e-01 6.580e-01 0.599 0.54906
## sp_ptbBMns539 -5.521e-01 6.729e-01 -0.820 0.41195
## sp_ptbBMns540 1.110e+00 8.786e-01 1.263 0.20661
## sp_ptbBMns541 -1.339e-01 7.262e-01 -0.184 0.85367
## sp_ptbBMns542 -5.081e-01 7.239e-01 -0.702 0.48276
## sp_ptbBMns543 2.770e-01 7.407e-01 0.374 0.70846
## sp_ptbBMns544 -6.001e-01 7.665e-01 -0.783 0.43366
## sp_ptbBMns545 -1.194e+00 8.621e-01 -1.386 0.16590
## sp_ptbBMns546 -6.228e-02 7.106e-01 -0.088 0.93016
## sp_ptbBMns547 -8.362e-01 6.801e-01 -1.230 0.21888
## sp_ptbBMns548 4.391e-01 6.161e-01 0.713 0.47604
## sp_ptbBMns549 -2.848e-01 7.340e-01 -0.388 0.69799
## sp_ptbBMns550 8.935e-01 9.347e-01 0.956 0.33907
## sp_ptbBMns551 8.199e-01 8.302e-01 0.988 0.32337
## sp_ptbBMns552 1.049e+00 8.662e-01 1.212 0.22569
## sp_ptbBMns553 1.459e+00 8.053e-01 1.812 0.07003 .
## sp_ptbBMns554 8.031e-01 8.269e-01 0.971 0.33145
## sp_ptbBMns555 1.324e+00 8.233e-01 1.608 0.10776
## sp_ptbBMns556 1.214e+00 7.321e-01 1.658 0.09733 .
## sp_ptbBMns557 -3.406e-01 8.187e-01 -0.416 0.67743
## sp_ptbBMns558 -2.044e-01 7.356e-01 -0.278 0.78113
## sp_ptbBMns559 -1.811e-01 7.130e-01 -0.254 0.79949
## sp_ptbBMns560 -9.123e-01 7.533e-01 -1.211 0.22587
## sp_ptbBMns561 -3.871e-01 7.996e-01 -0.484 0.62836
## sp_ptbBMns562 1.216e+00 7.331e-01 1.660 0.09701 .
## sp_ptbBMns563 8.952e-03 8.632e-01 0.010 0.99173
## sp_ptbBMns564 1.174e+00 8.815e-01 1.332 0.18294
## sp_ptbBMns565 8.050e-01 8.764e-01 0.918 0.35837
## sp_ptbBMns566 1.238e+00 7.411e-01 1.670 0.09492 .
## sp_ptbBMns567 -4.519e-01 7.913e-01 -0.571 0.56796
## sp_ptbBMns568 1.766e+00 6.697e-01 2.637 0.00836 **
## sp_ptbBMns569 7.331e-01 7.185e-01 1.020 0.30753
## sp_ptbBMns570 1.288e+00 8.950e-01 1.439 0.15025
## sp_ptbBMns571 6.955e-01 7.233e-01 0.962 0.33626
## sp_ptbBMns572 5.884e-01 6.804e-01 0.865 0.38716
## sp_ptbBMns573 2.847e-01 6.928e-01 0.411 0.68109
## sp_ptbBMns574 -1.214e-01 6.682e-01 -0.182 0.85583
## sp_ptbBMns575 -1.218e+00 8.415e-01 -1.447 0.14793
## sp_ptbBMns576 -1.260e+00 7.272e-01 -1.732 0.08324 .
## sp_ptbBMns577 -1.630e+00 8.056e-01 -2.024 0.04301 *
## sp_ptbBMns578 -7.450e-01 8.249e-01 -0.903 0.36647
## sp_ptbBMns579 -8.901e-01 7.907e-01 -1.126 0.26026
## sp_ptbBMns580 9.987e-01 7.041e-01 1.418 0.15608
## sp_ptbBMns581 6.099e-01 6.560e-01 0.930 0.35246
## sp_ptbBMns582 7.729e-01 7.250e-01 1.066 0.28637
## sp_ptbBMns583 8.030e-01 6.669e-01 1.204 0.22854
## sp_ptbBMns584 1.098e+00 7.561e-01 1.452 0.14658
## sp_ptbBMns585 -1.908e-02 8.283e-01 -0.023 0.98162
## sp_ptbBMns586 3.675e-01 6.717e-01 0.547 0.58431
## sp_ptbBMns587 6.609e-01 5.501e-01 1.201 0.22963
## sp_ptbBMns588 1.401e+00 6.712e-01 2.088 0.03682 *
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17573.98) family taken to be 1)
##
## Null deviance: 1101.24 on 886 degrees of freedom
## Residual deviance: 972.32 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3195.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17574
## Std. Err.: 134180
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3005.203scatter.smooth(predict(SA3m2a, type='response'), rstandard(SA3m2a, type='deviance'), col='gray')
SA3m2a.resid<-residuals(SA3m2a, type="deviance")
SA3m2a.pred<-predict(SA3m2a, type="response")
length(SA3m2a.resid); length(SA3m2a.pred)## [1] 939## [1] 939pacf(SA3m2a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1,3-12,15 & 18
#ensure that the lags are dplyr lags
SA3m2a.ac<-update(SA3m2a,.~.+lag(SA3m2a.resid,1)+lag(SA3m2a.resid,3)+lag(SA3m2a.resid,4)+
lag(SA3m2a.resid,5)+lag(SA3m2a.resid,6)+lag(SA3m2a.resid,7)+lag(SA3m2a.resid,8)+
lag(SA3m2a.resid,9)+lag(SA3m2a.resid,10)+lag(SA3m2a.resid,11)+lag(SA3m2a.resid,12)+
lag(SA3m2a.resid,15)+lag(SA3m2a.resid,18))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA3m2a.resid_ac<-residuals(SA3m2a.ac, type="deviance")
SA3m2a.pred_ac<-predict(SA3m2a.ac, type="response")
pacf(SA3m2a.resid_ac,na.action = na.omit) 
length(SA3m2a.pred_ac); length(SA3m2a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m2a.pred,lwd=1, col="blue")
plot(week$time,SA3m2a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m2a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m2a.pred_ac,lwd=1, col="blue")
plot(week$time,SA3m2a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m2a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA3m2a <- crosspred(cb2.sun, SA3m2a.ac, cen = 50.7, by=0.1,cumul=TRUE)
##for SA3m3a RF ######
summary(SA3m3a)##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + sp_ptbBMns5, data = week, init.theta = 15595.28843,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3932 -0.8221 -0.1343 0.5622 2.8897
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.847e+00 2.141e+00 0.863 0.3884
## cb3.RFv1.l1 8.706e-02 2.229e-01 0.391 0.6961
## cb3.RFv1.l2 1.256e-01 1.419e-01 0.885 0.3760
## cb3.RFv2.l1 -1.372e-01 3.107e-01 -0.442 0.6588
## cb3.RFv2.l2 1.504e-01 2.034e-01 0.739 0.4598
## cb3.RFv3.l1 -5.048e-02 5.084e-01 -0.099 0.9209
## cb3.RFv3.l2 7.432e-02 3.526e-01 0.211 0.8331
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.303e+06 5.361e+06 0.430 0.6675
## sp_ptbBMns53 -2.913e+00 9.759e+00 -0.298 0.7653
## sp_ptbBMns54 1.060e+00 1.609e+00 0.659 0.5102
## sp_ptbBMns55 -4.793e-01 9.862e-01 -0.486 0.6269
## sp_ptbBMns56 5.063e-01 7.873e-01 0.643 0.5201
## sp_ptbBMns57 -1.552e+00 8.352e-01 -1.858 0.0632 .
## sp_ptbBMns58 7.621e-01 6.759e-01 1.128 0.2595
## sp_ptbBMns59 7.744e-01 8.081e-01 0.958 0.3379
## sp_ptbBMns510 -1.267e-01 7.456e-01 -0.170 0.8650
## sp_ptbBMns511 9.749e-02 7.648e-01 0.127 0.8986
## sp_ptbBMns512 1.376e-01 7.872e-01 0.175 0.8612
## sp_ptbBMns513 2.145e-01 7.341e-01 0.292 0.7701
## sp_ptbBMns514 4.553e-01 8.508e-01 0.535 0.5926
## sp_ptbBMns515 9.638e-01 7.553e-01 1.276 0.2019
## sp_ptbBMns516 -1.417e-01 7.573e-01 -0.187 0.8516
## sp_ptbBMns517 -7.015e-02 8.071e-01 -0.087 0.9307
## sp_ptbBMns518 7.806e-01 7.900e-01 0.988 0.3231
## sp_ptbBMns519 -1.026e+00 9.774e-01 -1.050 0.2938
## sp_ptbBMns520 2.553e-01 8.305e-01 0.307 0.7585
## sp_ptbBMns521 2.482e-01 8.192e-01 0.303 0.7619
## sp_ptbBMns522 6.761e-01 6.885e-01 0.982 0.3261
## sp_ptbBMns523 -7.674e-01 8.009e-01 -0.958 0.3380
## sp_ptbBMns524 4.873e-01 7.673e-01 0.635 0.5254
## sp_ptbBMns525 5.423e-01 6.345e-01 0.855 0.3927
## sp_ptbBMns526 5.333e-01 7.360e-01 0.725 0.4687
## sp_ptbBMns527 2.610e-01 6.860e-01 0.381 0.7036
## sp_ptbBMns528 2.669e-01 7.288e-01 0.366 0.7142
## sp_ptbBMns529 4.667e-01 7.230e-01 0.646 0.5186
## sp_ptbBMns530 -4.013e-01 7.624e-01 -0.526 0.5986
## sp_ptbBMns531 5.814e-01 6.638e-01 0.876 0.3811
## sp_ptbBMns532 6.771e-01 7.563e-01 0.895 0.3706
## sp_ptbBMns533 -4.642e-01 6.570e-01 -0.707 0.4799
## sp_ptbBMns534 8.015e-01 6.543e-01 1.225 0.2206
## sp_ptbBMns535 7.315e-02 7.319e-01 0.100 0.9204
## sp_ptbBMns536 9.969e-01 6.782e-01 1.470 0.1416
## sp_ptbBMns537 2.986e-01 7.405e-01 0.403 0.6868
## sp_ptbBMns538 5.245e-01 6.884e-01 0.762 0.4461
## sp_ptbBMns539 -1.791e-01 7.238e-01 -0.247 0.8046
## sp_ptbBMns540 6.900e-01 1.022e+00 0.675 0.4996
## sp_ptbBMns541 1.643e-01 7.149e-01 0.230 0.8183
## sp_ptbBMns542 -1.034e+00 8.198e-01 -1.261 0.2072
## sp_ptbBMns543 1.627e-01 7.484e-01 0.217 0.8279
## sp_ptbBMns544 -3.629e-01 8.775e-01 -0.414 0.6792
## sp_ptbBMns545 3.725e-01 1.044e+00 0.357 0.7213
## sp_ptbBMns546 7.102e-01 7.885e-01 0.901 0.3677
## sp_ptbBMns547 6.216e-01 7.737e-01 0.803 0.4217
## sp_ptbBMns548 9.988e-01 7.273e-01 1.373 0.1697
## sp_ptbBMns549 9.101e-01 7.441e-01 1.223 0.2213
## sp_ptbBMns550 -4.083e-02 8.740e-01 -0.047 0.9627
## sp_ptbBMns551 -4.631e-01 7.978e-01 -0.580 0.5616
## sp_ptbBMns552 -2.041e-01 6.354e-01 -0.321 0.7480
## sp_ptbBMns553 4.695e-01 6.637e-01 0.707 0.4794
## sp_ptbBMns554 4.446e-03 7.074e-01 0.006 0.9950
## sp_ptbBMns555 9.562e-01 7.475e-01 1.279 0.2008
## sp_ptbBMns556 1.237e+00 8.063e-01 1.534 0.1251
## sp_ptbBMns557 1.856e-01 7.755e-01 0.239 0.8109
## sp_ptbBMns558 4.637e-01 7.323e-01 0.633 0.5266
## sp_ptbBMns559 4.951e-01 6.804e-01 0.728 0.4668
## sp_ptbBMns560 -1.756e-01 7.515e-01 -0.234 0.8152
## sp_ptbBMns561 6.378e-01 6.420e-01 0.993 0.3205
## sp_ptbBMns562 7.228e-01 6.668e-01 1.084 0.2784
## sp_ptbBMns563 -6.946e-01 6.992e-01 -0.993 0.3205
## sp_ptbBMns564 8.190e-01 7.479e-01 1.095 0.2735
## sp_ptbBMns565 -2.495e-02 7.457e-01 -0.033 0.9733
## sp_ptbBMns566 7.215e-01 6.955e-01 1.037 0.2996
## sp_ptbBMns567 -6.392e-01 8.307e-01 -0.769 0.4416
## sp_ptbBMns568 8.859e-01 6.168e-01 1.436 0.1509
## sp_ptbBMns569 1.281e-01 7.461e-01 0.172 0.8637
## sp_ptbBMns570 7.129e-01 8.445e-01 0.844 0.3986
## sp_ptbBMns571 1.857e-01 7.798e-01 0.238 0.8117
## sp_ptbBMns572 8.178e-01 6.616e-01 1.236 0.2164
## sp_ptbBMns573 -9.975e-03 7.012e-01 -0.014 0.9887
## sp_ptbBMns574 6.764e-01 6.670e-01 1.014 0.3105
## sp_ptbBMns575 1.357e-01 9.146e-01 0.148 0.8821
## sp_ptbBMns576 -3.685e-01 6.712e-01 -0.549 0.5830
## sp_ptbBMns577 3.210e-01 7.836e-01 0.410 0.6821
## sp_ptbBMns578 3.797e-01 6.372e-01 0.596 0.5512
## sp_ptbBMns579 3.907e-01 8.332e-01 0.469 0.6391
## sp_ptbBMns580 4.818e-01 7.383e-01 0.653 0.5140
## sp_ptbBMns581 8.058e-01 6.633e-01 1.215 0.2244
## sp_ptbBMns582 4.439e-01 7.217e-01 0.615 0.5384
## sp_ptbBMns583 6.840e-01 6.367e-01 1.074 0.2827
## sp_ptbBMns584 8.207e-01 7.229e-01 1.135 0.2563
## sp_ptbBMns585 -1.039e-01 8.041e-01 -0.129 0.8972
## sp_ptbBMns586 4.408e-01 6.461e-01 0.682 0.4951
## sp_ptbBMns587 2.259e-01 5.833e-01 0.387 0.6986
## sp_ptbBMns588 4.589e-01 6.659e-01 0.689 0.4907
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(15595.29) family taken to be 1)
##
## Null deviance: 1101.22 on 886 degrees of freedom
## Residual deviance: 983.64 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3206.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15595
## Std. Err.: 130089
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3016.537scatter.smooth(predict(SA3m3a, type='response'), rstandard(SA3m3a, type='deviance'), col='gray')
SA3m3a.resid<-residuals(SA3m3a, type="deviance")
SA3m3a.pred<-predict(SA3m3a, type="response")
length(SA3m3a.resid); length(SA3m3a.pred)## [1] 939## [1] 939pacf(SA3m3a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1,3-12,15, 18 & 29
#ensure that the lags are dplyr lags
SA3m3a.ac<-update(SA3m3a,.~.+lag(SA3m3a.resid,1)+lag(SA3m3a.resid,3)+lag(SA3m3a.resid,4)+
lag(SA3m3a.resid,5)+lag(SA3m3a.resid,6)+lag(SA3m3a.resid,7)+lag(SA3m3a.resid,8)+
lag(SA3m3a.resid,9)+lag(SA3m3a.resid,10)+lag(SA3m3a.resid,11)+lag(SA3m3a.resid,12)+
lag(SA3m3a.resid,15)+lag(SA3m3a.resid,18)+lag(SA3m3a.resid,29)) ## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA3m3a.resid_ac<-residuals(SA3m3a.ac, type="deviance")
SA3m3a.pred_ac<-predict(SA3m3a.ac, type="response")
pacf(SA3m3a.resid_ac,na.action = na.omit) 
length(SA3m3a.pred_ac); length(SA3m3a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m3a.pred,lwd=1, col="blue")
plot(week$time,SA3m3a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m3a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m3a.pred_ac,lwd=1, col="blue")
plot(week$time,SA3m3a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m3a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices now
pred.SA3m3a <- crosspred(cb3.RF, SA3m3a.ac, cen = 44.9, by=0.1,cumul=TRUE)
##for SA3m5a minRH ######
summary(SA3m5a)##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBMns5, data = week,
## init.theta = 15825.53811, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4178 -0.8148 -0.1139 0.5813 2.9998
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.414e+01 9.125e+00 1.550 0.12121
## cb5.minRHv1.l1 -3.329e-01 2.014e-01 -1.654 0.09822 .
## cb5.minRHv1.l2 -9.099e-03 1.201e-01 -0.076 0.93963
## cb5.minRHv2.l1 -1.161e+00 7.819e-01 -1.485 0.13743
## cb5.minRHv2.l2 -3.286e-01 5.247e-01 -0.626 0.53108
## cb5.minRHv3.l1 -6.686e-01 4.523e-01 -1.478 0.13939
## cb5.minRHv3.l2 -1.229e-01 2.934e-01 -0.419 0.67518
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.227e+06 5.339e+06 0.417 0.67667
## sp_ptbBMns53 -1.952e+00 9.664e+00 -0.202 0.83996
## sp_ptbBMns54 8.671e-01 1.617e+00 0.536 0.59184
## sp_ptbBMns55 -2.462e-01 9.454e-01 -0.260 0.79458
## sp_ptbBMns56 3.776e-01 8.730e-01 0.433 0.66534
## sp_ptbBMns57 -2.197e+00 8.272e-01 -2.656 0.00791 **
## sp_ptbBMns58 -2.096e-01 7.209e-01 -0.291 0.77119
## sp_ptbBMns59 -8.091e-01 8.039e-01 -1.006 0.31421
## sp_ptbBMns510 -1.143e+00 8.936e-01 -1.279 0.20086
## sp_ptbBMns511 -1.552e+00 1.051e+00 -1.477 0.13962
## sp_ptbBMns512 -1.075e+00 8.438e-01 -1.274 0.20255
## sp_ptbBMns513 -7.352e-01 8.327e-01 -0.883 0.37726
## sp_ptbBMns514 -9.332e-01 9.105e-01 -1.025 0.30542
## sp_ptbBMns515 1.000e+00 8.129e-01 1.230 0.21863
## sp_ptbBMns516 -3.255e-01 7.961e-01 -0.409 0.68260
## sp_ptbBMns517 -4.119e-01 7.143e-01 -0.577 0.56414
## sp_ptbBMns518 3.106e-01 6.959e-01 0.446 0.65538
## sp_ptbBMns519 -1.767e+00 7.707e-01 -2.293 0.02186 *
## sp_ptbBMns520 -2.331e-02 8.041e-01 -0.029 0.97687
## sp_ptbBMns521 -8.950e-01 7.965e-01 -1.124 0.26111
## sp_ptbBMns522 -8.213e-01 1.000e+00 -0.821 0.41148
## sp_ptbBMns523 -2.865e+00 1.178e+00 -2.432 0.01502 *
## sp_ptbBMns524 -1.965e+00 1.059e+00 -1.856 0.06344 .
## sp_ptbBMns525 -1.951e+00 1.358e+00 -1.437 0.15079
## sp_ptbBMns526 -1.340e+00 1.098e+00 -1.220 0.22249
## sp_ptbBMns527 -1.014e+00 9.507e-01 -1.067 0.28603
## sp_ptbBMns528 -5.400e-01 8.572e-01 -0.630 0.52871
## sp_ptbBMns529 -6.991e-01 8.506e-01 -0.822 0.41113
## sp_ptbBMns530 -1.033e+00 8.364e-01 -1.235 0.21700
## sp_ptbBMns531 -3.802e-01 7.927e-01 -0.480 0.63148
## sp_ptbBMns532 -4.809e-01 7.305e-01 -0.658 0.51035
## sp_ptbBMns533 -1.364e+00 8.241e-01 -1.655 0.09783 .
## sp_ptbBMns534 -4.898e-01 7.179e-01 -0.682 0.49509
## sp_ptbBMns535 -1.190e+00 9.089e-01 -1.309 0.19060
## sp_ptbBMns536 -4.386e-01 8.215e-01 -0.534 0.59337
## sp_ptbBMns537 -1.064e+00 8.104e-01 -1.313 0.18926
## sp_ptbBMns538 -2.890e-01 7.425e-01 -0.389 0.69712
## sp_ptbBMns539 -1.218e+00 7.792e-01 -1.563 0.11795
## sp_ptbBMns540 3.989e-01 9.292e-01 0.429 0.66769
## sp_ptbBMns541 -3.427e-01 7.371e-01 -0.465 0.64193
## sp_ptbBMns542 -1.668e+00 9.568e-01 -1.743 0.08127 .
## sp_ptbBMns543 -8.418e-01 9.407e-01 -0.895 0.37088
## sp_ptbBMns544 -1.592e+00 8.472e-01 -1.879 0.06028 .
## sp_ptbBMns545 -1.548e+00 1.239e+00 -1.250 0.21135
## sp_ptbBMns546 -1.195e+00 1.012e+00 -1.181 0.23742
## sp_ptbBMns547 -4.381e-01 7.003e-01 -0.626 0.53152
## sp_ptbBMns548 2.059e-01 5.566e-01 0.370 0.71142
## sp_ptbBMns549 -3.266e-01 6.453e-01 -0.506 0.61273
## sp_ptbBMns550 -2.425e-01 8.999e-01 -0.269 0.78757
## sp_ptbBMns551 -5.284e-01 7.463e-01 -0.708 0.47892
## sp_ptbBMns552 -2.186e-01 6.778e-01 -0.322 0.74710
## sp_ptbBMns553 6.017e-02 7.031e-01 0.086 0.93181
## sp_ptbBMns554 -2.981e-01 5.869e-01 -0.508 0.61151
## sp_ptbBMns555 1.993e-01 8.621e-01 0.231 0.81722
## sp_ptbBMns556 6.383e-01 7.610e-01 0.839 0.40160
## sp_ptbBMns557 -1.050e+00 8.360e-01 -1.256 0.20926
## sp_ptbBMns558 -2.767e-01 6.268e-01 -0.441 0.65886
## sp_ptbBMns559 -4.447e-01 7.019e-01 -0.633 0.52641
## sp_ptbBMns560 -6.508e-01 8.602e-01 -0.757 0.44927
## sp_ptbBMns561 -3.764e-02 9.179e-01 -0.041 0.96730
## sp_ptbBMns562 7.099e-01 6.727e-01 1.055 0.29125
## sp_ptbBMns563 -7.556e-01 7.202e-01 -1.049 0.29408
## sp_ptbBMns564 5.117e-01 7.303e-01 0.701 0.48357
## sp_ptbBMns565 9.909e-01 1.245e+00 0.796 0.42608
## sp_ptbBMns566 1.493e+00 8.836e-01 1.689 0.09118 .
## sp_ptbBMns567 8.977e-03 9.261e-01 0.010 0.99227
## sp_ptbBMns568 2.039e+00 8.855e-01 2.303 0.02127 *
## sp_ptbBMns569 1.211e+00 1.061e+00 1.141 0.25396
## sp_ptbBMns570 1.079e+00 1.091e+00 0.989 0.32256
## sp_ptbBMns571 1.525e+00 8.927e-01 1.708 0.08767 .
## sp_ptbBMns572 1.554e+00 8.162e-01 1.904 0.05696 .
## sp_ptbBMns573 1.139e+00 8.290e-01 1.374 0.16955
## sp_ptbBMns574 9.574e-01 6.830e-01 1.402 0.16095
## sp_ptbBMns575 8.485e-01 8.559e-01 0.991 0.32149
## sp_ptbBMns576 4.891e-01 6.837e-01 0.715 0.47443
## sp_ptbBMns577 6.335e-01 7.051e-01 0.899 0.36892
## sp_ptbBMns578 4.844e-01 5.988e-01 0.809 0.41858
## sp_ptbBMns579 4.633e-01 5.886e-01 0.787 0.43118
## sp_ptbBMns580 1.226e-01 6.534e-01 0.188 0.85113
## sp_ptbBMns581 8.279e-01 7.745e-01 1.069 0.28507
## sp_ptbBMns582 1.979e-01 6.703e-01 0.295 0.76783
## sp_ptbBMns583 1.039e+00 7.758e-01 1.339 0.18051
## sp_ptbBMns584 7.137e-01 6.704e-01 1.065 0.28709
## sp_ptbBMns585 4.546e-01 8.681e-01 0.524 0.60051
## sp_ptbBMns586 9.236e-01 8.327e-01 1.109 0.26738
## sp_ptbBMns587 8.453e-01 6.350e-01 1.331 0.18318
## sp_ptbBMns588 5.890e-01 6.610e-01 0.891 0.37288
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(15825.54) family taken to be 1)
##
## Null deviance: 1101.22 on 886 degrees of freedom
## Residual deviance: 981.24 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3204.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 15826
## Std. Err.: 130488
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3014.141scatter.smooth(predict(SA3m5a, type='response'), rstandard(SA3m5a, type='deviance'), col='gray')
SA3m5a.resid<-residuals(SA3m5a, type="deviance")
SA3m5a.pred<-predict(SA3m5a, type="response")
length(SA3m5a.resid); length(SA3m5a.pred)## [1] 939## [1] 939pacf(SA3m5a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1,3-12,15, 18 & 29
#ensure that the lags are dplyr lags
SA3m5a.ac<-update(SA3m5a,.~.+lag(SA3m5a.resid,1)+lag(SA3m5a.resid,3)+lag(SA3m5a.resid,4)+
lag(SA3m5a.resid,5)+lag(SA3m5a.resid,6)+lag(SA3m5a.resid,7)+lag(SA3m5a.resid,8)+
lag(SA3m5a.resid,9)+lag(SA3m5a.resid,10)+lag(SA3m5a.resid,11)+lag(SA3m5a.resid,12)+
lag(SA3m5a.resid,15)+lag(SA3m5a.resid,18)+lag(SA3m5a.resid,29)) ## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(SA3m5a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBMns5 + lag(SA3m5a.resid,
## 1) + lag(SA3m5a.resid, 3) + lag(SA3m5a.resid, 4) + lag(SA3m5a.resid,
## 5) + lag(SA3m5a.resid, 6) + lag(SA3m5a.resid, 7) + lag(SA3m5a.resid,
## 8) + lag(SA3m5a.resid, 9) + lag(SA3m5a.resid, 10) + lag(SA3m5a.resid,
## 11) + lag(SA3m5a.resid, 12) + lag(SA3m5a.resid, 15) + lag(SA3m5a.resid,
## 18) + lag(SA3m5a.resid, 29), data = week, init.theta = 35404.41833,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.42413 -0.72890 -0.08354 0.54791 2.38056
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.869e+01 9.295e+00 2.011 0.04431 *
## cb5.minRHv1.l1 -4.031e-01 2.054e-01 -1.963 0.04964 *
## cb5.minRHv1.l2 4.324e-02 1.235e-01 0.350 0.72621
## cb5.minRHv2.l1 -1.413e+00 8.036e-01 -1.758 0.07878 .
## cb5.minRHv2.l2 1.492e-02 5.404e-01 0.028 0.97798
## cb5.minRHv3.l1 -4.094e-01 4.749e-01 -0.862 0.38868
## cb5.minRHv3.l2 1.329e-01 3.074e-01 0.432 0.66562
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 NA NA NA NA
## sp_ptbBMns53 NA NA NA NA
## sp_ptbBMns54 NA NA NA NA
## sp_ptbBMns55 1.349e+03 1.233e+03 1.094 0.27395
## sp_ptbBMns56 -7.495e+00 9.434e+00 -0.794 0.42691
## sp_ptbBMns57 -2.053e+00 1.598e+00 -1.284 0.19898
## sp_ptbBMns58 -4.434e-01 8.575e-01 -0.517 0.60514
## sp_ptbBMns59 -1.982e-01 8.225e-01 -0.241 0.80958
## sp_ptbBMns510 -1.029e+00 9.159e-01 -1.124 0.26112
## sp_ptbBMns511 -3.773e-01 1.080e+00 -0.349 0.72693
## sp_ptbBMns512 -9.577e-01 8.651e-01 -1.107 0.26826
## sp_ptbBMns513 1.080e-01 8.344e-01 0.129 0.89704
## sp_ptbBMns514 -6.125e-01 9.273e-01 -0.661 0.50891
## sp_ptbBMns515 1.217e+00 8.251e-01 1.475 0.14012
## sp_ptbBMns516 -1.907e-01 8.096e-01 -0.236 0.81376
## sp_ptbBMns517 -7.220e-01 7.148e-01 -1.010 0.31242
## sp_ptbBMns518 1.399e-01 6.918e-01 0.202 0.83976
## sp_ptbBMns519 -1.622e+00 7.584e-01 -2.138 0.03249 *
## sp_ptbBMns520 -3.498e-01 8.206e-01 -0.426 0.66994
## sp_ptbBMns521 -2.405e-01 8.117e-01 -0.296 0.76706
## sp_ptbBMns522 -1.082e+00 1.007e+00 -1.074 0.28294
## sp_ptbBMns523 -3.356e+00 1.190e+00 -2.821 0.00479 **
## sp_ptbBMns524 -1.251e+00 1.075e+00 -1.164 0.24452
## sp_ptbBMns525 -1.646e+00 1.379e+00 -1.194 0.23263
## sp_ptbBMns526 -2.949e-01 1.135e+00 -0.260 0.79496
## sp_ptbBMns527 -4.770e-01 9.564e-01 -0.499 0.61801
## sp_ptbBMns528 -3.377e-01 8.637e-01 -0.391 0.69580
## sp_ptbBMns529 7.842e-02 8.725e-01 0.090 0.92839
## sp_ptbBMns530 -1.118e+00 8.203e-01 -1.363 0.17302
## sp_ptbBMns531 7.627e-02 8.118e-01 0.094 0.92515
## sp_ptbBMns532 -6.160e-01 7.511e-01 -0.820 0.41215
## sp_ptbBMns533 -1.251e+00 8.259e-01 -1.514 0.12993
## sp_ptbBMns534 -6.698e-01 7.283e-01 -0.920 0.35776
## sp_ptbBMns535 -6.570e-01 9.304e-01 -0.706 0.48005
## sp_ptbBMns536 -1.315e-01 8.213e-01 -0.160 0.87277
## sp_ptbBMns537 -6.565e-01 8.261e-01 -0.795 0.42677
## sp_ptbBMns538 2.594e-01 7.530e-01 0.344 0.73047
## sp_ptbBMns539 -1.407e+00 7.834e-01 -1.797 0.07241 .
## sp_ptbBMns540 1.116e+00 9.444e-01 1.182 0.23717
## sp_ptbBMns541 -4.900e-01 7.345e-01 -0.667 0.50473
## sp_ptbBMns542 -1.881e+00 9.779e-01 -1.923 0.05443 .
## sp_ptbBMns543 -1.767e+00 9.475e-01 -1.865 0.06225 .
## sp_ptbBMns544 -1.702e+00 8.519e-01 -1.998 0.04577 *
## sp_ptbBMns545 -1.586e+00 1.239e+00 -1.280 0.20065
## sp_ptbBMns546 -8.967e-01 1.014e+00 -0.885 0.37632
## sp_ptbBMns547 -9.723e-02 7.196e-01 -0.135 0.89252
## sp_ptbBMns548 5.098e-02 5.616e-01 0.091 0.92767
## sp_ptbBMns549 -3.945e-02 6.352e-01 -0.062 0.95048
## sp_ptbBMns550 2.625e-03 9.171e-01 0.003 0.99772
## sp_ptbBMns551 6.973e-02 7.487e-01 0.093 0.92580
## sp_ptbBMns552 -4.365e-01 6.756e-01 -0.646 0.51821
## sp_ptbBMns553 2.899e-01 7.476e-01 0.388 0.69815
## sp_ptbBMns554 -3.466e-01 6.050e-01 -0.573 0.56676
## sp_ptbBMns555 3.209e-01 8.711e-01 0.368 0.71257
## sp_ptbBMns556 1.866e+00 7.754e-01 2.407 0.01608 *
## sp_ptbBMns557 -1.540e+00 8.457e-01 -1.821 0.06858 .
## sp_ptbBMns558 5.013e-01 6.286e-01 0.797 0.42520
## sp_ptbBMns559 -1.198e+00 7.130e-01 -1.680 0.09288 .
## sp_ptbBMns560 2.399e-01 8.933e-01 0.268 0.78832
## sp_ptbBMns561 -6.255e-01 9.269e-01 -0.675 0.49978
## sp_ptbBMns562 1.541e+00 6.963e-01 2.214 0.02685 *
## sp_ptbBMns563 -1.040e+00 7.327e-01 -1.420 0.15572
## sp_ptbBMns564 8.482e-02 7.344e-01 0.115 0.90805
## sp_ptbBMns565 8.171e-01 1.256e+00 0.650 0.51544
## sp_ptbBMns566 1.354e+00 8.995e-01 1.505 0.13228
## sp_ptbBMns567 -4.199e-01 9.370e-01 -0.448 0.65402
## sp_ptbBMns568 8.925e-01 9.179e-01 0.972 0.33091
## sp_ptbBMns569 5.278e-01 1.098e+00 0.480 0.63087
## sp_ptbBMns570 -4.574e-01 1.099e+00 -0.416 0.67738
## sp_ptbBMns571 1.732e+00 9.047e-01 1.915 0.05555 .
## sp_ptbBMns572 7.622e-01 8.349e-01 0.913 0.36128
## sp_ptbBMns573 6.879e-01 8.456e-01 0.813 0.41597
## sp_ptbBMns574 -6.235e-02 7.003e-01 -0.089 0.92905
## sp_ptbBMns575 1.491e+00 8.592e-01 1.736 0.08263 .
## sp_ptbBMns576 -6.590e-01 7.080e-01 -0.931 0.35201
## sp_ptbBMns577 1.189e+00 7.223e-01 1.646 0.09972 .
## sp_ptbBMns578 -3.987e-01 6.155e-01 -0.648 0.51719
## sp_ptbBMns579 1.371e+00 6.087e-01 2.252 0.02433 *
## sp_ptbBMns580 -9.926e-01 6.664e-01 -1.489 0.13637
## sp_ptbBMns581 1.653e+00 7.850e-01 2.106 0.03524 *
## sp_ptbBMns582 -1.153e+00 6.917e-01 -1.667 0.09551 .
## sp_ptbBMns583 2.083e+00 8.100e-01 2.571 0.01013 *
## sp_ptbBMns584 -1.740e-01 6.557e-01 -0.265 0.79067
## sp_ptbBMns585 6.122e-01 9.059e-01 0.676 0.49921
## sp_ptbBMns586 4.551e-01 8.629e-01 0.527 0.59790
## sp_ptbBMns587 5.255e-01 6.496e-01 0.809 0.41851
## sp_ptbBMns588 2.533e-01 6.482e-01 0.391 0.69593
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## lag(SA3m5a.resid, 1) -1.835e-01 2.501e-02 -7.334 2.23e-13 ***
## lag(SA3m5a.resid, 3) -2.168e-01 2.600e-02 -8.339 < 2e-16 ***
## lag(SA3m5a.resid, 4) -1.975e-01 2.734e-02 -7.226 4.97e-13 ***
## lag(SA3m5a.resid, 5) -2.064e-01 2.718e-02 -7.595 3.09e-14 ***
## lag(SA3m5a.resid, 6) -1.860e-01 2.777e-02 -6.698 2.11e-11 ***
## lag(SA3m5a.resid, 7) -2.039e-01 2.732e-02 -7.463 8.43e-14 ***
## lag(SA3m5a.resid, 8) -2.170e-01 2.792e-02 -7.770 7.84e-15 ***
## lag(SA3m5a.resid, 9) -1.589e-01 2.648e-02 -6.003 1.94e-09 ***
## lag(SA3m5a.resid, 10) -1.782e-01 2.630e-02 -6.775 1.25e-11 ***
## lag(SA3m5a.resid, 11) -1.211e-01 2.667e-02 -4.539 5.64e-06 ***
## lag(SA3m5a.resid, 12) -1.118e-01 2.555e-02 -4.375 1.21e-05 ***
## lag(SA3m5a.resid, 15) -6.587e-02 2.477e-02 -2.659 0.00783 **
## lag(SA3m5a.resid, 18) -4.248e-02 2.401e-02 -1.769 0.07684 .
## lag(SA3m5a.resid, 29) 7.129e-03 2.304e-02 0.309 0.75696
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(35404.42) family taken to be 1)
##
## Null deviance: 1050.09 on 857 degrees of freedom
## Residual deviance: 766.79 on 753 degrees of freedom
## (81 observations deleted due to missingness)
## AIC: 2953.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 35404
## Std. Err.: 173634
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2741.502SA3m5a.resid_ac<-residuals(SA3m5a.ac, type="deviance")
SA3m5a.pred_ac<-predict(SA3m5a.ac, type="response")
pacf(SA3m5a.resid_ac,na.action = na.omit) 
length(SA3m5a.pred_ac); length(SA3m5a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m5a.pred,lwd=1, col="blue")
plot(week$time,SA3m5a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m5a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m5a.pred_ac,lwd=1, col="blue")
plot(week$time,SA3m5a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m5a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA3m5a <- crosspred(cb5.minRH, SA3m5a.ac, cen = 63, by=0.1,cumul=TRUE)
##for SA3m9a minT ######
summary(SA3m9a)##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBMns5, data = week,
## init.theta = 17448.55852, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.3977 -0.8342 -0.1111 0.5435 2.8324
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.883e-01 8.973e+00 0.032 0.97437
## cb9.minTv1.l1 2.738e-01 2.171e-01 1.261 0.20724
## cb9.minTv1.l2 -3.795e-01 1.497e-01 -2.535 0.01123 *
## cb9.minTv2.l1 1.316e-01 7.587e-01 0.173 0.86226
## cb9.minTv2.l2 -8.424e-01 5.516e-01 -1.527 0.12675
## cb9.minTv3.l1 1.779e-01 4.041e-01 0.440 0.65973
## cb9.minTv3.l2 2.391e-01 3.077e-01 0.777 0.43720
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.288e+06 5.352e+06 0.427 0.66906
## sp_ptbBMns53 -3.215e+00 9.649e+00 -0.333 0.73902
## sp_ptbBMns54 1.517e-01 1.592e+00 0.095 0.92408
## sp_ptbBMns55 -1.351e+00 1.013e+00 -1.333 0.18249
## sp_ptbBMns56 -7.888e-01 8.786e-01 -0.898 0.36928
## sp_ptbBMns57 -2.564e+00 9.731e-01 -2.634 0.00843 **
## sp_ptbBMns58 2.176e-01 7.092e-01 0.307 0.75901
## sp_ptbBMns59 -5.288e-01 7.909e-01 -0.669 0.50374
## sp_ptbBMns510 -9.688e-01 7.792e-01 -1.243 0.21372
## sp_ptbBMns511 -8.698e-01 8.503e-01 -1.023 0.30632
## sp_ptbBMns512 -6.818e-01 8.364e-01 -0.815 0.41493
## sp_ptbBMns513 -1.559e+00 9.759e-01 -1.598 0.11015
## sp_ptbBMns514 -1.003e+00 8.471e-01 -1.184 0.23656
## sp_ptbBMns515 -1.378e-01 7.560e-01 -0.182 0.85539
## sp_ptbBMns516 -1.464e+00 7.654e-01 -1.913 0.05580 .
## sp_ptbBMns517 -5.405e-01 6.984e-01 -0.774 0.43897
## sp_ptbBMns518 2.708e-01 6.889e-01 0.393 0.69421
## sp_ptbBMns519 -1.722e+00 8.219e-01 -2.095 0.03620 *
## sp_ptbBMns520 8.761e-01 7.094e-01 1.235 0.21687
## sp_ptbBMns521 -2.945e-01 7.139e-01 -0.413 0.67996
## sp_ptbBMns522 8.915e-01 7.909e-01 1.127 0.25966
## sp_ptbBMns523 -2.045e-01 7.899e-01 -0.259 0.79576
## sp_ptbBMns524 1.445e+00 1.181e+00 1.223 0.22131
## sp_ptbBMns525 2.467e+00 1.185e+00 2.081 0.03740 *
## sp_ptbBMns526 2.202e+00 1.740e+00 1.266 0.20551
## sp_ptbBMns527 9.763e-01 1.538e+00 0.635 0.52570
## sp_ptbBMns528 5.225e-01 1.636e+00 0.319 0.74948
## sp_ptbBMns529 -5.243e-01 1.138e+00 -0.461 0.64496
## sp_ptbBMns530 -2.397e-01 1.058e+00 -0.227 0.82079
## sp_ptbBMns531 -2.175e-01 7.228e-01 -0.301 0.76345
## sp_ptbBMns532 4.706e-01 7.048e-01 0.668 0.50429
## sp_ptbBMns533 1.320e-01 6.889e-01 0.192 0.84806
## sp_ptbBMns534 5.734e-01 8.025e-01 0.715 0.47487
## sp_ptbBMns535 1.165e+00 8.056e-01 1.446 0.14826
## sp_ptbBMns536 1.612e+00 8.279e-01 1.947 0.05152 .
## sp_ptbBMns537 1.271e+00 1.011e+00 1.257 0.20883
## sp_ptbBMns538 1.718e+00 1.017e+00 1.690 0.09112 .
## sp_ptbBMns539 -1.008e+00 1.003e+00 -1.005 0.31489
## sp_ptbBMns540 1.304e+00 9.737e-01 1.339 0.18068
## sp_ptbBMns541 -6.513e-02 7.696e-01 -0.085 0.93256
## sp_ptbBMns542 -1.059e+00 7.072e-01 -1.498 0.13414
## sp_ptbBMns543 2.650e-01 6.359e-01 0.417 0.67694
## sp_ptbBMns544 -1.705e+00 7.212e-01 -2.364 0.01809 *
## sp_ptbBMns545 -6.838e-01 8.665e-01 -0.789 0.43003
## sp_ptbBMns546 -7.905e-01 1.126e+00 -0.702 0.48261
## sp_ptbBMns547 -1.378e+00 9.923e-01 -1.389 0.16497
## sp_ptbBMns548 -7.710e-01 9.973e-01 -0.773 0.43948
## sp_ptbBMns549 -1.865e+00 1.062e+00 -1.757 0.07898 .
## sp_ptbBMns550 -9.185e-01 9.479e-01 -0.969 0.33254
## sp_ptbBMns551 -2.664e-01 7.230e-01 -0.368 0.71257
## sp_ptbBMns552 -3.145e-01 6.704e-01 -0.469 0.63894
## sp_ptbBMns553 2.668e-01 6.800e-01 0.392 0.69478
## sp_ptbBMns554 -1.088e+00 7.108e-01 -1.530 0.12592
## sp_ptbBMns555 -8.039e-02 7.373e-01 -0.109 0.91318
## sp_ptbBMns556 -1.090e-01 7.574e-01 -0.144 0.88552
## sp_ptbBMns557 -6.145e-01 7.593e-01 -0.809 0.41831
## sp_ptbBMns558 -6.226e-01 7.609e-01 -0.818 0.41325
## sp_ptbBMns559 -8.698e-01 8.036e-01 -1.082 0.27906
## sp_ptbBMns560 -1.835e+00 9.901e-01 -1.854 0.06380 .
## sp_ptbBMns561 -1.411e+00 1.126e+00 -1.254 0.21002
## sp_ptbBMns562 -7.120e-01 1.093e+00 -0.652 0.51471
## sp_ptbBMns563 -2.494e+00 1.103e+00 -2.261 0.02375 *
## sp_ptbBMns564 -2.036e+00 1.144e+00 -1.779 0.07520 .
## sp_ptbBMns565 -1.048e+00 8.401e-01 -1.248 0.21202
## sp_ptbBMns566 -3.034e-01 9.312e-01 -0.326 0.74453
## sp_ptbBMns567 -2.010e+00 9.807e-01 -2.050 0.04037 *
## sp_ptbBMns568 -8.636e-01 9.836e-01 -0.878 0.37992
## sp_ptbBMns569 -2.665e+00 1.079e+00 -2.470 0.01351 *
## sp_ptbBMns570 -2.376e+00 1.231e+00 -1.931 0.05354 .
## sp_ptbBMns571 -1.093e+00 1.148e+00 -0.952 0.34091
## sp_ptbBMns572 -7.583e-02 1.203e+00 -0.063 0.94974
## sp_ptbBMns573 -1.847e+00 1.242e+00 -1.487 0.13700
## sp_ptbBMns574 -1.941e+00 1.273e+00 -1.524 0.12740
## sp_ptbBMns575 -1.930e+00 2.179e+00 -0.886 0.37583
## sp_ptbBMns576 -3.890e-01 3.155e+00 -0.123 0.90187
## sp_ptbBMns577 -1.156e+00 2.827e+00 -0.409 0.68263
## sp_ptbBMns578 -2.669e+00 2.857e+00 -0.934 0.35010
## sp_ptbBMns579 -5.529e+00 3.295e+00 -1.678 0.09335 .
## sp_ptbBMns580 -4.665e+00 2.548e+00 -1.831 0.06714 .
## sp_ptbBMns581 -2.028e+00 1.200e+00 -1.691 0.09093 .
## sp_ptbBMns582 -2.046e+00 1.160e+00 -1.763 0.07793 .
## sp_ptbBMns583 -8.936e-01 9.637e-01 -0.927 0.35380
## sp_ptbBMns584 -3.209e-01 7.035e-01 -0.456 0.64835
## sp_ptbBMns585 -1.636e-01 7.235e-01 -0.226 0.82115
## sp_ptbBMns586 -3.060e-01 6.221e-01 -0.492 0.62279
## sp_ptbBMns587 1.331e-02 5.364e-01 0.025 0.98020
## sp_ptbBMns588 5.988e-01 6.864e-01 0.872 0.38300
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(17448.56) family taken to be 1)
##
## Null deviance: 1101.2 on 886 degrees of freedom
## Residual deviance: 974.2 on 793 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3197.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 17449
## Std. Err.: 134101
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -3007.088scatter.smooth(predict(SA3m9a, type='response'), rstandard(SA3m9a, type='deviance'), col='gray')
SA3m9a.resid<-residuals(SA3m9a, type="deviance")
SA3m9a.pred<-predict(SA3m9a, type="response")
length(SA3m9a.resid); length(SA3m9a.pred)## [1] 939## [1] 939pacf(SA3m9a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1,3-12,15, 18 & 29
#ensure that the lags are dplyr lags
SA3m9a.ac<-update(SA3m9a,.~.+lag(SA3m9a.resid,1)+lag(SA3m9a.resid,3)+lag(SA3m9a.resid,4)+
lag(SA3m9a.resid,5)+lag(SA3m9a.resid,6)+lag(SA3m9a.resid,7)+lag(SA3m9a.resid,8)+
lag(SA3m9a.resid,9)+lag(SA3m9a.resid,10)+lag(SA3m9a.resid,11)+lag(SA3m9a.resid,12)+
lag(SA3m9a.resid,15)+lag(SA3m9a.resid,18)+lag(SA3m9a.resid,29))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(SA3m9a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBMns5 + lag(SA3m9a.resid,
## 1) + lag(SA3m9a.resid, 3) + lag(SA3m9a.resid, 4) + lag(SA3m9a.resid,
## 5) + lag(SA3m9a.resid, 6) + lag(SA3m9a.resid, 7) + lag(SA3m9a.resid,
## 8) + lag(SA3m9a.resid, 9) + lag(SA3m9a.resid, 10) + lag(SA3m9a.resid,
## 11) + lag(SA3m9a.resid, 12) + lag(SA3m9a.resid, 15) + lag(SA3m9a.resid,
## 18) + lag(SA3m9a.resid, 29), data = week, init.theta = 35327.4745,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.49377 -0.72605 -0.08221 0.54480 2.50584
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.544e+00 9.357e+00 0.272 0.78572
## cb9.minTv1.l1 5.624e-02 2.254e-01 0.250 0.80296
## cb9.minTv1.l2 -4.386e-01 1.547e-01 -2.836 0.00457 **
## cb9.minTv2.l1 1.180e-01 7.878e-01 0.150 0.88091
## cb9.minTv2.l2 -1.224e+00 5.644e-01 -2.168 0.03013 *
## cb9.minTv3.l1 3.035e-01 4.132e-01 0.735 0.46264
## cb9.minTv3.l2 5.723e-02 3.118e-01 0.184 0.85437
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 NA NA NA NA
## sp_ptbBMns53 NA NA NA NA
## sp_ptbBMns54 NA NA NA NA
## sp_ptbBMns55 1.360e+03 1.209e+03 1.124 0.26086
## sp_ptbBMns56 -9.077e+00 9.202e+00 -0.986 0.32392
## sp_ptbBMns57 -1.931e+00 1.582e+00 -1.221 0.22219
## sp_ptbBMns58 3.859e-01 9.039e-01 0.427 0.66946
## sp_ptbBMns59 -3.440e-01 8.002e-01 -0.430 0.66733
## sp_ptbBMns510 -8.312e-01 7.946e-01 -1.046 0.29556
## sp_ptbBMns511 -4.159e-01 8.723e-01 -0.477 0.63351
## sp_ptbBMns512 -9.993e-01 8.566e-01 -1.167 0.24336
## sp_ptbBMns513 -7.795e-01 9.781e-01 -0.797 0.42545
## sp_ptbBMns514 -1.036e+00 8.539e-01 -1.214 0.22481
## sp_ptbBMns515 1.295e-01 7.736e-01 0.167 0.86708
## sp_ptbBMns516 -1.332e+00 7.759e-01 -1.717 0.08598 .
## sp_ptbBMns517 -6.204e-01 7.023e-01 -0.883 0.37700
## sp_ptbBMns518 -2.328e-01 6.768e-01 -0.344 0.73085
## sp_ptbBMns519 -1.855e+00 8.190e-01 -2.264 0.02355 *
## sp_ptbBMns520 2.002e-02 7.286e-01 0.027 0.97808
## sp_ptbBMns521 -7.566e-01 7.237e-01 -1.045 0.29584
## sp_ptbBMns522 8.784e-02 7.933e-01 0.111 0.91184
## sp_ptbBMns523 -1.061e+00 8.142e-01 -1.303 0.19272
## sp_ptbBMns524 1.121e+00 1.205e+00 0.931 0.35203
## sp_ptbBMns525 1.350e+00 1.207e+00 1.119 0.26301
## sp_ptbBMns526 9.549e-01 1.815e+00 0.526 0.59879
## sp_ptbBMns527 -6.080e-01 1.596e+00 -0.381 0.70326
## sp_ptbBMns528 -1.215e+00 1.694e+00 -0.717 0.47336
## sp_ptbBMns529 -1.693e+00 1.193e+00 -1.420 0.15574
## sp_ptbBMns530 -1.554e+00 1.075e+00 -1.445 0.14839
## sp_ptbBMns531 -1.057e+00 7.218e-01 -1.464 0.14317
## sp_ptbBMns532 -2.658e-01 7.222e-01 -0.368 0.71280
## sp_ptbBMns533 -3.987e-01 6.851e-01 -0.582 0.56056
## sp_ptbBMns534 -5.032e-01 8.289e-01 -0.607 0.54378
## sp_ptbBMns535 7.632e-01 8.199e-01 0.931 0.35191
## sp_ptbBMns536 5.805e-01 8.380e-01 0.693 0.48848
## sp_ptbBMns537 3.872e-01 1.039e+00 0.373 0.70943
## sp_ptbBMns538 9.466e-01 1.034e+00 0.916 0.35974
## sp_ptbBMns539 -2.356e+00 1.030e+00 -2.287 0.02221 *
## sp_ptbBMns540 7.329e-01 9.855e-01 0.744 0.45702
## sp_ptbBMns541 -9.871e-01 7.775e-01 -1.270 0.20424
## sp_ptbBMns542 -1.089e+00 7.081e-01 -1.538 0.12407
## sp_ptbBMns543 -3.246e-01 6.494e-01 -0.500 0.61723
## sp_ptbBMns544 -1.575e+00 7.272e-01 -2.166 0.03030 *
## sp_ptbBMns545 -5.912e-01 8.548e-01 -0.692 0.48917
## sp_ptbBMns546 -4.491e-01 1.141e+00 -0.394 0.69394
## sp_ptbBMns547 -9.145e-01 1.015e+00 -0.901 0.36742
## sp_ptbBMns548 -6.121e-01 1.007e+00 -0.608 0.54312
## sp_ptbBMns549 -1.523e+00 1.064e+00 -1.432 0.15222
## sp_ptbBMns550 -1.049e+00 9.617e-01 -1.091 0.27541
## sp_ptbBMns551 -3.494e-01 7.224e-01 -0.484 0.62867
## sp_ptbBMns552 -9.333e-01 6.674e-01 -1.398 0.16196
## sp_ptbBMns553 -1.502e-01 7.207e-01 -0.208 0.83495
## sp_ptbBMns554 -1.154e+00 7.248e-01 -1.592 0.11129
## sp_ptbBMns555 -4.264e-01 7.418e-01 -0.575 0.56540
## sp_ptbBMns556 9.670e-01 7.702e-01 1.255 0.20931
## sp_ptbBMns557 -1.067e+00 7.777e-01 -1.372 0.16998
## sp_ptbBMns558 2.344e-01 7.701e-01 0.304 0.76086
## sp_ptbBMns559 -1.459e+00 8.202e-01 -1.779 0.07524 .
## sp_ptbBMns560 -6.223e-01 1.027e+00 -0.606 0.54458
## sp_ptbBMns561 -1.675e+00 1.142e+00 -1.466 0.14259
## sp_ptbBMns562 6.092e-01 1.121e+00 0.544 0.58674
## sp_ptbBMns563 -2.257e+00 1.120e+00 -2.015 0.04388 *
## sp_ptbBMns564 -1.951e+00 1.154e+00 -1.690 0.09094 .
## sp_ptbBMns565 -4.347e-01 8.553e-01 -0.508 0.61129
## sp_ptbBMns566 6.500e-02 9.427e-01 0.069 0.94503
## sp_ptbBMns567 -1.440e+00 9.951e-01 -1.447 0.14797
## sp_ptbBMns568 -6.939e-01 1.000e+00 -0.694 0.48773
## sp_ptbBMns569 -1.958e+00 1.102e+00 -1.777 0.07557 .
## sp_ptbBMns570 -2.854e+00 1.243e+00 -2.297 0.02164 *
## sp_ptbBMns571 -1.019e-01 1.166e+00 -0.087 0.93038
## sp_ptbBMns572 1.585e-03 1.214e+00 0.001 0.99896
## sp_ptbBMns573 -1.045e+00 1.263e+00 -0.827 0.40829
## sp_ptbBMns574 -1.779e+00 1.289e+00 -1.380 0.16764
## sp_ptbBMns575 -1.193e+00 2.189e+00 -0.545 0.58585
## sp_ptbBMns576 -2.678e+00 3.217e+00 -0.833 0.40507
## sp_ptbBMns577 -1.152e+00 2.866e+00 -0.402 0.68782
## sp_ptbBMns578 -3.828e+00 2.890e+00 -1.325 0.18526
## sp_ptbBMns579 -4.299e+00 3.351e+00 -1.283 0.19957
## sp_ptbBMns580 -5.493e+00 2.579e+00 -2.130 0.03317 *
## sp_ptbBMns581 -6.620e-01 1.228e+00 -0.539 0.58967
## sp_ptbBMns582 -2.712e+00 1.178e+00 -2.303 0.02126 *
## sp_ptbBMns583 6.326e-01 1.005e+00 0.629 0.52919
## sp_ptbBMns584 -1.071e+00 6.953e-01 -1.541 0.12341
## sp_ptbBMns585 -1.963e-01 7.457e-01 -0.263 0.79239
## sp_ptbBMns586 -9.698e-01 6.577e-01 -1.475 0.14034
## sp_ptbBMns587 -1.980e-01 5.363e-01 -0.369 0.71202
## sp_ptbBMns588 9.771e-02 6.688e-01 0.146 0.88385
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## lag(SA3m9a.resid, 1) -1.853e-01 2.519e-02 -7.357 1.88e-13 ***
## lag(SA3m9a.resid, 3) -2.155e-01 2.611e-02 -8.255 < 2e-16 ***
## lag(SA3m9a.resid, 4) -1.966e-01 2.761e-02 -7.121 1.07e-12 ***
## lag(SA3m9a.resid, 5) -2.041e-01 2.733e-02 -7.469 8.11e-14 ***
## lag(SA3m9a.resid, 6) -1.852e-01 2.805e-02 -6.603 4.04e-11 ***
## lag(SA3m9a.resid, 7) -2.031e-01 2.768e-02 -7.338 2.17e-13 ***
## lag(SA3m9a.resid, 8) -2.180e-01 2.832e-02 -7.696 1.40e-14 ***
## lag(SA3m9a.resid, 9) -1.598e-01 2.691e-02 -5.939 2.87e-09 ***
## lag(SA3m9a.resid, 10) -1.768e-01 2.664e-02 -6.634 3.26e-11 ***
## lag(SA3m9a.resid, 11) -1.214e-01 2.689e-02 -4.514 6.36e-06 ***
## lag(SA3m9a.resid, 12) -1.086e-01 2.562e-02 -4.240 2.23e-05 ***
## lag(SA3m9a.resid, 15) -6.324e-02 2.493e-02 -2.537 0.01117 *
## lag(SA3m9a.resid, 18) -3.680e-02 2.412e-02 -1.525 0.12719
## lag(SA3m9a.resid, 29) 1.205e-02 2.299e-02 0.524 0.60025
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(35327.47) family taken to be 1)
##
## Null deviance: 1050.09 on 857 degrees of freedom
## Residual deviance: 763.81 on 753 degrees of freedom
## (81 observations deleted due to missingness)
## AIC: 2950.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 35327
## Std. Err.: 170980
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2738.517SA3m9a.resid_ac<-residuals(SA3m9a.ac, type="deviance")
SA3m9a.pred_ac<-predict(SA3m9a.ac, type="response")
pacf(SA3m9a.resid_ac,na.action = na.omit) 
length(SA3m9a.pred_ac); length(SA3m9a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m9a.pred,lwd=1, col="blue")
plot(week$time,SA3m9a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m9a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA3m9a.pred_ac,lwd=1, col="blue")
plot(week$time,SA3m9a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA3m9a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA3m9a <- crosspred(cb9.minT, SA3m9a.ac, cen = 24.0, by=0.1,cumul=TRUE)
###final SA3 model #####
mod_fullSA3bm<-glm.nb(ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun + cb1.avgWindSp + sp_ptbBMns5, data = week)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullSA3bm)##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBMns5, data = week, init.theta = 20835.00492,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.4281 -0.8020 -0.1077 0.5694 2.8407
##
## Coefficients: (3 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.577e+00 1.728e+01 0.381 0.7035
## cb3.RFv1.l1 5.399e-02 2.395e-01 0.225 0.8216
## cb3.RFv1.l2 1.749e-01 1.684e-01 1.038 0.2992
## cb3.RFv2.l1 4.603e-01 3.774e-01 1.220 0.2226
## cb3.RFv2.l2 2.271e-01 2.625e-01 0.865 0.3870
## cb3.RFv3.l1 6.699e-01 6.083e-01 1.101 0.2708
## cb3.RFv3.l2 1.145e-01 4.142e-01 0.277 0.7821
## cb9.minTv1.l1 2.693e-01 2.390e-01 1.127 0.2599
## cb9.minTv1.l2 -2.721e-01 1.786e-01 -1.524 0.1276
## cb9.minTv2.l1 4.048e-01 8.230e-01 0.492 0.6228
## cb9.minTv2.l2 -9.606e-01 6.409e-01 -1.499 0.1339
## cb9.minTv3.l1 3.419e-01 4.576e-01 0.747 0.4549
## cb9.minTv3.l2 2.276e-01 3.447e-01 0.660 0.5090
## cb5.minRHv1.l1 -1.692e-01 2.443e-01 -0.693 0.4886
## cb5.minRHv1.l2 9.114e-02 1.439e-01 0.633 0.5266
## cb5.minRHv2.l1 -1.329e+00 8.844e-01 -1.502 0.1331
## cb5.minRHv2.l2 -4.640e-01 5.954e-01 -0.779 0.4358
## cb5.minRHv3.l1 -1.036e+00 5.772e-01 -1.795 0.0726 .
## cb5.minRHv3.l2 -6.438e-01 4.167e-01 -1.545 0.1223
## cb2.sunv1.l1 -9.056e-02 1.955e-01 -0.463 0.6431
## cb2.sunv1.l2 -1.635e-01 1.410e-01 -1.160 0.2462
## cb2.sunv2.l1 -3.506e-01 7.724e-01 -0.454 0.6499
## cb2.sunv2.l2 2.984e-01 5.553e-01 0.537 0.5911
## cb2.sunv3.l1 2.117e-01 2.866e-01 0.738 0.4602
## cb2.sunv3.l2 3.320e-01 2.008e-01 1.653 0.0984 .
## cb1.avgWindSpv1.l1 -1.839e-01 2.573e-01 -0.715 0.4747
## cb1.avgWindSpv1.l2 1.133e-01 2.002e-01 0.566 0.5716
## cb1.avgWindSpv2.l1 1.039e+00 6.144e-01 1.691 0.0909 .
## cb1.avgWindSpv2.l2 -1.332e-01 4.277e-01 -0.311 0.7555
## cb1.avgWindSpv3.l1 1.160e+00 7.068e-01 1.641 0.1008
## cb1.avgWindSpv3.l2 -2.063e-01 4.226e-01 -0.488 0.6255
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 2.222e+06 5.439e+06 0.408 0.6829
## sp_ptbBMns53 1.698e+00 1.180e+01 0.144 0.8856
## sp_ptbBMns54 -1.616e-01 2.070e+00 -0.078 0.9378
## sp_ptbBMns55 -2.374e+00 2.124e+00 -1.118 0.2635
## sp_ptbBMns56 -7.457e-01 1.848e+00 -0.403 0.6866
## sp_ptbBMns57 -3.899e+00 1.625e+00 -2.400 0.0164 *
## sp_ptbBMns58 -1.094e+00 1.407e+00 -0.778 0.4367
## sp_ptbBMns59 -1.691e+00 1.703e+00 -0.993 0.3206
## sp_ptbBMns510 -2.788e+00 1.458e+00 -1.912 0.0559 .
## sp_ptbBMns511 -1.428e+00 1.534e+00 -0.931 0.3520
## sp_ptbBMns512 -1.328e+00 1.309e+00 -1.014 0.3106
## sp_ptbBMns513 -2.223e+00 1.366e+00 -1.627 0.1037
## sp_ptbBMns514 -1.695e+00 1.421e+00 -1.193 0.2330
## sp_ptbBMns515 -5.434e-01 1.643e+00 -0.331 0.7409
## sp_ptbBMns516 -7.440e-01 1.430e+00 -0.520 0.6029
## sp_ptbBMns517 -4.258e-01 1.246e+00 -0.342 0.7325
## sp_ptbBMns518 4.086e-01 1.157e+00 0.353 0.7241
## sp_ptbBMns519 -1.083e+00 1.339e+00 -0.809 0.4187
## sp_ptbBMns520 1.224e-01 1.611e+00 0.076 0.9394
## sp_ptbBMns521 -4.424e-01 1.454e+00 -0.304 0.7609
## sp_ptbBMns522 4.016e-02 1.489e+00 0.027 0.9785
## sp_ptbBMns523 -1.049e+00 1.567e+00 -0.669 0.5034
## sp_ptbBMns524 1.050e+00 1.909e+00 0.550 0.5823
## sp_ptbBMns525 1.864e+00 2.274e+00 0.820 0.4124
## sp_ptbBMns526 1.543e+00 2.568e+00 0.601 0.5480
## sp_ptbBMns527 4.490e-01 2.331e+00 0.193 0.8473
## sp_ptbBMns528 4.786e-02 2.329e+00 0.021 0.9836
## sp_ptbBMns529 -7.829e-01 1.969e+00 -0.398 0.6909
## sp_ptbBMns530 -6.115e-01 1.893e+00 -0.323 0.7466
## sp_ptbBMns531 -9.991e-01 1.669e+00 -0.599 0.5493
## sp_ptbBMns532 -8.925e-01 1.618e+00 -0.552 0.5812
## sp_ptbBMns533 -1.914e+00 1.636e+00 -1.170 0.2422
## sp_ptbBMns534 -2.683e-01 1.895e+00 -0.142 0.8874
## sp_ptbBMns535 -5.335e-01 1.914e+00 -0.279 0.7805
## sp_ptbBMns536 -2.458e-01 1.790e+00 -0.137 0.8908
## sp_ptbBMns537 -4.782e-02 1.765e+00 -0.027 0.9784
## sp_ptbBMns538 5.394e-01 1.667e+00 0.324 0.7462
## sp_ptbBMns539 -1.601e+00 1.596e+00 -1.003 0.3156
## sp_ptbBMns540 4.653e-01 1.866e+00 0.249 0.8030
## sp_ptbBMns541 -9.869e-01 1.352e+00 -0.730 0.4653
## sp_ptbBMns542 -2.726e+00 1.451e+00 -1.879 0.0602 .
## sp_ptbBMns543 -1.713e+00 1.388e+00 -1.234 0.2173
## sp_ptbBMns544 -1.800e+00 1.459e+00 -1.233 0.2175
## sp_ptbBMns545 -2.539e+00 2.073e+00 -1.224 0.2208
## sp_ptbBMns546 -9.369e-01 2.076e+00 -0.451 0.6517
## sp_ptbBMns547 -1.987e+00 2.048e+00 -0.970 0.3319
## sp_ptbBMns548 -1.726e+00 2.050e+00 -0.842 0.3999
## sp_ptbBMns549 -3.106e+00 2.112e+00 -1.471 0.1414
## sp_ptbBMns550 -2.052e+00 2.016e+00 -1.018 0.3088
## sp_ptbBMns551 -5.278e-01 1.806e+00 -0.292 0.7701
## sp_ptbBMns552 -8.333e-01 1.713e+00 -0.487 0.6266
## sp_ptbBMns553 1.194e-01 1.642e+00 0.073 0.9420
## sp_ptbBMns554 -1.330e-01 1.633e+00 -0.081 0.9351
## sp_ptbBMns555 1.686e-01 1.872e+00 0.090 0.9282
## sp_ptbBMns556 7.431e-01 1.740e+00 0.427 0.6694
## sp_ptbBMns557 -1.102e+00 1.827e+00 -0.603 0.5465
## sp_ptbBMns558 -1.595e+00 1.685e+00 -0.946 0.3439
## sp_ptbBMns559 -1.061e+00 1.673e+00 -0.634 0.5260
## sp_ptbBMns560 -2.108e+00 2.095e+00 -1.006 0.3144
## sp_ptbBMns561 -1.816e+00 2.144e+00 -0.847 0.3969
## sp_ptbBMns562 -4.488e-01 1.925e+00 -0.233 0.8157
## sp_ptbBMns563 -2.826e+00 1.943e+00 -1.454 0.1458
## sp_ptbBMns564 -1.553e+00 2.061e+00 -0.754 0.4510
## sp_ptbBMns565 -9.271e-01 2.235e+00 -0.415 0.6783
## sp_ptbBMns566 1.421e+00 2.020e+00 0.704 0.4816
## sp_ptbBMns567 -6.859e-02 2.020e+00 -0.034 0.9729
## sp_ptbBMns568 1.130e+00 1.942e+00 0.582 0.5607
## sp_ptbBMns569 8.240e-01 2.235e+00 0.369 0.7124
## sp_ptbBMns570 -1.440e+00 2.432e+00 -0.592 0.5537
## sp_ptbBMns571 6.719e-01 2.336e+00 0.288 0.7736
## sp_ptbBMns572 1.638e+00 2.167e+00 0.756 0.4497
## sp_ptbBMns573 1.910e-01 2.029e+00 0.094 0.9250
## sp_ptbBMns574 -2.560e-02 2.017e+00 -0.013 0.9899
## sp_ptbBMns575 -3.505e+00 2.902e+00 -1.208 0.2272
## sp_ptbBMns576 -2.915e+00 3.660e+00 -0.797 0.4257
## sp_ptbBMns577 -3.023e+00 3.423e+00 -0.883 0.3772
## sp_ptbBMns578 -5.496e+00 3.377e+00 -1.628 0.1036
## sp_ptbBMns579 -6.160e+00 3.751e+00 -1.642 0.1005
## sp_ptbBMns580 -5.154e+00 3.071e+00 -1.678 0.0933 .
## sp_ptbBMns581 -1.784e+00 1.665e+00 -1.072 0.2839
## sp_ptbBMns582 -1.011e+00 1.623e+00 -0.623 0.5334
## sp_ptbBMns583 -4.924e-01 1.396e+00 -0.353 0.7242
## sp_ptbBMns584 2.462e-01 1.140e+00 0.216 0.8290
## sp_ptbBMns585 -1.180e+00 1.358e+00 -0.869 0.3850
## sp_ptbBMns586 -2.456e-01 1.046e+00 -0.235 0.8144
## sp_ptbBMns587 -5.768e-02 8.804e-01 -0.066 0.9478
## sp_ptbBMns588 4.719e-01 8.572e-01 0.551 0.5819
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(20835) family taken to be 1)
##
## Null deviance: 1101.26 on 886 degrees of freedom
## Residual deviance: 951.88 on 769 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3222.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 20835
## Std. Err.: 140243
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2984.748pred.fullbm<-predict(mod_fullSA3bm, type="response") #fitted
resid.fullbm<-residuals(mod_fullSA3bm, type="deviance") #residuals deviance
length(pred.fullbm)## [1] 939length(week$ptbBM)## [1] 939length(resid.fullbm)## [1] 939pacf(resid.fullbm,na.action=na.omit) #PACF for residuals, sig lags from 1-12,15 & 29
#ensure that the lags are dplyr lags
mod_fullSA3bm.ac<-update(mod_fullSA3bm,.~.+lag(resid.fullbm,1)+lag(resid.fullbm,2)+lag(resid.fullbm,3)+lag(resid.fullbm,4)+
lag(resid.fullbm,5)+lag(resid.fullbm,6)+lag(resid.fullbm,7)+lag(resid.fullbm,8)+lag(resid.fullbm,9)+
lag(resid.fullbm,10)+lag(resid.fullbm,11)+lag(resid.fullbm,12)+lag(resid.fullbm,15)+
lag(resid.fullbm,29))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullSA3bm.ac)##aic=3045##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBMns5 + lag(resid.fullbm, 1) + lag(resid.fullbm,
## 2) + lag(resid.fullbm, 3) + lag(resid.fullbm, 4) + lag(resid.fullbm,
## 5) + lag(resid.fullbm, 6) + lag(resid.fullbm, 7) + lag(resid.fullbm,
## 8) + lag(resid.fullbm, 9) + lag(resid.fullbm, 10) + lag(resid.fullbm,
## 11) + lag(resid.fullbm, 12) + lag(resid.fullbm, 15) + lag(resid.fullbm,
## 29), data = week, init.theta = 43871.91116, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.23044 -0.72513 -0.02565 0.47321 2.39163
##
## Coefficients: (6 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 17.46889 18.46301 0.946 0.344069
## cb3.RFv1.l1 0.08816 0.25511 0.346 0.729664
## cb3.RFv1.l2 0.16777 0.17701 0.948 0.343250
## cb3.RFv2.l1 0.30723 0.39968 0.769 0.442068
## cb3.RFv2.l2 0.26849 0.28330 0.948 0.343259
## cb3.RFv3.l1 0.60075 0.65490 0.917 0.358981
## cb3.RFv3.l2 0.00344 0.45452 0.008 0.993962
## cb9.minTv1.l1 0.13909 0.24590 0.566 0.571635
## cb9.minTv1.l2 -0.46295 0.18584 -2.491 0.012735 *
## cb9.minTv2.l1 0.41907 0.87280 0.480 0.631119
## cb9.minTv2.l2 -1.55386 0.67373 -2.306 0.021091 *
## cb9.minTv3.l1 0.32611 0.46592 0.700 0.483979
## cb9.minTv3.l2 -0.18927 0.35368 -0.535 0.592549
## cb5.minRHv1.l1 -0.34610 0.25957 -1.333 0.182400
## cb5.minRHv1.l2 -0.02550 0.14826 -0.172 0.863412
## cb5.minRHv2.l1 -2.25040 0.93869 -2.397 0.016512 *
## cb5.minRHv2.l2 0.33440 0.61532 0.543 0.586817
## cb5.minRHv3.l1 -0.46998 0.62402 -0.753 0.451354
## cb5.minRHv3.l2 0.15834 0.43723 0.362 0.717244
## cb2.sunv1.l1 0.16906 0.20665 0.818 0.413283
## cb2.sunv1.l2 0.11932 0.14210 0.840 0.401070
## cb2.sunv2.l1 0.35507 0.81284 0.437 0.662233
## cb2.sunv2.l2 1.18782 0.56461 2.104 0.035397 *
## cb2.sunv3.l1 0.20791 0.30066 0.692 0.489251
## cb2.sunv3.l2 0.49672 0.20805 2.387 0.016964 *
## cb1.avgWindSpv1.l1 0.18842 0.26913 0.700 0.483852
## cb1.avgWindSpv1.l2 0.29159 0.21249 1.372 0.169991
## cb1.avgWindSpv2.l1 0.27631 0.65716 0.420 0.674142
## cb1.avgWindSpv2.l2 -0.20709 0.45018 -0.460 0.645502
## cb1.avgWindSpv3.l1 -0.01671 0.76083 -0.022 0.982481
## cb1.avgWindSpv3.l2 -0.17066 0.45313 -0.377 0.706448
## sp_ptbBMns51 NA NA NA NA
## sp_ptbBMns52 NA NA NA NA
## sp_ptbBMns53 NA NA NA NA
## sp_ptbBMns54 NA NA NA NA
## sp_ptbBMns55 1161.19604 1270.44727 0.914 0.360714
## sp_ptbBMns56 -5.34952 10.08358 -0.531 0.595753
## sp_ptbBMns57 -5.35300 2.24823 -2.381 0.017267 *
## sp_ptbBMns58 -0.53299 1.55872 -0.342 0.732396
## sp_ptbBMns59 -1.79468 1.75820 -1.021 0.307375
## sp_ptbBMns510 -2.69956 1.49152 -1.810 0.070306 .
## sp_ptbBMns511 -0.17680 1.56179 -0.113 0.909872
## sp_ptbBMns512 -2.48641 1.33178 -1.867 0.061904 .
## sp_ptbBMns513 0.03532 1.38679 0.025 0.979681
## sp_ptbBMns514 -2.22339 1.43445 -1.550 0.121142
## sp_ptbBMns515 0.16925 1.67419 0.101 0.919477
## sp_ptbBMns516 -1.96975 1.46322 -1.346 0.178246
## sp_ptbBMns517 -2.25541 1.27561 -1.768 0.077042 .
## sp_ptbBMns518 0.16300 1.18119 0.138 0.890243
## sp_ptbBMns519 -3.48572 1.39993 -2.490 0.012777 *
## sp_ptbBMns520 0.36707 1.63834 0.224 0.822720
## sp_ptbBMns521 -2.46318 1.47727 -1.667 0.095436 .
## sp_ptbBMns522 -0.65153 1.49510 -0.436 0.662996
## sp_ptbBMns523 -4.24688 1.59743 -2.659 0.007847 **
## sp_ptbBMns524 1.90255 1.92557 0.988 0.323132
## sp_ptbBMns525 0.33431 2.31179 0.145 0.885016
## sp_ptbBMns526 1.88960 2.64558 0.714 0.475072
## sp_ptbBMns527 -0.66052 2.37896 -0.278 0.781280
## sp_ptbBMns528 -0.44098 2.37879 -0.185 0.852930
## sp_ptbBMns529 -1.94005 2.03328 -0.954 0.340011
## sp_ptbBMns530 -1.99347 1.91658 -1.040 0.298285
## sp_ptbBMns531 -1.92628 1.69920 -1.134 0.256946
## sp_ptbBMns532 -1.42928 1.69272 -0.844 0.398461
## sp_ptbBMns533 -2.61967 1.69635 -1.544 0.122516
## sp_ptbBMns534 -2.62907 1.96961 -1.335 0.181935
## sp_ptbBMns535 -1.28154 1.95084 -0.657 0.511233
## sp_ptbBMns536 -1.37078 1.83355 -0.748 0.454696
## sp_ptbBMns537 -0.40718 1.78354 -0.228 0.819416
## sp_ptbBMns538 0.76582 1.70902 0.448 0.654079
## sp_ptbBMns539 -3.64513 1.63193 -2.234 0.025508 *
## sp_ptbBMns540 -0.41709 1.89230 -0.220 0.825550
## sp_ptbBMns541 -3.41569 1.36133 -2.509 0.012105 *
## sp_ptbBMns542 -5.16174 1.50848 -3.422 0.000622 ***
## sp_ptbBMns543 -3.45816 1.42035 -2.435 0.014903 *
## sp_ptbBMns544 -3.14664 1.50840 -2.086 0.036972 *
## sp_ptbBMns545 -3.69708 2.07753 -1.780 0.075149 .
## sp_ptbBMns546 -2.40840 2.09586 -1.149 0.250504
## sp_ptbBMns547 -3.02628 2.09094 -1.447 0.147805
## sp_ptbBMns548 -3.51121 2.12532 -1.652 0.098517 .
## sp_ptbBMns549 -3.46961 2.19516 -1.581 0.113976
## sp_ptbBMns550 -3.95692 2.08698 -1.896 0.057960 .
## sp_ptbBMns551 -1.67495 1.86957 -0.896 0.370304
## sp_ptbBMns552 -4.11922 1.76565 -2.333 0.019649 *
## sp_ptbBMns553 -0.81408 1.70106 -0.479 0.632242
## sp_ptbBMns554 -1.80953 1.67484 -1.080 0.279956
## sp_ptbBMns555 -1.14089 1.90409 -0.599 0.549054
## sp_ptbBMns556 1.21490 1.76902 0.687 0.492231
## sp_ptbBMns557 -3.80316 1.87675 -2.026 0.042717 *
## sp_ptbBMns558 -1.48165 1.72544 -0.859 0.390501
## sp_ptbBMns559 -4.32179 1.74364 -2.479 0.013190 *
## sp_ptbBMns560 -1.93481 2.12947 -0.909 0.363567
## sp_ptbBMns561 -5.42952 2.19344 -2.475 0.013311 *
## sp_ptbBMns562 -0.54875 1.95352 -0.281 0.778785
## sp_ptbBMns563 -4.36194 1.99244 -2.189 0.028579 *
## sp_ptbBMns564 -3.68451 2.11409 -1.743 0.081362 .
## sp_ptbBMns565 -2.16875 2.32132 -0.934 0.350162
## sp_ptbBMns566 -1.20529 2.06897 -0.583 0.560193
## sp_ptbBMns567 -3.62285 2.10205 -1.723 0.084801 .
## sp_ptbBMns568 -2.54397 2.03148 -1.252 0.210470
## sp_ptbBMns569 -3.09453 2.32968 -1.328 0.184077
## sp_ptbBMns570 -4.24219 2.52793 -1.678 0.093322 .
## sp_ptbBMns571 -1.14136 2.38746 -0.478 0.632604
## sp_ptbBMns572 -2.37032 2.23465 -1.061 0.288819
## sp_ptbBMns573 -3.13715 2.10320 -1.492 0.135801
## sp_ptbBMns574 -4.14025 2.09641 -1.975 0.048277 *
## sp_ptbBMns575 -1.60581 2.95609 -0.543 0.586979
## sp_ptbBMns576 -5.58735 3.65070 -1.530 0.125897
## sp_ptbBMns577 -1.44518 3.47928 -0.415 0.677873
## sp_ptbBMns578 -5.14315 3.41494 -1.506 0.132048
## sp_ptbBMns579 -3.51096 3.86425 -0.909 0.363574
## sp_ptbBMns580 -4.33802 3.13780 -1.383 0.166817
## sp_ptbBMns581 0.07658 1.67062 0.046 0.963437
## sp_ptbBMns582 -2.57084 1.66221 -1.547 0.121950
## sp_ptbBMns583 1.56288 1.43354 1.090 0.275616
## sp_ptbBMns584 -1.33319 1.12709 -1.183 0.236866
## sp_ptbBMns585 -1.23826 1.39310 -0.889 0.374085
## sp_ptbBMns586 -1.29666 1.05614 -1.228 0.219550
## sp_ptbBMns587 -1.30887 0.88884 -1.473 0.140868
## sp_ptbBMns588 -0.34069 0.82821 -0.411 0.680811
## sp_ptbBMns589 NA NA NA NA
## sp_ptbBMns590 NA NA NA NA
## lag(resid.fullbm, 1) -0.32202 0.02854 -11.284 < 2e-16 ***
## lag(resid.fullbm, 2) -0.32769 0.03131 -10.467 < 2e-16 ***
## lag(resid.fullbm, 3) -0.38468 0.03178 -12.104 < 2e-16 ***
## lag(resid.fullbm, 4) -0.37047 0.03312 -11.186 < 2e-16 ***
## lag(resid.fullbm, 5) -0.39075 0.03324 -11.754 < 2e-16 ***
## lag(resid.fullbm, 6) -0.37306 0.03452 -10.808 < 2e-16 ***
## lag(resid.fullbm, 7) -0.37230 0.03400 -10.949 < 2e-16 ***
## lag(resid.fullbm, 8) -0.35430 0.03335 -10.625 < 2e-16 ***
## lag(resid.fullbm, 9) -0.29752 0.03216 -9.250 < 2e-16 ***
## lag(resid.fullbm, 10) -0.29216 0.03102 -9.418 < 2e-16 ***
## lag(resid.fullbm, 11) -0.21519 0.03057 -7.039 1.94e-12 ***
## lag(resid.fullbm, 12) -0.18630 0.02836 -6.569 5.08e-11 ***
## lag(resid.fullbm, 15) -0.04740 0.02509 -1.889 0.058860 .
## lag(resid.fullbm, 29) 0.02203 0.02361 0.933 0.350725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(43871.91) family taken to be 1)
##
## Null deviance: 1050.10 on 857 degrees of freedom
## Residual deviance: 640.76 on 729 degrees of freedom
## (81 observations deleted due to missingness)
## AIC: 2875.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 43872
## Std. Err.: 187431
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2615.462resid.fullbm.ac<-residuals(mod_fullSA3bm.ac, type="deviance")
pred.fullbm.ac<-predict(mod_fullSA3bm.ac, type="response")
pacf(resid.fullbm.ac,na.action = na.omit) 
length(pred.fullbm.ac)## [1] 939length(resid.fullbm.ac)## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,pred.fullbm,lwd=1, col="dark blue")
plot(week$time,resid.fullbm)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,pred.fullbm.ac,lwd=1, col="dark blue")
plot(week$time,resid.fullbm.ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm.ac, week$ptbBM)
abline(coef = c(0,1), col="red")
##checking general model fit plot
plot(mod_fullSA3bm)## Warning: not plotting observations with leverage one:
## 53
plot(mod_fullSA3bm.ac)
## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced## Warning in sqrt(crit * p * (1 - hh)/hh): NaNs produced
#1. plotting the dose reponse and slices now for min temperature
SA3predbm.temp <- crosspred(cb9.minT, mod_fullSA3bm.ac,cen = 24.0, by=0.1,cumul=TRUE)
#cumulative effect
plot(SA3predbm.temp, "overall", xlab="Min temperature (?C)",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of min temperature on ptb")
#90th %
plot(SA3predbm.temp,"slices", var=c(24.6),type="p",ci="bars",col=1,pch=19,ylim=c(0.85,1.04),
xlab="Lag (weeks)",ylab="RR")
##getting est from SA3predbm.temp
#for 5th % - mat
SA3predbm.temp$matRRfit["22.9","lag0"]; SA3predbm.temp$matRRlow["22.9","lag0"]; SA3predbm.temp$matRRhigh["22.9","lag0"]## [1] 0.8281664## [1] 0.7049418## [1] 0.9729307SA3predbm.temp$matRRfit["22.9","lag13"]; SA3predbm.temp$matRRlow["22.9","lag13"]; SA3predbm.temp$matRRhigh["22.9","lag13"]## [1] 0.8991408## [1] 0.7912698## [1] 1.021717SA3predbm.temp$matRRfit["22.9","lag26"]; SA3predbm.temp$matRRlow["22.9","lag26"]; SA3predbm.temp$matRRhigh["22.9","lag26"]## [1] 0.9761978## [1] 0.8685493## [1] 1.097188SA3predbm.temp$matRRfit["22.9","lag39"]; SA3predbm.temp$matRRlow["22.9","lag39"]; SA3predbm.temp$matRRhigh["22.9","lag39"]## [1] 1.059859## [1] 0.927104## [1] 1.211623SA3predbm.temp$matRRfit["22.9","lag52"]; SA3predbm.temp$matRRlow["22.9","lag52"]; SA3predbm.temp$matRRhigh["22.9","lag52"]## [1] 1.150689## [1] 0.9702116## [1] 1.364739#for 95th % - mat
SA3predbm.temp$matRRfit["25.1","lag0"]; SA3predbm.temp$matRRlow["25.1","lag0"]; SA3predbm.temp$matRRhigh["25.1","lag0"]## [1] 0.9879565## [1] 0.8423467## [1] 1.158737SA3predbm.temp$matRRfit["25.1","lag13"]; SA3predbm.temp$matRRlow["25.1","lag13"]; SA3predbm.temp$matRRhigh["25.1","lag13"]## [1] 1.016819## [1] 0.892515## [1] 1.158436SA3predbm.temp$matRRfit["25.1","lag26"]; SA3predbm.temp$matRRlow["25.1","lag26"]; SA3predbm.temp$matRRhigh["25.1","lag26"]## [1] 1.046525## [1] 0.9292033## [1] 1.17866SA3predbm.temp$matRRfit["25.1","lag27"]; SA3predbm.temp$matRRlow["25.1","lag27"]; SA3predbm.temp$matRRhigh["25.1","lag27"]## [1] 1.048846## [1] 0.9312251## [1] 1.181323SA3predbm.temp$matRRfit["25.1","lag28"]; SA3predbm.temp$matRRlow["25.1","lag28"]; SA3predbm.temp$matRRhigh["25.1","lag28"]## [1] 1.051172## [1] 0.9331224## [1] 1.184155SA3predbm.temp$matRRfit["25.1","lag29"]; SA3predbm.temp$matRRlow["25.1","lag29"]; SA3predbm.temp$matRRhigh["25.1","lag29"]## [1] 1.053503## [1] 0.9348951## [1] 1.187158SA3predbm.temp$matRRfit["25.1","lag30"]; SA3predbm.temp$matRRlow["25.1","lag30"]; SA3predbm.temp$matRRhigh["25.1","lag30"]## [1] 1.055839## [1] 0.9365434## [1] 1.19033SA3predbm.temp$matRRfit["25.1","lag31"]; SA3predbm.temp$matRRlow["25.1","lag31"]; SA3predbm.temp$matRRhigh["25.1","lag31"]## [1] 1.05818## [1] 0.9380682## [1] 1.193672SA3predbm.temp$matRRfit["25.1","lag32"]; SA3predbm.temp$matRRlow["25.1","lag32"]; SA3predbm.temp$matRRhigh["25.1","lag32"]## [1] 1.060527## [1] 0.9394705## [1] 1.197182SA3predbm.temp$matRRfit["25.1","lag33"]; SA3predbm.temp$matRRlow["25.1","lag33"]; SA3predbm.temp$matRRhigh["25.1","lag33"]## [1] 1.062878## [1] 0.9407522## [1] 1.200859SA3predbm.temp$matRRfit["25.1","lag34"]; SA3predbm.temp$matRRlow["25.1","lag34"]; SA3predbm.temp$matRRhigh["25.1","lag34"]## [1] 1.065235## [1] 0.9419152## [1] 1.204701SA3predbm.temp$matRRfit["25.1","lag35"]; SA3predbm.temp$matRRlow["25.1","lag35"]; SA3predbm.temp$matRRhigh["25.1","lag35"]## [1] 1.067598## [1] 0.942962## [1] 1.208707SA3predbm.temp$matRRfit["25.1","lag36"]; SA3predbm.temp$matRRlow["25.1","lag36"]; SA3predbm.temp$matRRhigh["25.1","lag36"]## [1] 1.069965## [1] 0.9438953## [1] 1.212873SA3predbm.temp$matRRfit["25.1","lag37"]; SA3predbm.temp$matRRlow["25.1","lag37"]; SA3predbm.temp$matRRhigh["25.1","lag37"]## [1] 1.072338## [1] 0.9447181## [1] 1.217197SA3predbm.temp$matRRfit["25.1","lag38"]; SA3predbm.temp$matRRlow["25.1","lag38"]; SA3predbm.temp$matRRhigh["25.1","lag38"]## [1] 1.074716## [1] 0.9454335## [1] 1.221676SA3predbm.temp$matRRfit["25.1","lag39"]; SA3predbm.temp$matRRlow["25.1","lag39"]; SA3predbm.temp$matRRhigh["25.1","lag39"]## [1] 1.077099## [1] 0.9460451## [1] 1.226307SA3predbm.temp$matRRfit["25.1","lag52"]; SA3predbm.temp$matRRlow["25.1","lag52"]; SA3predbm.temp$matRRhigh["25.1","lag52"]## [1] 1.108566## [1] 0.946194## [1] 1.298801#2. plotting the dose reponse and slices now for RF
SA3predbm.rf <- crosspred(cb3.RF, mod_fullSA3bm.ac,cen = 44.9, by=0.1,cumul=TRUE)
#cumulative effect of RF
plot(SA3predbm.rf, "overall", xlab="Total precipitation",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of rainfall on ptb")
#3. plotting the dose reponse and slices now for wind
SA3predbm.wind <- crosspred(cb1.avgWindSp, mod_fullSA3bm.ac,cen = 4.5, by=0.1,cumul=TRUE)
#cumulative effect of wind
plot(SA3predbm.wind, "overall", xlab="Av wind speed",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of wind speed on ptb")
#4. plotting the dose reponse and slices now for sun
SA3predbm.sun <- crosspred(cb2.sun, mod_fullSA3bm.ac,cen = 50.7, by=0.1,cumul=TRUE)
#cumulative effect of sun
plot(SA3predbm.sun, "overall", xlab="Total sunshine hours",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of sunshine on ptb")
#5. plotting the dose reponse and slices now for minRH
SA3predbm.minRH <- crosspred(cb5.minRH, mod_fullSA3bm.ac,cen = 63, by=0.1, cumul=TRUE)
#cumulative effect
plot(SA3predbm.minRH, "overall", xlab="Min RH",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of minRH on ptb")
###to make lag plots for full model, ptbBM version, SA3 -----
##order: minT, rf, sun, minRH & avgWindSp
#tiff("fig_lagFullptbBM_SA3_Apr7.tiff", units="in", width=10, height=6, res=400)
par(mfrow=c(5,5),mar=c(2.5,4,2.5,2))
for (i in c(0,13,26,39,52)){
x = crosspred(cb1.avgWindSp, mod_fullSA3bm.ac, cen=4.5)
title = paste(c(i,"week lag"),collapse=" ")
plot(x,lag=i,main=title,ylab="RR (Av wind speed)", xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.3),xaxt = "n")
axis(1, at= c(3.0,3.6,4.5,6.1,10.2), labels = c("3.0","5th","50th","95th","10.2"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb9.minT, mod_fullSA3bm.ac, cen=24.0)
plot(x,lag=i,ylab="RR (Min Temp)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(21.8,22.9,24.0,25.1,26.3), labels = c("21.8","5th","50th","95th","26.3"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb3.RF, mod_fullSA3bm.ac, cen=44.9)
plot(x,lag=i,ylab="RR (Rainfall)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(0.0,0.8,44.9,160.7,414.5), labels = c("0.0","5th","50th","95th","414.5"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb2.sun, mod_fullSA3bm.ac, cen=50.7)
plot(x,lag=i,ylab="RR (Sunshine hours)", xlab="",col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(11.2,30.8,50.7,66.1,76.0), labels = c("11.2","5th","50th","95th","76.0"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb5.minRH, mod_fullSA3bm.ac, cen=63.0)
plot(x,lag=i,ylab="RR (Min RH)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.9,1.5),xaxt = "n")
axis(1, at= c(45.0,53.7,63.0,70.3,80.6), labels = c("45.0","5th","50th","95th","80.6"),
cex.axis=0.8)
}
dev.off()## null device
## 1Sensitivity analysis 2 - using ptbBM & ns9 for long term trend
sp_ptbBMns9 <-ns(week$time,df=18*9)
options(na.action="na.exclude")
SA4m1a <- glm.nb(ptbBM ~ cb1.avgWindSp + sp_ptbBMns9,data=week); summary(SA4m1a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb1.avgWindSp + sp_ptbBMns9, data = week,
## init.theta = 28292.39249, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.94862 -0.76455 -0.08865 0.53435 2.33121
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.043e+00 7.256e+00 -0.833 0.40497
## cb1.avgWindSpv1.l1 3.718e-01 3.411e-01 1.090 0.27559
## cb1.avgWindSpv1.l2 5.683e-01 2.506e-01 2.268 0.02331 *
## cb1.avgWindSpv2.l1 5.765e-01 7.978e-01 0.723 0.46991
## cb1.avgWindSpv2.l2 8.887e-01 5.536e-01 1.605 0.10845
## cb1.avgWindSpv3.l1 1.839e-01 9.443e-01 0.195 0.84556
## cb1.avgWindSpv3.l2 4.212e-01 5.896e-01 0.714 0.47503
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.475e+06 1.302e+06 -1.900 0.05737 .
## sp_ptbBMns97 3.961e+01 1.596e+01 2.482 0.01307 *
## sp_ptbBMns98 -7.277e+00 2.832e+00 -2.570 0.01017 *
## sp_ptbBMns99 2.236e+00 2.119e+00 1.055 0.29149
## sp_ptbBMns910 -1.964e+00 2.063e+00 -0.952 0.34093
## sp_ptbBMns911 -6.070e-01 2.075e+00 -0.293 0.76990
## sp_ptbBMns912 -2.467e+00 1.852e+00 -1.332 0.18292
## sp_ptbBMns913 -3.411e+00 1.863e+00 -1.831 0.06708 .
## sp_ptbBMns914 -2.710e+00 1.759e+00 -1.541 0.12341
## sp_ptbBMns915 -1.342e+00 1.688e+00 -0.795 0.42655
## sp_ptbBMns916 -3.047e+00 1.592e+00 -1.914 0.05560 .
## sp_ptbBMns917 -1.356e+00 1.857e+00 -0.730 0.46539
## sp_ptbBMns918 -2.424e+00 1.457e+00 -1.663 0.09629 .
## sp_ptbBMns919 7.371e-01 1.228e+00 0.600 0.54831
## sp_ptbBMns920 -8.017e-01 1.070e+00 -0.749 0.45357
## sp_ptbBMns921 -5.856e-01 1.036e+00 -0.565 0.57189
## sp_ptbBMns922 -1.299e+00 1.026e+00 -1.267 0.20531
## sp_ptbBMns923 -4.077e-01 1.011e+00 -0.403 0.68689
## sp_ptbBMns924 -5.205e-01 1.114e+00 -0.467 0.64038
## sp_ptbBMns925 -6.758e-01 9.973e-01 -0.678 0.49802
## sp_ptbBMns926 -1.540e+00 1.174e+00 -1.312 0.18963
## sp_ptbBMns927 1.171e+00 1.269e+00 0.923 0.35609
## sp_ptbBMns928 1.334e+00 1.248e+00 1.069 0.28516
## sp_ptbBMns929 -3.132e-01 1.101e+00 -0.284 0.77614
## sp_ptbBMns930 -1.638e+00 1.070e+00 -1.530 0.12591
## sp_ptbBMns931 -1.044e+00 1.135e+00 -0.920 0.35747
## sp_ptbBMns932 -1.604e+00 1.020e+00 -1.572 0.11584
## sp_ptbBMns933 -2.564e-03 9.765e-01 -0.003 0.99790
## sp_ptbBMns934 -1.561e+00 1.062e+00 -1.470 0.14153
## sp_ptbBMns935 -1.943e+00 1.318e+00 -1.474 0.14041
## sp_ptbBMns936 -5.825e-01 1.201e+00 -0.485 0.62759
## sp_ptbBMns937 1.611e+00 1.451e+00 1.110 0.26679
## sp_ptbBMns938 3.718e-01 1.188e+00 0.313 0.75437
## sp_ptbBMns939 -8.117e-01 1.043e+00 -0.778 0.43657
## sp_ptbBMns940 -4.980e-01 1.004e+00 -0.496 0.62001
## sp_ptbBMns941 -4.081e-01 1.052e+00 -0.388 0.69801
## sp_ptbBMns942 -2.674e+00 1.176e+00 -2.273 0.02300 *
## sp_ptbBMns943 -9.525e-01 1.118e+00 -0.852 0.39441
## sp_ptbBMns944 -1.603e+00 9.400e-01 -1.706 0.08804 .
## sp_ptbBMns945 -1.036e+00 9.930e-01 -1.043 0.29683
## sp_ptbBMns946 1.284e+00 9.200e-01 1.396 0.16280
## sp_ptbBMns947 1.383e+00 9.119e-01 1.516 0.12945
## sp_ptbBMns948 -4.616e-01 1.110e+00 -0.416 0.67748
## sp_ptbBMns949 6.073e-01 1.065e+00 0.570 0.56862
## sp_ptbBMns950 -1.997e-01 1.322e+00 -0.151 0.87998
## sp_ptbBMns951 -3.504e-01 1.275e+00 -0.275 0.78343
## sp_ptbBMns952 8.955e-02 1.267e+00 0.071 0.94365
## sp_ptbBMns953 -6.863e-01 1.520e+00 -0.452 0.65160
## sp_ptbBMns954 -3.356e+00 1.722e+00 -1.949 0.05129 .
## sp_ptbBMns955 6.779e-01 1.658e+00 0.409 0.68256
## sp_ptbBMns956 -3.068e+00 1.844e+00 -1.664 0.09613 .
## sp_ptbBMns957 -9.855e-02 1.708e+00 -0.058 0.95399
## sp_ptbBMns958 -1.898e+00 1.762e+00 -1.077 0.28129
## sp_ptbBMns959 -9.455e-01 1.723e+00 -0.549 0.58318
## sp_ptbBMns960 -2.340e+00 1.870e+00 -1.251 0.21086
## sp_ptbBMns961 -1.396e+00 1.931e+00 -0.723 0.46989
## sp_ptbBMns962 -2.713e+00 2.101e+00 -1.292 0.19645
## sp_ptbBMns963 -1.242e+00 1.964e+00 -0.633 0.52692
## sp_ptbBMns964 -2.293e+00 1.941e+00 -1.181 0.23745
## sp_ptbBMns965 -2.033e+00 1.726e+00 -1.177 0.23903
## sp_ptbBMns966 -1.315e+00 1.544e+00 -0.852 0.39443
## sp_ptbBMns967 -2.792e+00 1.446e+00 -1.931 0.05350 .
## sp_ptbBMns968 -2.274e+00 1.311e+00 -1.734 0.08294 .
## sp_ptbBMns969 -1.794e+00 1.369e+00 -1.310 0.19029
## sp_ptbBMns970 -1.696e+00 1.293e+00 -1.312 0.18961
## sp_ptbBMns971 -1.443e+00 1.218e+00 -1.184 0.23624
## sp_ptbBMns972 -1.956e-01 1.298e+00 -0.151 0.88021
## sp_ptbBMns973 -1.088e-01 1.143e+00 -0.095 0.92419
## sp_ptbBMns974 -3.766e-02 1.174e+00 -0.032 0.97441
## sp_ptbBMns975 -6.271e-01 1.067e+00 -0.588 0.55677
## sp_ptbBMns976 -1.730e+00 1.131e+00 -1.530 0.12608
## sp_ptbBMns977 -1.905e+00 1.254e+00 -1.519 0.12864
## sp_ptbBMns978 -5.744e-01 1.202e+00 -0.478 0.63287
## sp_ptbBMns979 -9.128e-01 1.134e+00 -0.805 0.42069
## sp_ptbBMns980 5.479e-02 1.391e+00 0.039 0.96857
## sp_ptbBMns981 -1.806e+00 1.543e+00 -1.170 0.24186
## sp_ptbBMns982 1.633e+00 1.733e+00 0.943 0.34583
## sp_ptbBMns983 -1.049e+00 1.806e+00 -0.581 0.56149
## sp_ptbBMns984 -9.944e-02 1.876e+00 -0.053 0.95772
## sp_ptbBMns985 -1.972e+00 2.063e+00 -0.956 0.33923
## sp_ptbBMns986 -2.091e+00 2.191e+00 -0.955 0.33980
## sp_ptbBMns987 -1.228e+00 2.162e+00 -0.568 0.56992
## sp_ptbBMns988 -3.175e+00 2.376e+00 -1.336 0.18146
## sp_ptbBMns989 -1.992e+00 2.236e+00 -0.891 0.37292
## sp_ptbBMns990 -3.257e+00 2.151e+00 -1.514 0.12995
## sp_ptbBMns991 -2.723e+00 2.145e+00 -1.270 0.20426
## sp_ptbBMns992 -2.875e+00 2.111e+00 -1.362 0.17329
## sp_ptbBMns993 -2.144e+00 2.055e+00 -1.044 0.29663
## sp_ptbBMns994 -4.919e+00 2.023e+00 -2.431 0.01505 *
## sp_ptbBMns995 3.158e-02 1.887e+00 0.017 0.98665
## sp_ptbBMns996 -4.116e+00 1.880e+00 -2.189 0.02859 *
## sp_ptbBMns997 -3.642e-01 1.833e+00 -0.199 0.84246
## sp_ptbBMns998 -2.726e+00 1.893e+00 -1.440 0.14975
## sp_ptbBMns999 -1.567e+00 1.877e+00 -0.835 0.40368
## sp_ptbBMns9100 -1.623e+00 1.965e+00 -0.826 0.40878
## sp_ptbBMns9101 -9.448e-01 1.815e+00 -0.521 0.60263
## sp_ptbBMns9102 -1.743e+00 1.876e+00 -0.929 0.35291
## sp_ptbBMns9103 -1.843e+00 1.862e+00 -0.989 0.32246
## sp_ptbBMns9104 -2.526e+00 1.978e+00 -1.277 0.20173
## sp_ptbBMns9105 -1.273e+00 1.886e+00 -0.675 0.49971
## sp_ptbBMns9106 -2.855e+00 1.994e+00 -1.432 0.15215
## sp_ptbBMns9107 -2.025e+00 1.895e+00 -1.069 0.28529
## sp_ptbBMns9108 -1.552e+00 2.105e+00 -0.737 0.46085
## sp_ptbBMns9109 -2.287e+00 2.175e+00 -1.051 0.29306
## sp_ptbBMns9110 -2.296e+00 2.142e+00 -1.072 0.28363
## sp_ptbBMns9111 -1.593e+00 1.958e+00 -0.813 0.41595
## sp_ptbBMns9112 -2.178e+00 2.008e+00 -1.085 0.27802
## sp_ptbBMns9113 -2.712e+00 1.894e+00 -1.431 0.15231
## sp_ptbBMns9114 -2.224e+00 2.033e+00 -1.094 0.27412
## sp_ptbBMns9115 -3.776e+00 2.011e+00 -1.878 0.06041 .
## sp_ptbBMns9116 -1.976e+00 2.030e+00 -0.973 0.33032
## sp_ptbBMns9117 -2.747e+00 1.926e+00 -1.426 0.15374
## sp_ptbBMns9118 -1.198e+00 2.079e+00 -0.576 0.56456
## sp_ptbBMns9119 -1.236e+00 2.006e+00 -0.616 0.53787
## sp_ptbBMns9120 -2.239e+00 2.026e+00 -1.105 0.26899
## sp_ptbBMns9121 -2.724e+00 1.919e+00 -1.420 0.15570
## sp_ptbBMns9122 -2.957e+00 1.963e+00 -1.506 0.13197
## sp_ptbBMns9123 -1.785e+00 1.874e+00 -0.952 0.34095
## sp_ptbBMns9124 -2.228e+00 1.867e+00 -1.193 0.23268
## sp_ptbBMns9125 -1.468e+00 1.900e+00 -0.772 0.43994
## sp_ptbBMns9126 -4.122e+00 2.065e+00 -1.996 0.04593 *
## sp_ptbBMns9127 1.676e-01 2.082e+00 0.081 0.93584
## sp_ptbBMns9128 -2.187e+00 2.050e+00 -1.067 0.28597
## sp_ptbBMns9129 -7.285e-01 2.138e+00 -0.341 0.73331
## sp_ptbBMns9130 -2.421e+00 1.957e+00 -1.237 0.21614
## sp_ptbBMns9131 -1.998e+00 1.789e+00 -1.117 0.26415
## sp_ptbBMns9132 -2.908e+00 1.532e+00 -1.898 0.05776 .
## sp_ptbBMns9133 -2.790e+00 1.409e+00 -1.981 0.04763 *
## sp_ptbBMns9134 -2.020e+00 1.273e+00 -1.587 0.11253
## sp_ptbBMns9135 -2.257e+00 1.216e+00 -1.857 0.06333 .
## sp_ptbBMns9136 1.495e+00 1.902e+00 0.786 0.43176
## sp_ptbBMns9137 -1.620e+00 1.561e+00 -1.038 0.29918
## sp_ptbBMns9138 1.941e+00 1.615e+00 1.202 0.22944
## sp_ptbBMns9139 -4.833e-01 1.353e+00 -0.357 0.72092
## sp_ptbBMns9140 6.528e-01 1.446e+00 0.451 0.65171
## sp_ptbBMns9141 -1.267e+00 1.434e+00 -0.883 0.37698
## sp_ptbBMns9142 -4.958e-01 1.582e+00 -0.313 0.75391
## sp_ptbBMns9143 -2.288e+00 1.543e+00 -1.483 0.13815
## sp_ptbBMns9144 -1.725e+00 1.577e+00 -1.094 0.27400
## sp_ptbBMns9145 7.012e-01 8.991e-01 0.780 0.43546
## sp_ptbBMns9146 9.078e-01 7.918e-01 1.146 0.25161
## sp_ptbBMns9147 6.481e-01 7.805e-01 0.830 0.40635
## sp_ptbBMns9148 -1.566e+00 8.998e-01 -1.741 0.08173 .
## sp_ptbBMns9149 6.736e-01 7.428e-01 0.907 0.36452
## sp_ptbBMns9150 8.586e-01 7.377e-01 1.164 0.24448
## sp_ptbBMns9151 -5.740e-01 8.871e-01 -0.647 0.51760
## sp_ptbBMns9152 2.293e+00 7.867e-01 2.915 0.00355 **
## sp_ptbBMns9153 -6.347e-01 9.173e-01 -0.692 0.48897
## sp_ptbBMns9154 1.077e+00 1.046e+00 1.029 0.30326
## sp_ptbBMns9155 1.888e+00 8.900e-01 2.122 0.03387 *
## sp_ptbBMns9156 -4.664e-01 8.432e-01 -0.553 0.58011
## sp_ptbBMns9157 1.391e+00 6.874e-01 2.023 0.04308 *
## sp_ptbBMns9158 -3.005e-01 7.726e-01 -0.389 0.69729
## sp_ptbBMns9159 1.121e+00 5.972e-01 1.878 0.06044 .
## sp_ptbBMns9160 4.708e-02 8.419e-01 0.056 0.95541
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28292.39) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 877.57 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3236.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28292
## Std. Err.: 152106
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2910.414SA4m2a <- glm.nb(ptbBM ~ cb2.sun + sp_ptbBMns9,data=week); summary(SA4m2a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb2.sun + sp_ptbBMns9, data = week,
## init.theta = 28424.25298, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9828 -0.7391 -0.0994 0.5556 2.3919
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.125e+01 1.038e+01 -1.084 0.27835
## cb2.sunv1.l1 2.434e-01 2.307e-01 1.055 0.29129
## cb2.sunv1.l2 1.060e-01 1.564e-01 0.678 0.49795
## cb2.sunv2.l1 1.031e+00 9.082e-01 1.135 0.25622
## cb2.sunv2.l2 1.159e+00 6.101e-01 1.899 0.05755 .
## cb2.sunv3.l1 4.292e-01 3.567e-01 1.203 0.22887
## cb2.sunv3.l2 5.088e-01 2.453e-01 2.074 0.03806 *
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.239e+06 1.296e+06 -1.728 0.08400 .
## sp_ptbBMns97 3.635e+01 1.582e+01 2.298 0.02158 *
## sp_ptbBMns98 -6.689e+00 2.596e+00 -2.576 0.00999 **
## sp_ptbBMns99 1.756e+00 1.136e+00 1.545 0.12241
## sp_ptbBMns910 -2.399e+00 1.078e+00 -2.227 0.02597 *
## sp_ptbBMns911 1.254e-01 8.729e-01 0.144 0.88578
## sp_ptbBMns912 -1.196e+00 9.339e-01 -1.281 0.20016
## sp_ptbBMns913 -2.273e+00 1.130e+00 -2.011 0.04435 *
## sp_ptbBMns914 -1.407e+00 9.607e-01 -1.465 0.14297
## sp_ptbBMns915 2.076e-01 8.460e-01 0.245 0.80615
## sp_ptbBMns916 -2.006e+00 9.510e-01 -2.110 0.03488 *
## sp_ptbBMns917 2.165e-01 9.396e-01 0.230 0.81774
## sp_ptbBMns918 -2.726e+00 1.028e+00 -2.651 0.00803 **
## sp_ptbBMns919 -5.791e-01 9.760e-01 -0.593 0.55294
## sp_ptbBMns920 -1.928e+00 9.922e-01 -1.943 0.05201 .
## sp_ptbBMns921 -1.184e+00 9.633e-01 -1.229 0.21910
## sp_ptbBMns922 -1.508e+00 9.485e-01 -1.590 0.11194
## sp_ptbBMns923 -1.469e+00 9.723e-01 -1.511 0.13073
## sp_ptbBMns924 -1.260e+00 9.694e-01 -1.299 0.19381
## sp_ptbBMns925 -1.499e+00 9.466e-01 -1.583 0.11340
## sp_ptbBMns926 -1.340e+00 9.483e-01 -1.413 0.15771
## sp_ptbBMns927 -7.217e-01 9.184e-01 -0.786 0.43196
## sp_ptbBMns928 -9.442e-01 9.482e-01 -0.996 0.31934
## sp_ptbBMns929 -4.435e-01 8.934e-01 -0.496 0.61960
## sp_ptbBMns930 -1.686e+00 1.047e+00 -1.611 0.10727
## sp_ptbBMns931 -3.652e-01 9.047e-01 -0.404 0.68644
## sp_ptbBMns932 -1.260e+00 9.404e-01 -1.340 0.18017
## sp_ptbBMns933 -3.668e-01 9.655e-01 -0.380 0.70400
## sp_ptbBMns934 -1.814e+00 1.111e+00 -1.633 0.10238
## sp_ptbBMns935 -2.090e+00 1.213e+00 -1.724 0.08475 .
## sp_ptbBMns936 -2.465e+00 1.179e+00 -2.090 0.03663 *
## sp_ptbBMns937 -1.588e+00 1.036e+00 -1.534 0.12510
## sp_ptbBMns938 -1.359e+00 1.014e+00 -1.341 0.18008
## sp_ptbBMns939 -1.309e+00 9.918e-01 -1.320 0.18678
## sp_ptbBMns940 -1.325e+00 9.657e-01 -1.372 0.16997
## sp_ptbBMns941 -9.250e-01 1.022e+00 -0.905 0.36532
## sp_ptbBMns942 -3.238e+00 1.159e+00 -2.795 0.00520 **
## sp_ptbBMns943 -8.410e-01 1.065e+00 -0.790 0.42962
## sp_ptbBMns944 -1.474e+00 1.067e+00 -1.382 0.16710
## sp_ptbBMns945 -8.655e-01 1.046e+00 -0.828 0.40787
## sp_ptbBMns946 -4.608e-01 1.069e+00 -0.431 0.66646
## sp_ptbBMns947 1.300e-01 9.917e-01 0.131 0.89569
## sp_ptbBMns948 -1.161e+00 1.034e+00 -1.122 0.26180
## sp_ptbBMns949 4.275e-01 1.015e+00 0.421 0.67367
## sp_ptbBMns950 -5.964e-01 9.662e-01 -0.617 0.53705
## sp_ptbBMns951 -2.767e-01 1.121e+00 -0.247 0.80496
## sp_ptbBMns952 3.809e-02 1.021e+00 0.037 0.97023
## sp_ptbBMns953 3.482e-02 9.616e-01 0.036 0.97111
## sp_ptbBMns954 -2.932e+00 1.282e+00 -2.288 0.02215 *
## sp_ptbBMns955 9.048e-01 9.086e-01 0.996 0.31933
## sp_ptbBMns956 -2.077e+00 9.882e-01 -2.102 0.03555 *
## sp_ptbBMns957 5.147e-01 7.965e-01 0.646 0.51816
## sp_ptbBMns958 -7.988e-01 7.988e-01 -1.000 0.31729
## sp_ptbBMns959 -1.440e-01 8.403e-01 -0.171 0.86390
## sp_ptbBMns960 -1.660e+00 9.099e-01 -1.825 0.06807 .
## sp_ptbBMns961 -5.358e-01 9.192e-01 -0.583 0.55996
## sp_ptbBMns962 -7.944e-01 8.590e-01 -0.925 0.35511
## sp_ptbBMns963 -1.070e+00 1.033e+00 -1.036 0.30005
## sp_ptbBMns964 -1.438e+00 1.015e+00 -1.416 0.15665
## sp_ptbBMns965 -9.726e-01 8.519e-01 -1.142 0.25362
## sp_ptbBMns966 -1.323e-02 8.888e-01 -0.015 0.98812
## sp_ptbBMns967 -1.059e+00 8.651e-01 -1.224 0.22094
## sp_ptbBMns968 -1.670e-01 8.180e-01 -0.204 0.83821
## sp_ptbBMns969 -4.125e-01 8.371e-01 -0.493 0.62215
## sp_ptbBMns970 -6.337e-01 8.189e-01 -0.774 0.43896
## sp_ptbBMns971 -1.243e+00 9.257e-01 -1.342 0.17948
## sp_ptbBMns972 -5.112e-01 9.588e-01 -0.533 0.59389
## sp_ptbBMns973 -1.258e+00 1.061e+00 -1.185 0.23608
## sp_ptbBMns974 -6.282e-01 9.161e-01 -0.686 0.49290
## sp_ptbBMns975 -8.092e-01 9.000e-01 -0.899 0.36857
## sp_ptbBMns976 -1.142e+00 9.394e-01 -1.216 0.22415
## sp_ptbBMns977 -6.875e-01 9.328e-01 -0.737 0.46109
## sp_ptbBMns978 -6.369e-01 8.952e-01 -0.712 0.47676
## sp_ptbBMns979 -7.283e-01 9.670e-01 -0.753 0.45136
## sp_ptbBMns980 -4.043e-01 9.878e-01 -0.409 0.68232
## sp_ptbBMns981 -3.477e+00 1.116e+00 -3.116 0.00183 **
## sp_ptbBMns982 -1.247e-01 1.106e+00 -0.113 0.91019
## sp_ptbBMns983 -2.341e+00 9.438e-01 -2.480 0.01314 *
## sp_ptbBMns984 -2.473e-01 8.781e-01 -0.282 0.77823
## sp_ptbBMns985 -1.748e+00 8.917e-01 -1.960 0.04996 *
## sp_ptbBMns986 -8.484e-01 8.022e-01 -1.058 0.29024
## sp_ptbBMns987 -3.977e-01 8.184e-01 -0.486 0.62705
## sp_ptbBMns988 -1.223e+00 8.762e-01 -1.395 0.16293
## sp_ptbBMns989 -1.643e-01 9.096e-01 -0.181 0.85670
## sp_ptbBMns990 -2.234e+00 1.157e+00 -1.931 0.05353 .
## sp_ptbBMns991 -6.531e-01 1.155e+00 -0.565 0.57193
## sp_ptbBMns992 -1.426e+00 1.120e+00 -1.274 0.20261
## sp_ptbBMns993 6.754e-04 1.098e+00 0.001 0.99951
## sp_ptbBMns994 -3.031e+00 1.201e+00 -2.524 0.01159 *
## sp_ptbBMns995 1.553e+00 9.898e-01 1.569 0.11654
## sp_ptbBMns996 -2.400e+00 1.145e+00 -2.096 0.03609 *
## sp_ptbBMns997 1.102e+00 8.923e-01 1.235 0.21694
## sp_ptbBMns998 -1.167e+00 1.087e+00 -1.074 0.28291
## sp_ptbBMns999 -8.227e-02 9.847e-01 -0.084 0.93342
## sp_ptbBMns9100 -5.286e-01 9.792e-01 -0.540 0.58929
## sp_ptbBMns9101 -2.418e-01 9.222e-01 -0.262 0.79319
## sp_ptbBMns9102 -6.337e-01 9.022e-01 -0.702 0.48245
## sp_ptbBMns9103 -4.900e-01 9.647e-01 -0.508 0.61150
## sp_ptbBMns9104 -1.566e+00 1.073e+00 -1.460 0.14440
## sp_ptbBMns9105 -2.984e-01 9.225e-01 -0.323 0.74632
## sp_ptbBMns9106 -1.601e+00 9.242e-01 -1.732 0.08329 .
## sp_ptbBMns9107 -1.122e+00 9.275e-01 -1.210 0.22635
## sp_ptbBMns9108 -1.060e+00 9.221e-01 -1.150 0.25026
## sp_ptbBMns9109 -2.078e+00 1.029e+00 -2.019 0.04353 *
## sp_ptbBMns9110 -1.924e+00 1.036e+00 -1.858 0.06323 .
## sp_ptbBMns9111 -9.111e-01 9.746e-01 -0.935 0.34985
## sp_ptbBMns9112 -6.603e-01 9.356e-01 -0.706 0.48031
## sp_ptbBMns9113 -1.141e+00 9.790e-01 -1.166 0.24371
## sp_ptbBMns9114 -6.295e-01 1.068e+00 -0.589 0.55565
## sp_ptbBMns9115 -2.257e+00 1.150e+00 -1.963 0.04961 *
## sp_ptbBMns9116 8.388e-02 1.106e+00 0.076 0.93956
## sp_ptbBMns9117 -1.634e+00 1.183e+00 -1.382 0.16702
## sp_ptbBMns9118 -5.094e-01 9.866e-01 -0.516 0.60562
## sp_ptbBMns9119 -2.716e-01 9.377e-01 -0.290 0.77204
## sp_ptbBMns9120 -7.155e-01 9.668e-01 -0.740 0.45928
## sp_ptbBMns9121 -9.457e-01 9.888e-01 -0.956 0.33886
## sp_ptbBMns9122 -9.169e-01 9.541e-01 -0.961 0.33652
## sp_ptbBMns9123 3.078e-01 8.610e-01 0.357 0.72074
## sp_ptbBMns9124 -2.439e-01 8.824e-01 -0.276 0.78225
## sp_ptbBMns9125 1.124e+00 8.879e-01 1.266 0.20542
## sp_ptbBMns9126 -2.761e+00 1.251e+00 -2.208 0.02728 *
## sp_ptbBMns9127 9.327e-01 9.325e-01 1.000 0.31723
## sp_ptbBMns9128 -1.399e+00 9.608e-01 -1.456 0.14545
## sp_ptbBMns9129 2.051e-01 8.646e-01 0.237 0.81245
## sp_ptbBMns9130 -7.154e-01 8.332e-01 -0.859 0.39052
## sp_ptbBMns9131 -1.643e-01 8.124e-01 -0.202 0.83974
## sp_ptbBMns9132 -8.468e-01 8.552e-01 -0.990 0.32205
## sp_ptbBMns9133 -9.074e-01 8.403e-01 -1.080 0.28023
## sp_ptbBMns9134 -1.541e-01 8.236e-01 -0.187 0.85160
## sp_ptbBMns9135 -1.666e+00 1.140e+00 -1.460 0.14418
## sp_ptbBMns9136 1.136e-01 1.009e+00 0.113 0.91036
## sp_ptbBMns9137 -2.555e+00 1.060e+00 -2.411 0.01593 *
## sp_ptbBMns9138 -8.608e-02 1.014e+00 -0.085 0.93237
## sp_ptbBMns9139 -2.040e+00 1.126e+00 -1.813 0.06991 .
## sp_ptbBMns9140 -1.018e+00 1.016e+00 -1.002 0.31638
## sp_ptbBMns9141 -2.089e+00 1.136e+00 -1.839 0.06592 .
## sp_ptbBMns9142 -8.530e-01 1.143e+00 -0.746 0.45558
## sp_ptbBMns9143 -1.798e+00 1.098e+00 -1.636 0.10174
## sp_ptbBMns9144 -9.280e-01 9.054e-01 -1.025 0.30540
## sp_ptbBMns9145 -3.655e-01 8.980e-01 -0.407 0.68404
## sp_ptbBMns9146 2.468e-01 8.397e-01 0.294 0.76886
## sp_ptbBMns9147 2.747e-01 8.572e-01 0.320 0.74859
## sp_ptbBMns9148 -9.975e-01 9.537e-01 -1.046 0.29559
## sp_ptbBMns9149 4.079e-01 8.253e-01 0.494 0.62114
## sp_ptbBMns9150 5.353e-01 8.135e-01 0.658 0.51055
## sp_ptbBMns9151 -2.074e+00 9.954e-01 -2.084 0.03720 *
## sp_ptbBMns9152 1.112e+00 9.127e-01 1.218 0.22313
## sp_ptbBMns9153 -1.555e+00 1.059e+00 -1.467 0.14224
## sp_ptbBMns9154 -1.293e+00 1.098e+00 -1.177 0.23900
## sp_ptbBMns9155 4.692e-01 8.283e-01 0.566 0.57106
## sp_ptbBMns9156 -1.261e+00 9.526e-01 -1.324 0.18545
## sp_ptbBMns9157 9.371e-01 7.257e-01 1.291 0.19659
## sp_ptbBMns9158 1.106e-01 8.014e-01 0.138 0.89025
## sp_ptbBMns9159 1.323e+00 6.384e-01 2.072 0.03825 *
## sp_ptbBMns9160 3.149e-01 8.500e-01 0.371 0.71100
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28424.25) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 880.04 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3238.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28424
## Std. Err.: 155485
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2912.888SA4m3a <- glm.nb(ptbBM ~ cb3.RF + sp_ptbBMns9,data=week); summary(SA4m3a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + sp_ptbBMns9, data = week, init.theta = 27894.8583,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.02133 -0.74382 -0.09557 0.54575 2.35117
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.948e+00 2.934e+00 1.005 0.31501
## cb3.RFv1.l1 9.686e-02 2.864e-01 0.338 0.73520
## cb3.RFv1.l2 1.589e-01 2.070e-01 0.768 0.44264
## cb3.RFv2.l1 -3.992e-01 4.456e-01 -0.896 0.37035
## cb3.RFv2.l2 -2.651e-01 3.376e-01 -0.785 0.43230
## cb3.RFv3.l1 -5.042e-01 7.143e-01 -0.706 0.48025
## cb3.RFv3.l2 -6.828e-01 5.734e-01 -1.191 0.23372
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.357e+06 1.300e+06 -1.813 0.06986 .
## sp_ptbBMns97 3.787e+01 1.592e+01 2.379 0.01735 *
## sp_ptbBMns98 -5.758e+00 2.589e+00 -2.224 0.02612 *
## sp_ptbBMns99 1.847e+00 1.182e+00 1.563 0.11813
## sp_ptbBMns910 -2.293e+00 1.148e+00 -1.997 0.04577 *
## sp_ptbBMns911 2.182e-01 9.607e-01 0.227 0.82028
## sp_ptbBMns912 -8.498e-01 1.008e+00 -0.843 0.39925
## sp_ptbBMns913 -1.834e+00 1.213e+00 -1.512 0.13065
## sp_ptbBMns914 -1.324e+00 1.002e+00 -1.321 0.18642
## sp_ptbBMns915 5.348e-01 8.999e-01 0.594 0.55232
## sp_ptbBMns916 -1.118e+00 9.688e-01 -1.154 0.24850
## sp_ptbBMns917 1.250e+00 9.787e-01 1.277 0.20157
## sp_ptbBMns918 -1.828e+00 9.907e-01 -1.846 0.06495 .
## sp_ptbBMns919 3.541e-01 9.572e-01 0.370 0.71143
## sp_ptbBMns920 -1.023e+00 9.529e-01 -1.073 0.28305
## sp_ptbBMns921 -4.079e-01 1.050e+00 -0.388 0.69766
## sp_ptbBMns922 -5.090e-01 9.598e-01 -0.530 0.59591
## sp_ptbBMns923 -4.322e-01 1.039e+00 -0.416 0.67745
## sp_ptbBMns924 -4.027e-01 9.151e-01 -0.440 0.65991
## sp_ptbBMns925 -3.799e-01 1.026e+00 -0.370 0.71120
## sp_ptbBMns926 -5.069e-01 1.107e+00 -0.458 0.64711
## sp_ptbBMns927 2.938e-01 9.782e-01 0.300 0.76390
## sp_ptbBMns928 -5.779e-02 9.901e-01 -0.058 0.95346
## sp_ptbBMns929 1.678e-01 9.555e-01 0.176 0.86062
## sp_ptbBMns930 -1.052e+00 1.036e+00 -1.015 0.31019
## sp_ptbBMns931 2.377e-01 9.884e-01 0.240 0.80996
## sp_ptbBMns932 -5.926e-01 1.025e+00 -0.578 0.56332
## sp_ptbBMns933 5.785e-01 9.896e-01 0.585 0.55887
## sp_ptbBMns934 -6.121e-01 1.136e+00 -0.539 0.59004
## sp_ptbBMns935 -1.031e+00 1.270e+00 -0.812 0.41688
## sp_ptbBMns936 -1.505e+00 1.227e+00 -1.226 0.22016
## sp_ptbBMns937 -4.289e-01 1.073e+00 -0.400 0.68949
## sp_ptbBMns938 -1.691e-01 1.015e+00 -0.167 0.86762
## sp_ptbBMns939 -5.811e-01 9.827e-01 -0.591 0.55426
## sp_ptbBMns940 -1.795e-01 8.988e-01 -0.200 0.84174
## sp_ptbBMns941 1.227e-01 8.850e-01 0.139 0.88969
## sp_ptbBMns942 -2.054e+00 1.144e+00 -1.796 0.07251 .
## sp_ptbBMns943 -4.974e-02 1.014e+00 -0.049 0.96088
## sp_ptbBMns944 -4.024e-01 1.009e+00 -0.399 0.69004
## sp_ptbBMns945 -2.114e-01 9.694e-01 -0.218 0.82735
## sp_ptbBMns946 1.148e-01 7.885e-01 0.146 0.88421
## sp_ptbBMns947 4.545e-01 8.474e-01 0.536 0.59173
## sp_ptbBMns948 -7.332e-01 9.364e-01 -0.783 0.43363
## sp_ptbBMns949 2.993e-01 8.674e-01 0.345 0.73005
## sp_ptbBMns950 -6.380e-01 8.704e-01 -0.733 0.46356
## sp_ptbBMns951 -2.096e-01 9.113e-01 -0.230 0.81811
## sp_ptbBMns952 -3.174e-01 8.653e-01 -0.367 0.71372
## sp_ptbBMns953 3.667e-01 9.442e-01 0.388 0.69770
## sp_ptbBMns954 -2.666e+00 1.211e+00 -2.202 0.02769 *
## sp_ptbBMns955 1.030e+00 9.031e-01 1.141 0.25399
## sp_ptbBMns956 -1.992e+00 1.029e+00 -1.936 0.05289 .
## sp_ptbBMns957 9.172e-01 8.668e-01 1.058 0.29001
## sp_ptbBMns958 -3.320e-01 9.339e-01 -0.356 0.72221
## sp_ptbBMns959 7.954e-02 9.261e-01 0.086 0.93156
## sp_ptbBMns960 -6.055e-01 9.197e-01 -0.658 0.51033
## sp_ptbBMns961 5.414e-01 8.351e-01 0.648 0.51680
## sp_ptbBMns962 -3.832e-01 9.052e-01 -0.423 0.67209
## sp_ptbBMns963 6.460e-01 8.941e-01 0.723 0.46994
## sp_ptbBMns964 -3.667e-01 9.555e-01 -0.384 0.70112
## sp_ptbBMns965 -1.644e-02 9.169e-01 -0.018 0.98570
## sp_ptbBMns966 1.025e+00 8.827e-01 1.161 0.24573
## sp_ptbBMns967 -1.347e-01 9.190e-01 -0.147 0.88350
## sp_ptbBMns968 8.422e-02 9.397e-01 0.090 0.92859
## sp_ptbBMns969 2.055e-01 8.579e-01 0.239 0.81072
## sp_ptbBMns970 7.969e-03 1.026e+00 0.008 0.99381
## sp_ptbBMns971 -3.617e-01 9.740e-01 -0.371 0.71035
## sp_ptbBMns972 -1.677e-01 1.481e+00 -0.113 0.90984
## sp_ptbBMns973 -2.000e-01 1.111e+00 -0.180 0.85711
## sp_ptbBMns974 -2.422e-02 1.150e+00 -0.021 0.98319
## sp_ptbBMns975 -2.304e-01 1.029e+00 -0.224 0.82288
## sp_ptbBMns976 -5.481e-01 1.090e+00 -0.503 0.61508
## sp_ptbBMns977 -3.042e-01 1.057e+00 -0.288 0.77353
## sp_ptbBMns978 4.897e-01 1.083e+00 0.452 0.65112
## sp_ptbBMns979 -9.123e-02 9.662e-01 -0.094 0.92478
## sp_ptbBMns980 1.521e+00 1.261e+00 1.207 0.22760
## sp_ptbBMns981 -2.661e+00 1.439e+00 -1.849 0.06441 .
## sp_ptbBMns982 1.057e+00 1.188e+00 0.890 0.37366
## sp_ptbBMns983 -1.197e+00 1.176e+00 -1.017 0.30901
## sp_ptbBMns984 7.683e-01 1.003e+00 0.766 0.44362
## sp_ptbBMns985 -1.430e-01 9.796e-01 -0.146 0.88392
## sp_ptbBMns986 3.161e-01 1.056e+00 0.299 0.76474
## sp_ptbBMns987 1.222e+00 9.325e-01 1.310 0.19007
## sp_ptbBMns988 1.622e-01 9.192e-01 0.176 0.85992
## sp_ptbBMns989 1.697e+00 1.135e+00 1.495 0.13495
## sp_ptbBMns990 -7.799e-01 1.066e+00 -0.731 0.46453
## sp_ptbBMns991 -1.089e-01 1.182e+00 -0.092 0.92656
## sp_ptbBMns992 -5.863e-01 1.086e+00 -0.540 0.58914
## sp_ptbBMns993 6.395e-01 1.040e+00 0.615 0.53861
## sp_ptbBMns994 -2.090e+00 1.090e+00 -1.917 0.05528 .
## sp_ptbBMns995 2.146e+00 7.855e-01 2.733 0.00628 **
## sp_ptbBMns996 -1.994e+00 9.217e-01 -2.163 0.03050 *
## sp_ptbBMns997 1.572e+00 8.663e-01 1.815 0.06951 .
## sp_ptbBMns998 -8.712e-01 9.340e-01 -0.933 0.35096
## sp_ptbBMns999 5.508e-01 9.083e-01 0.606 0.54423
## sp_ptbBMns9100 5.301e-02 9.213e-01 0.058 0.95412
## sp_ptbBMns9101 8.362e-01 1.015e+00 0.824 0.41003
## sp_ptbBMns9102 2.306e-02 9.601e-01 0.024 0.98084
## sp_ptbBMns9103 1.229e-01 9.622e-01 0.128 0.89835
## sp_ptbBMns9104 -7.700e-01 9.947e-01 -0.774 0.43889
## sp_ptbBMns9105 6.733e-01 9.290e-01 0.725 0.46855
## sp_ptbBMns9106 -1.026e+00 8.759e-01 -1.172 0.24124
## sp_ptbBMns9107 -1.474e-01 9.026e-01 -0.163 0.87024
## sp_ptbBMns9108 1.231e-01 9.031e-01 0.136 0.89155
## sp_ptbBMns9109 -1.087e+00 9.526e-01 -1.141 0.25398
## sp_ptbBMns9110 -5.011e-01 9.454e-01 -0.530 0.59607
## sp_ptbBMns9111 3.906e-01 7.887e-01 0.495 0.62046
## sp_ptbBMns9112 1.801e-02 7.899e-01 0.023 0.98181
## sp_ptbBMns9113 -6.057e-01 8.730e-01 -0.694 0.48779
## sp_ptbBMns9114 1.171e-01 8.667e-01 0.135 0.89250
## sp_ptbBMns9115 -1.479e+00 9.867e-01 -1.499 0.13383
## sp_ptbBMns9116 6.474e-01 8.990e-01 0.720 0.47143
## sp_ptbBMns9117 -6.790e-01 1.135e+00 -0.598 0.54980
## sp_ptbBMns9118 -2.411e-01 8.929e-01 -0.270 0.78717
## sp_ptbBMns9119 2.320e-01 9.023e-01 0.257 0.79712
## sp_ptbBMns9120 -3.652e-01 9.762e-01 -0.374 0.70832
## sp_ptbBMns9121 -3.310e-01 1.005e+00 -0.329 0.74193
## sp_ptbBMns9122 -4.428e-01 9.982e-01 -0.444 0.65732
## sp_ptbBMns9123 7.278e-01 8.426e-01 0.864 0.38773
## sp_ptbBMns9124 1.082e-01 8.082e-01 0.134 0.89346
## sp_ptbBMns9125 1.312e+00 9.685e-01 1.354 0.17560
## sp_ptbBMns9126 -2.001e+00 1.180e+00 -1.695 0.09008 .
## sp_ptbBMns9127 1.239e+00 9.767e-01 1.268 0.20473
## sp_ptbBMns9128 -1.227e+00 1.017e+00 -1.206 0.22791
## sp_ptbBMns9129 4.688e-01 8.973e-01 0.523 0.60132
## sp_ptbBMns9130 -4.456e-01 8.369e-01 -0.532 0.59442
## sp_ptbBMns9131 2.306e-01 8.374e-01 0.275 0.78306
## sp_ptbBMns9132 -5.815e-01 8.991e-01 -0.647 0.51776
## sp_ptbBMns9133 -2.212e-01 8.472e-01 -0.261 0.79405
## sp_ptbBMns9134 2.898e-01 8.919e-01 0.325 0.74523
## sp_ptbBMns9135 -1.103e+00 1.063e+00 -1.038 0.29921
## sp_ptbBMns9136 7.587e-01 1.066e+00 0.712 0.47652
## sp_ptbBMns9137 -2.127e+00 1.063e+00 -2.001 0.04538 *
## sp_ptbBMns9138 7.072e-01 9.204e-01 0.768 0.44226
## sp_ptbBMns9139 -7.257e-01 9.152e-01 -0.793 0.42778
## sp_ptbBMns9140 2.327e-01 9.661e-01 0.241 0.80962
## sp_ptbBMns9141 -6.055e-01 9.020e-01 -0.671 0.50208
## sp_ptbBMns9142 7.941e-01 8.757e-01 0.907 0.36453
## sp_ptbBMns9143 -5.558e-01 1.095e+00 -0.507 0.61187
## sp_ptbBMns9144 -6.372e-01 1.034e+00 -0.616 0.53772
## sp_ptbBMns9145 -8.785e-02 8.828e-01 -0.100 0.92073
## sp_ptbBMns9146 4.012e-01 8.120e-01 0.494 0.62122
## sp_ptbBMns9147 5.099e-02 8.994e-01 0.057 0.95479
## sp_ptbBMns9148 -1.021e+00 9.667e-01 -1.056 0.29112
## sp_ptbBMns9149 5.346e-01 8.010e-01 0.667 0.50450
## sp_ptbBMns9150 5.459e-01 8.342e-01 0.654 0.51282
## sp_ptbBMns9151 -1.376e+00 9.235e-01 -1.490 0.13622
## sp_ptbBMns9152 1.518e+00 9.095e-01 1.669 0.09505 .
## sp_ptbBMns9153 -1.257e+00 1.129e+00 -1.113 0.26564
## sp_ptbBMns9154 -5.614e-01 1.014e+00 -0.554 0.57970
## sp_ptbBMns9155 6.101e-01 8.630e-01 0.707 0.47959
## sp_ptbBMns9156 -1.200e+00 8.969e-01 -1.338 0.18089
## sp_ptbBMns9157 9.732e-01 7.682e-01 1.267 0.20519
## sp_ptbBMns9158 -5.766e-01 8.480e-01 -0.680 0.49657
## sp_ptbBMns9159 8.594e-01 6.349e-01 1.354 0.17585
## sp_ptbBMns9160 -1.185e-01 8.126e-01 -0.146 0.88405
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(27894.86) family taken to be 1)
##
## Null deviance: 1101.3 on 886 degrees of freedom
## Residual deviance: 884.4 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3243.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 27895
## Std. Err.: 153572
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2917.245SA4m5a <- glm.nb(ptbBM ~ cb5.minRH + sp_ptbBMns9,data=week); summary(SA4m5a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBMns9, data = week,
## init.theta = 28286.77676, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.86355 -0.72927 -0.08996 0.56738 2.41082
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.906e+01 1.163e+01 1.639 0.10129
## cb5.minRHv1.l1 -1.304e-01 2.747e-01 -0.475 0.63498
## cb5.minRHv1.l2 -2.200e-02 1.886e-01 -0.117 0.90712
## cb5.minRHv2.l1 -1.762e+00 1.013e+00 -1.739 0.08201 .
## cb5.minRHv2.l2 -8.531e-01 7.616e-01 -1.120 0.26265
## cb5.minRHv3.l1 -1.028e+00 6.518e-01 -1.577 0.11477
## cb5.minRHv3.l2 -6.854e-01 5.151e-01 -1.330 0.18336
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.263e+06 1.299e+06 -1.742 0.08146 .
## sp_ptbBMns97 3.683e+01 1.582e+01 2.327 0.01996 *
## sp_ptbBMns98 -6.531e+00 2.605e+00 -2.507 0.01217 *
## sp_ptbBMns99 1.621e+00 1.208e+00 1.342 0.17958
## sp_ptbBMns910 -2.418e+00 1.118e+00 -2.162 0.03062 *
## sp_ptbBMns911 1.790e-01 1.031e+00 0.174 0.86211
## sp_ptbBMns912 -1.147e+00 1.039e+00 -1.104 0.26953
## sp_ptbBMns913 -1.856e+00 1.121e+00 -1.656 0.09782 .
## sp_ptbBMns914 -1.838e+00 1.137e+00 -1.616 0.10614
## sp_ptbBMns915 6.812e-02 9.566e-01 0.071 0.94323
## sp_ptbBMns916 -1.675e+00 9.506e-01 -1.762 0.07807 .
## sp_ptbBMns917 3.401e-01 1.046e+00 0.325 0.74508
## sp_ptbBMns918 -2.271e+00 1.201e+00 -1.891 0.05866 .
## sp_ptbBMns919 1.720e-02 1.303e+00 0.013 0.98947
## sp_ptbBMns920 -1.662e+00 1.346e+00 -1.235 0.21696
## sp_ptbBMns921 -9.859e-01 1.436e+00 -0.687 0.49237
## sp_ptbBMns922 -1.392e+00 1.236e+00 -1.127 0.25987
## sp_ptbBMns923 -5.537e-01 1.252e+00 -0.442 0.65831
## sp_ptbBMns924 -6.076e-01 1.163e+00 -0.523 0.60131
## sp_ptbBMns925 -6.465e-01 1.178e+00 -0.549 0.58327
## sp_ptbBMns926 -7.508e-01 1.200e+00 -0.625 0.53164
## sp_ptbBMns927 -2.175e-01 1.183e+00 -0.184 0.85408
## sp_ptbBMns928 -3.544e-01 1.032e+00 -0.343 0.73127
## sp_ptbBMns929 -3.508e-01 1.102e+00 -0.318 0.75019
## sp_ptbBMns930 -1.521e+00 1.037e+00 -1.467 0.14226
## sp_ptbBMns931 -3.213e-01 9.513e-01 -0.338 0.73559
## sp_ptbBMns932 -9.824e-01 8.611e-01 -1.141 0.25393
## sp_ptbBMns933 -4.346e-02 8.577e-01 -0.051 0.95959
## sp_ptbBMns934 -1.222e+00 9.605e-01 -1.273 0.20315
## sp_ptbBMns935 -9.898e-01 1.042e+00 -0.950 0.34216
## sp_ptbBMns936 -2.169e+00 1.147e+00 -1.892 0.05855 .
## sp_ptbBMns937 -6.879e-01 1.002e+00 -0.687 0.49229
## sp_ptbBMns938 -4.001e-01 1.034e+00 -0.387 0.69881
## sp_ptbBMns939 -1.014e+00 1.096e+00 -0.926 0.35450
## sp_ptbBMns940 -6.083e-01 1.253e+00 -0.485 0.62746
## sp_ptbBMns941 -5.224e-01 1.429e+00 -0.365 0.71474
## sp_ptbBMns942 -2.988e+00 1.638e+00 -1.823 0.06825 .
## sp_ptbBMns943 -7.512e-01 1.436e+00 -0.523 0.60084
## sp_ptbBMns944 -1.629e+00 1.606e+00 -1.014 0.31039
## sp_ptbBMns945 -1.604e+00 1.697e+00 -0.945 0.34465
## sp_ptbBMns946 -1.075e+00 1.817e+00 -0.591 0.55424
## sp_ptbBMns947 -9.659e-01 1.648e+00 -0.586 0.55787
## sp_ptbBMns948 -1.653e+00 1.520e+00 -1.087 0.27696
## sp_ptbBMns949 -3.564e-01 1.274e+00 -0.280 0.77960
## sp_ptbBMns950 -9.420e-01 1.382e+00 -0.682 0.49535
## sp_ptbBMns951 -6.569e-01 1.153e+00 -0.570 0.56893
## sp_ptbBMns952 -7.187e-01 1.130e+00 -0.636 0.52469
## sp_ptbBMns953 -2.003e-01 1.143e+00 -0.175 0.86094
## sp_ptbBMns954 -3.393e+00 1.326e+00 -2.560 0.01047 *
## sp_ptbBMns955 3.670e-01 1.056e+00 0.347 0.72823
## sp_ptbBMns956 -2.553e+00 1.099e+00 -2.324 0.02014 *
## sp_ptbBMns957 1.640e-01 9.895e-01 0.166 0.86839
## sp_ptbBMns958 -1.290e+00 1.050e+00 -1.229 0.21913
## sp_ptbBMns959 -4.310e-01 1.018e+00 -0.423 0.67195
## sp_ptbBMns960 -1.451e+00 1.145e+00 -1.268 0.20483
## sp_ptbBMns961 -4.434e-01 1.007e+00 -0.440 0.65962
## sp_ptbBMns962 -1.250e+00 9.236e-01 -1.354 0.17586
## sp_ptbBMns963 -1.914e-01 1.196e+00 -0.160 0.87281
## sp_ptbBMns964 -1.658e+00 1.251e+00 -1.325 0.18506
## sp_ptbBMns965 -1.034e+00 1.072e+00 -0.965 0.33477
## sp_ptbBMns966 -3.991e-01 1.204e+00 -0.332 0.74016
## sp_ptbBMns967 -1.111e+00 1.122e+00 -0.990 0.32213
## sp_ptbBMns968 -2.587e-01 1.056e+00 -0.245 0.80645
## sp_ptbBMns969 -2.235e-01 1.039e+00 -0.215 0.82974
## sp_ptbBMns970 -4.242e-01 1.022e+00 -0.415 0.67805
## sp_ptbBMns971 -9.195e-01 1.178e+00 -0.781 0.43493
## sp_ptbBMns972 -4.197e-01 1.131e+00 -0.371 0.71054
## sp_ptbBMns973 -8.902e-01 1.276e+00 -0.698 0.48526
## sp_ptbBMns974 -3.806e-01 1.029e+00 -0.370 0.71147
## sp_ptbBMns975 -8.280e-01 1.082e+00 -0.766 0.44396
## sp_ptbBMns976 -1.365e+00 1.109e+00 -1.231 0.21837
## sp_ptbBMns977 -1.311e+00 1.285e+00 -1.020 0.30766
## sp_ptbBMns978 -4.455e-01 1.142e+00 -0.390 0.69649
## sp_ptbBMns979 -1.285e+00 1.193e+00 -1.078 0.28114
## sp_ptbBMns980 6.618e-02 1.165e+00 0.057 0.95470
## sp_ptbBMns981 -3.669e+00 1.440e+00 -2.549 0.01080 *
## sp_ptbBMns982 -2.878e-01 1.557e+00 -0.185 0.85336
## sp_ptbBMns983 -2.932e+00 1.543e+00 -1.900 0.05738 .
## sp_ptbBMns984 -6.059e-01 1.183e+00 -0.512 0.60848
## sp_ptbBMns985 -1.415e+00 1.032e+00 -1.372 0.17013
## sp_ptbBMns986 -4.478e-01 8.471e-01 -0.529 0.59708
## sp_ptbBMns987 2.025e-01 7.203e-01 0.281 0.77865
## sp_ptbBMns988 -5.911e-01 7.728e-01 -0.765 0.44436
## sp_ptbBMns989 3.798e-01 8.214e-01 0.462 0.64382
## sp_ptbBMns990 -1.358e+00 1.071e+00 -1.268 0.20482
## sp_ptbBMns991 -9.822e-01 1.100e+00 -0.893 0.37176
## sp_ptbBMns992 -1.244e+00 1.096e+00 -1.134 0.25675
## sp_ptbBMns993 -3.896e-03 9.957e-01 -0.004 0.99688
## sp_ptbBMns994 -2.976e+00 1.101e+00 -2.703 0.00688 **
## sp_ptbBMns995 1.941e+00 8.847e-01 2.194 0.02821 *
## sp_ptbBMns996 -2.024e+00 9.568e-01 -2.115 0.03444 *
## sp_ptbBMns997 1.275e+00 9.183e-01 1.388 0.16499
## sp_ptbBMns998 -8.197e-01 8.338e-01 -0.983 0.32557
## sp_ptbBMns999 -1.236e-02 9.248e-01 -0.013 0.98934
## sp_ptbBMns9100 -2.289e-01 1.087e+00 -0.211 0.83317
## sp_ptbBMns9101 4.613e-01 1.126e+00 0.410 0.68212
## sp_ptbBMns9102 -2.225e-01 9.623e-01 -0.231 0.81715
## sp_ptbBMns9103 -5.088e-01 1.071e+00 -0.475 0.63466
## sp_ptbBMns9104 -1.203e+00 9.557e-01 -1.259 0.20817
## sp_ptbBMns9105 1.598e-01 8.993e-01 0.178 0.85894
## sp_ptbBMns9106 -1.414e+00 8.653e-01 -1.634 0.10231
## sp_ptbBMns9107 -1.008e+00 9.536e-01 -1.058 0.29027
## sp_ptbBMns9108 -6.837e-01 1.030e+00 -0.664 0.50669
## sp_ptbBMns9109 -1.964e+00 1.167e+00 -1.682 0.09251 .
## sp_ptbBMns9110 -1.402e+00 1.250e+00 -1.121 0.26214
## sp_ptbBMns9111 -6.531e-01 1.097e+00 -0.595 0.55173
## sp_ptbBMns9112 -1.731e-01 8.714e-01 -0.199 0.84255
## sp_ptbBMns9113 -9.763e-01 9.151e-01 -1.067 0.28603
## sp_ptbBMns9114 -3.351e-01 9.518e-01 -0.352 0.72482
## sp_ptbBMns9115 -1.755e+00 9.626e-01 -1.823 0.06834 .
## sp_ptbBMns9116 3.913e-02 9.940e-01 0.039 0.96859
## sp_ptbBMns9117 -1.076e+00 1.770e+00 -0.608 0.54316
## sp_ptbBMns9118 -2.628e-01 1.610e+00 -0.163 0.87037
## sp_ptbBMns9119 6.114e-01 1.422e+00 0.430 0.66726
## sp_ptbBMns9120 1.230e-01 1.458e+00 0.084 0.93276
## sp_ptbBMns9121 4.687e-01 1.279e+00 0.366 0.71405
## sp_ptbBMns9122 4.459e-01 1.271e+00 0.351 0.72580
## sp_ptbBMns9123 1.926e+00 1.279e+00 1.506 0.13216
## sp_ptbBMns9124 1.280e+00 1.401e+00 0.913 0.36112
## sp_ptbBMns9125 2.705e+00 1.551e+00 1.744 0.08120 .
## sp_ptbBMns9126 -2.027e+00 1.602e+00 -1.265 0.20588
## sp_ptbBMns9127 1.121e+00 1.366e+00 0.821 0.41164
## sp_ptbBMns9128 -7.239e-01 1.414e+00 -0.512 0.60864
## sp_ptbBMns9129 9.896e-01 1.227e+00 0.806 0.42007
## sp_ptbBMns9130 2.314e-01 1.205e+00 0.192 0.84776
## sp_ptbBMns9131 1.209e+00 1.131e+00 1.069 0.28486
## sp_ptbBMns9132 6.909e-01 1.144e+00 0.604 0.54581
## sp_ptbBMns9133 6.994e-01 1.094e+00 0.639 0.52263
## sp_ptbBMns9134 1.304e+00 1.058e+00 1.232 0.21795
## sp_ptbBMns9135 -1.291e+00 9.715e-01 -1.329 0.18399
## sp_ptbBMns9136 9.430e-01 1.174e+00 0.803 0.42170
## sp_ptbBMns9137 -1.738e+00 1.065e+00 -1.631 0.10279
## sp_ptbBMns9138 6.439e-01 9.503e-01 0.678 0.49803
## sp_ptbBMns9139 -6.649e-01 9.068e-01 -0.733 0.46340
## sp_ptbBMns9140 4.075e-01 8.531e-01 0.478 0.63291
## sp_ptbBMns9141 -1.645e-01 7.605e-01 -0.216 0.82877
## sp_ptbBMns9142 1.213e+00 7.427e-01 1.633 0.10243
## sp_ptbBMns9143 1.172e-01 7.664e-01 0.153 0.87849
## sp_ptbBMns9144 -2.523e-01 7.804e-01 -0.323 0.74644
## sp_ptbBMns9145 -2.263e-01 1.034e+00 -0.219 0.82672
## sp_ptbBMns9146 1.717e-01 9.100e-01 0.189 0.85033
## sp_ptbBMns9147 -2.372e-02 1.043e+00 -0.023 0.98185
## sp_ptbBMns9148 -1.026e+00 1.010e+00 -1.016 0.30977
## sp_ptbBMns9149 5.527e-01 8.572e-01 0.645 0.51906
## sp_ptbBMns9150 4.723e-01 1.013e+00 0.466 0.64091
## sp_ptbBMns9151 -1.703e+00 1.014e+00 -1.679 0.09319 .
## sp_ptbBMns9152 1.350e+00 9.003e-01 1.499 0.13381
## sp_ptbBMns9153 -1.157e+00 1.115e+00 -1.038 0.29930
## sp_ptbBMns9154 -1.236e+00 1.282e+00 -0.964 0.33499
## sp_ptbBMns9155 4.781e-01 1.024e+00 0.467 0.64055
## sp_ptbBMns9156 -1.416e+00 1.155e+00 -1.226 0.22017
## sp_ptbBMns9157 9.910e-01 8.707e-01 1.138 0.25505
## sp_ptbBMns9158 -5.751e-01 8.481e-01 -0.678 0.49772
## sp_ptbBMns9159 1.020e+00 6.377e-01 1.599 0.10981
## sp_ptbBMns9160 -4.082e-03 7.966e-01 -0.005 0.99591
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28286.78) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 881.61 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3240.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28287
## Std. Err.: 154283
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2914.458SA4m6a <- glm.nb(ptbBM ~ cb6.meanRH + sp_ptbBMns9,data=week); summary(SA4m6a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb6.meanRH + sp_ptbBMns9, data = week,
## init.theta = 27875.54668, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.93814 -0.74876 -0.08958 0.54504 2.41956
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.252e+01 1.369e+01 0.915 0.36042
## cb6.meanRHv1.l1 -3.541e-01 3.190e-01 -1.110 0.26694
## cb6.meanRHv1.l2 -1.628e-01 2.067e-01 -0.788 0.43087
## cb6.meanRHv2.l1 -7.772e-01 1.119e+00 -0.695 0.48733
## cb6.meanRHv2.l2 1.512e-01 7.771e-01 0.195 0.84575
## cb6.meanRHv3.l1 -1.924e-01 5.080e-01 -0.379 0.70490
## cb6.meanRHv3.l2 4.056e-02 3.908e-01 0.104 0.91734
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.213e+06 1.297e+06 -1.705 0.08812 .
## sp_ptbBMns97 3.636e+01 1.582e+01 2.299 0.02153 *
## sp_ptbBMns98 -5.881e+00 2.611e+00 -2.252 0.02432 *
## sp_ptbBMns99 2.093e+00 1.228e+00 1.704 0.08830 .
## sp_ptbBMns910 -2.130e+00 1.094e+00 -1.948 0.05147 .
## sp_ptbBMns911 1.450e-01 9.995e-01 0.145 0.88462
## sp_ptbBMns912 -1.178e+00 1.043e+00 -1.130 0.25835
## sp_ptbBMns913 -1.762e+00 1.201e+00 -1.468 0.14223
## sp_ptbBMns914 -8.972e-01 1.109e+00 -0.809 0.41837
## sp_ptbBMns915 7.033e-01 9.137e-01 0.770 0.44149
## sp_ptbBMns916 -1.228e+00 9.622e-01 -1.276 0.20182
## sp_ptbBMns917 8.892e-01 1.173e+00 0.758 0.44845
## sp_ptbBMns918 -2.111e+00 1.237e+00 -1.707 0.08781 .
## sp_ptbBMns919 3.547e-01 1.226e+00 0.289 0.77239
## sp_ptbBMns920 -1.571e+00 1.234e+00 -1.273 0.20292
## sp_ptbBMns921 -9.359e-01 1.368e+00 -0.684 0.49384
## sp_ptbBMns922 -1.088e+00 1.189e+00 -0.915 0.36028
## sp_ptbBMns923 -3.110e-01 1.260e+00 -0.247 0.80512
## sp_ptbBMns924 -3.745e-01 1.164e+00 -0.322 0.74768
## sp_ptbBMns925 -7.551e-01 1.220e+00 -0.619 0.53583
## sp_ptbBMns926 -1.216e+00 1.260e+00 -0.965 0.33430
## sp_ptbBMns927 -3.036e-01 1.121e+00 -0.271 0.78653
## sp_ptbBMns928 -2.547e-01 1.091e+00 -0.234 0.81537
## sp_ptbBMns929 -8.742e-01 1.419e+00 -0.616 0.53788
## sp_ptbBMns930 -2.219e+00 1.665e+00 -1.333 0.18265
## sp_ptbBMns931 -1.645e+00 2.144e+00 -0.767 0.44310
## sp_ptbBMns932 -2.823e+00 2.401e+00 -1.176 0.23974
## sp_ptbBMns933 -1.948e+00 2.719e+00 -0.717 0.47355
## sp_ptbBMns934 -3.563e+00 2.850e+00 -1.250 0.21123
## sp_ptbBMns935 -4.370e+00 3.129e+00 -1.397 0.16251
## sp_ptbBMns936 -4.820e+00 3.134e+00 -1.538 0.12403
## sp_ptbBMns937 -3.651e+00 3.183e+00 -1.147 0.25143
## sp_ptbBMns938 -3.310e+00 3.088e+00 -1.072 0.28364
## sp_ptbBMns939 -3.704e+00 3.079e+00 -1.203 0.22910
## sp_ptbBMns940 -3.478e+00 3.099e+00 -1.122 0.26166
## sp_ptbBMns941 -3.086e+00 3.081e+00 -1.002 0.31650
## sp_ptbBMns942 -5.315e+00 3.177e+00 -1.673 0.09438 .
## sp_ptbBMns943 -3.235e+00 3.041e+00 -1.064 0.28738
## sp_ptbBMns944 -3.797e+00 3.018e+00 -1.258 0.20838
## sp_ptbBMns945 -3.935e+00 3.185e+00 -1.235 0.21670
## sp_ptbBMns946 -3.422e+00 3.294e+00 -1.039 0.29882
## sp_ptbBMns947 -2.919e+00 2.807e+00 -1.040 0.29848
## sp_ptbBMns948 -3.618e+00 2.591e+00 -1.396 0.16262
## sp_ptbBMns949 -1.998e+00 2.241e+00 -0.892 0.37265
## sp_ptbBMns950 -2.786e+00 2.212e+00 -1.259 0.20787
## sp_ptbBMns951 -1.930e+00 1.779e+00 -1.085 0.27811
## sp_ptbBMns952 -1.661e+00 1.724e+00 -0.963 0.33550
## sp_ptbBMns953 -9.372e-01 1.570e+00 -0.597 0.55051
## sp_ptbBMns954 -3.854e+00 1.591e+00 -2.422 0.01543 *
## sp_ptbBMns955 7.854e-01 1.309e+00 0.600 0.54849
## sp_ptbBMns956 -2.679e+00 1.519e+00 -1.764 0.07771 .
## sp_ptbBMns957 1.502e-01 1.406e+00 0.107 0.91490
## sp_ptbBMns958 -1.458e+00 1.645e+00 -0.886 0.37540
## sp_ptbBMns959 -5.602e-01 1.550e+00 -0.361 0.71778
## sp_ptbBMns960 -1.865e+00 1.753e+00 -1.064 0.28747
## sp_ptbBMns961 -7.584e-01 1.680e+00 -0.451 0.65177
## sp_ptbBMns962 -1.918e+00 1.566e+00 -1.225 0.22071
## sp_ptbBMns963 -1.146e+00 1.759e+00 -0.652 0.51471
## sp_ptbBMns964 -2.344e+00 1.612e+00 -1.454 0.14595
## sp_ptbBMns965 -1.402e+00 1.400e+00 -1.002 0.31654
## sp_ptbBMns966 -5.787e-01 1.397e+00 -0.414 0.67860
## sp_ptbBMns967 -1.277e+00 1.315e+00 -0.971 0.33138
## sp_ptbBMns968 -5.567e-01 1.218e+00 -0.457 0.64762
## sp_ptbBMns969 -2.973e-01 1.210e+00 -0.246 0.80592
## sp_ptbBMns970 -3.208e-01 1.088e+00 -0.295 0.76804
## sp_ptbBMns971 -1.294e+00 1.163e+00 -1.113 0.26574
## sp_ptbBMns972 -3.571e-01 1.082e+00 -0.330 0.74134
## sp_ptbBMns973 -8.442e-01 1.214e+00 -0.695 0.48694
## sp_ptbBMns974 -6.459e-01 1.071e+00 -0.603 0.54651
## sp_ptbBMns975 -8.564e-01 1.176e+00 -0.728 0.46648
## sp_ptbBMns976 -1.558e+00 1.249e+00 -1.248 0.21219
## sp_ptbBMns977 -1.325e+00 1.462e+00 -0.906 0.36485
## sp_ptbBMns978 -6.592e-01 1.357e+00 -0.486 0.62701
## sp_ptbBMns979 -1.235e+00 1.385e+00 -0.892 0.37263
## sp_ptbBMns980 -9.238e-01 1.385e+00 -0.667 0.50466
## sp_ptbBMns981 -3.684e+00 1.626e+00 -2.265 0.02352 *
## sp_ptbBMns982 -4.111e-01 1.784e+00 -0.230 0.81774
## sp_ptbBMns983 -3.021e+00 1.806e+00 -1.673 0.09438 .
## sp_ptbBMns984 -4.812e-01 1.403e+00 -0.343 0.73162
## sp_ptbBMns985 -1.463e+00 1.240e+00 -1.180 0.23815
## sp_ptbBMns986 -5.003e-01 1.143e+00 -0.438 0.66156
## sp_ptbBMns987 4.184e-01 9.426e-01 0.444 0.65711
## sp_ptbBMns988 -4.014e-01 1.019e+00 -0.394 0.69353
## sp_ptbBMns989 2.220e-01 9.963e-01 0.223 0.82369
## sp_ptbBMns990 -8.531e-01 1.180e+00 -0.723 0.46964
## sp_ptbBMns991 -6.964e-01 1.178e+00 -0.591 0.55452
## sp_ptbBMns992 -8.602e-01 1.151e+00 -0.748 0.45469
## sp_ptbBMns993 3.584e-01 1.017e+00 0.353 0.72440
## sp_ptbBMns994 -2.676e+00 1.083e+00 -2.471 0.01349 *
## sp_ptbBMns995 2.366e+00 9.048e-01 2.615 0.00893 **
## sp_ptbBMns996 -1.860e+00 9.507e-01 -1.956 0.05045 .
## sp_ptbBMns997 1.881e+00 9.300e-01 2.022 0.04315 *
## sp_ptbBMns998 -1.114e+00 8.219e-01 -1.356 0.17524
## sp_ptbBMns999 2.517e-01 9.393e-01 0.268 0.78873
## sp_ptbBMns9100 -2.771e-01 1.038e+00 -0.267 0.78948
## sp_ptbBMns9101 -2.750e-02 1.137e+00 -0.024 0.98071
## sp_ptbBMns9102 -3.673e-01 9.686e-01 -0.379 0.70456
## sp_ptbBMns9103 -6.746e-01 1.115e+00 -0.605 0.54517
## sp_ptbBMns9104 -7.127e-01 1.017e+00 -0.701 0.48353
## sp_ptbBMns9105 7.172e-01 1.017e+00 0.705 0.48077
## sp_ptbBMns9106 -9.523e-01 9.004e-01 -1.058 0.29023
## sp_ptbBMns9107 -4.663e-01 1.054e+00 -0.442 0.65828
## sp_ptbBMns9108 -3.728e-01 9.891e-01 -0.377 0.70627
## sp_ptbBMns9109 -1.501e+00 1.243e+00 -1.208 0.22710
## sp_ptbBMns9110 -1.568e+00 1.251e+00 -1.253 0.21004
## sp_ptbBMns9111 -1.147e-01 1.160e+00 -0.099 0.92130
## sp_ptbBMns9112 -1.085e-01 9.879e-01 -0.110 0.91256
## sp_ptbBMns9113 -3.742e-01 1.065e+00 -0.351 0.72524
## sp_ptbBMns9114 7.011e-01 1.102e+00 0.636 0.52450
## sp_ptbBMns9115 -1.101e+00 1.171e+00 -0.940 0.34716
## sp_ptbBMns9116 6.968e-01 1.071e+00 0.651 0.51534
## sp_ptbBMns9117 -4.735e-01 1.532e+00 -0.309 0.75729
## sp_ptbBMns9118 -6.186e-02 1.223e+00 -0.051 0.95967
## sp_ptbBMns9119 3.420e-01 1.114e+00 0.307 0.75893
## sp_ptbBMns9120 -6.475e-01 1.049e+00 -0.617 0.53720
## sp_ptbBMns9121 -4.144e-01 9.526e-01 -0.435 0.66351
## sp_ptbBMns9122 -5.444e-01 9.173e-01 -0.594 0.55282
## sp_ptbBMns9123 6.436e-01 8.834e-01 0.729 0.46629
## sp_ptbBMns9124 -1.197e-02 9.934e-01 -0.012 0.99038
## sp_ptbBMns9125 5.858e-01 9.530e-01 0.615 0.53877
## sp_ptbBMns9126 -1.957e+00 1.573e+00 -1.244 0.21346
## sp_ptbBMns9127 1.213e+00 1.035e+00 1.172 0.24111
## sp_ptbBMns9128 -8.794e-01 1.124e+00 -0.782 0.43404
## sp_ptbBMns9129 9.237e-01 9.356e-01 0.987 0.32348
## sp_ptbBMns9130 6.320e-02 8.701e-01 0.073 0.94209
## sp_ptbBMns9131 6.565e-01 8.432e-01 0.779 0.43622
## sp_ptbBMns9132 1.803e-01 8.804e-01 0.205 0.83776
## sp_ptbBMns9133 -4.621e-02 8.676e-01 -0.053 0.95753
## sp_ptbBMns9134 4.265e-01 9.194e-01 0.464 0.64272
## sp_ptbBMns9135 -6.532e-01 1.058e+00 -0.617 0.53697
## sp_ptbBMns9136 1.031e+00 8.430e-01 1.223 0.22122
## sp_ptbBMns9137 -1.808e+00 9.046e-01 -1.998 0.04568 *
## sp_ptbBMns9138 7.300e-01 7.848e-01 0.930 0.35233
## sp_ptbBMns9139 -3.154e-01 8.306e-01 -0.380 0.70415
## sp_ptbBMns9140 6.652e-01 8.004e-01 0.831 0.40595
## sp_ptbBMns9141 -1.316e-01 7.816e-01 -0.168 0.86625
## sp_ptbBMns9142 7.672e-01 7.073e-01 1.085 0.27806
## sp_ptbBMns9143 -3.382e-01 9.642e-01 -0.351 0.72575
## sp_ptbBMns9144 -4.450e-01 9.611e-01 -0.463 0.64333
## sp_ptbBMns9145 -4.496e-01 1.054e+00 -0.426 0.66981
## sp_ptbBMns9146 1.827e-01 9.166e-01 0.199 0.84206
## sp_ptbBMns9147 -3.607e-01 9.542e-01 -0.378 0.70546
## sp_ptbBMns9148 -1.187e+00 9.883e-01 -1.201 0.22977
## sp_ptbBMns9149 5.437e-01 8.922e-01 0.609 0.54228
## sp_ptbBMns9150 8.996e-01 9.702e-01 0.927 0.35379
## sp_ptbBMns9151 -1.251e+00 1.038e+00 -1.205 0.22809
## sp_ptbBMns9152 1.870e+00 1.069e+00 1.749 0.08025 .
## sp_ptbBMns9153 -1.001e+00 1.209e+00 -0.828 0.40760
## sp_ptbBMns9154 -6.780e-02 1.272e+00 -0.053 0.95748
## sp_ptbBMns9155 1.126e+00 9.882e-01 1.139 0.25469
## sp_ptbBMns9156 -8.397e-01 1.060e+00 -0.792 0.42808
## sp_ptbBMns9157 1.307e+00 8.122e-01 1.609 0.10751
## sp_ptbBMns9158 5.340e-02 8.442e-01 0.063 0.94956
## sp_ptbBMns9159 1.276e+00 6.438e-01 1.982 0.04743 *
## sp_ptbBMns9160 -9.053e-02 8.142e-01 -0.111 0.91147
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(27875.55) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 883.99 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3242.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 27876
## Std. Err.: 153212
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2916.84SA4m7a <- glm.nb(ptbBM ~ cb7.maxRH + sp_ptbBMns9,data=week); summary(SA4m7a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb7.maxRH + sp_ptbBMns9, data = week,
## init.theta = 27886.35075, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9041 -0.7561 -0.1015 0.5410 2.4180
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 6.696e+00 1.368e+01 0.489 0.6246
## cb7.maxRHv1.l1 -2.148e-01 3.364e-01 -0.639 0.5231
## cb7.maxRHv1.l2 -2.479e-01 2.386e-01 -1.039 0.2989
## cb7.maxRHv2.l1 -3.312e-01 1.075e+00 -0.308 0.7580
## cb7.maxRHv2.l2 -1.444e-01 7.948e-01 -0.182 0.8558
## cb7.maxRHv3.l1 -4.526e-02 3.117e-01 -0.145 0.8846
## cb7.maxRHv3.l2 1.268e-01 2.315e-01 0.548 0.5837
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.232e+06 1.300e+06 -1.716 0.0861 .
## sp_ptbBMns97 3.799e+01 1.605e+01 2.367 0.0179 *
## sp_ptbBMns98 -4.952e+00 2.782e+00 -1.780 0.0750 .
## sp_ptbBMns99 3.098e+00 1.466e+00 2.113 0.0346 *
## sp_ptbBMns910 -1.234e+00 1.354e+00 -0.911 0.3624
## sp_ptbBMns911 1.152e+00 1.144e+00 1.007 0.3140
## sp_ptbBMns912 -2.816e-01 1.097e+00 -0.257 0.7974
## sp_ptbBMns913 -1.465e+00 1.201e+00 -1.220 0.2224
## sp_ptbBMns914 -8.440e-01 9.661e-01 -0.874 0.3823
## sp_ptbBMns915 4.280e-01 8.195e-01 0.522 0.6015
## sp_ptbBMns916 -1.276e+00 8.011e-01 -1.592 0.1113
## sp_ptbBMns917 1.977e+00 1.054e+00 1.875 0.0608 .
## sp_ptbBMns918 -1.030e+00 9.691e-01 -1.063 0.2879
## sp_ptbBMns919 1.088e+00 8.424e-01 1.292 0.1965
## sp_ptbBMns920 -8.186e-01 8.619e-01 -0.950 0.3422
## sp_ptbBMns921 1.274e-01 8.563e-01 0.149 0.8817
## sp_ptbBMns922 -5.121e-01 8.883e-01 -0.576 0.5643
## sp_ptbBMns923 -6.505e-01 8.683e-01 -0.749 0.4538
## sp_ptbBMns924 -8.745e-01 9.172e-01 -0.953 0.3404
## sp_ptbBMns925 -7.010e-01 8.341e-01 -0.840 0.4007
## sp_ptbBMns926 2.088e-01 1.333e+00 0.157 0.8755
## sp_ptbBMns927 9.375e-01 9.136e-01 1.026 0.3048
## sp_ptbBMns928 4.964e-01 8.242e-01 0.602 0.5470
## sp_ptbBMns929 4.441e-01 9.451e-01 0.470 0.6384
## sp_ptbBMns930 -1.272e+00 9.913e-01 -1.283 0.1995
## sp_ptbBMns931 2.204e-01 8.768e-01 0.251 0.8015
## sp_ptbBMns932 -1.406e+00 1.039e+00 -1.352 0.1762
## sp_ptbBMns933 1.844e-01 1.213e+00 0.152 0.8791
## sp_ptbBMns934 -1.165e+00 1.286e+00 -0.906 0.3651
## sp_ptbBMns935 5.152e-02 1.725e+00 0.030 0.9762
## sp_ptbBMns936 -6.230e-01 1.468e+00 -0.424 0.6714
## sp_ptbBMns937 -2.158e-01 2.033e+00 -0.106 0.9155
## sp_ptbBMns938 -1.215e-01 2.576e+00 -0.047 0.9624
## sp_ptbBMns939 -1.365e+00 3.048e+00 -0.448 0.6544
## sp_ptbBMns940 -1.751e+00 3.941e+00 -0.444 0.6568
## sp_ptbBMns941 -2.580e+00 4.983e+00 -0.518 0.6047
## sp_ptbBMns942 -4.931e+00 5.816e+00 -0.848 0.3966
## sp_ptbBMns943 -3.559e+00 6.549e+00 -0.543 0.5869
## sp_ptbBMns944 -3.968e+00 6.988e+00 -0.568 0.5701
## sp_ptbBMns945 -4.288e+00 7.994e+00 -0.536 0.5917
## sp_ptbBMns946 -3.764e+00 7.793e+00 -0.483 0.6291
## sp_ptbBMns947 -3.546e+00 6.802e+00 -0.521 0.6022
## sp_ptbBMns948 -4.788e+00 6.530e+00 -0.733 0.4634
## sp_ptbBMns949 -3.229e+00 5.890e+00 -0.548 0.5835
## sp_ptbBMns950 -3.833e+00 5.171e+00 -0.741 0.4586
## sp_ptbBMns951 -2.977e+00 4.244e+00 -0.702 0.4830
## sp_ptbBMns952 -2.379e+00 3.661e+00 -0.650 0.5157
## sp_ptbBMns953 -1.276e+00 3.121e+00 -0.409 0.6826
## sp_ptbBMns954 -3.683e+00 2.601e+00 -1.416 0.1568
## sp_ptbBMns955 1.411e+00 2.211e+00 0.638 0.5232
## sp_ptbBMns956 -2.014e+00 2.479e+00 -0.812 0.4166
## sp_ptbBMns957 4.975e-01 2.569e+00 0.194 0.8465
## sp_ptbBMns958 -9.569e-01 2.779e+00 -0.344 0.7306
## sp_ptbBMns959 -3.114e-01 2.824e+00 -0.110 0.9122
## sp_ptbBMns960 -1.595e+00 3.039e+00 -0.525 0.5998
## sp_ptbBMns961 -5.046e-01 3.237e+00 -0.156 0.8761
## sp_ptbBMns962 -1.983e+00 2.974e+00 -0.667 0.5049
## sp_ptbBMns963 -1.173e+00 2.953e+00 -0.397 0.6913
## sp_ptbBMns964 -2.218e+00 2.629e+00 -0.844 0.3987
## sp_ptbBMns965 -1.598e+00 2.334e+00 -0.685 0.4935
## sp_ptbBMns966 -6.048e-01 2.204e+00 -0.274 0.7838
## sp_ptbBMns967 -1.470e+00 2.047e+00 -0.718 0.4726
## sp_ptbBMns968 -6.269e-01 1.814e+00 -0.346 0.7296
## sp_ptbBMns969 -1.950e-01 1.660e+00 -0.117 0.9065
## sp_ptbBMns970 2.434e-01 1.511e+00 0.161 0.8720
## sp_ptbBMns971 -3.012e-01 1.531e+00 -0.197 0.8441
## sp_ptbBMns972 4.900e-01 1.368e+00 0.358 0.7203
## sp_ptbBMns973 3.412e-01 1.437e+00 0.237 0.8123
## sp_ptbBMns974 4.015e-01 1.405e+00 0.286 0.7750
## sp_ptbBMns975 4.485e-01 1.423e+00 0.315 0.7526
## sp_ptbBMns976 -1.855e-01 1.554e+00 -0.119 0.9050
## sp_ptbBMns977 1.934e-01 1.733e+00 0.112 0.9111
## sp_ptbBMns978 4.792e-01 1.866e+00 0.257 0.7973
## sp_ptbBMns979 -2.711e-01 1.765e+00 -0.154 0.8780
## sp_ptbBMns980 1.253e-01 1.878e+00 0.067 0.9468
## sp_ptbBMns981 -2.568e+00 1.957e+00 -1.312 0.1895
## sp_ptbBMns982 7.778e-01 2.232e+00 0.348 0.7275
## sp_ptbBMns983 -2.056e+00 2.208e+00 -0.931 0.3518
## sp_ptbBMns984 1.589e-01 1.969e+00 0.081 0.9357
## sp_ptbBMns985 -9.751e-01 1.810e+00 -0.539 0.5901
## sp_ptbBMns986 -3.521e-02 1.674e+00 -0.021 0.9832
## sp_ptbBMns987 1.005e+00 1.436e+00 0.700 0.4842
## sp_ptbBMns988 -2.363e-02 1.466e+00 -0.016 0.9871
## sp_ptbBMns989 1.014e+00 1.305e+00 0.778 0.4368
## sp_ptbBMns990 -1.410e-01 1.347e+00 -0.105 0.9166
## sp_ptbBMns991 2.605e-01 1.346e+00 0.194 0.8465
## sp_ptbBMns992 -6.572e-02 1.293e+00 -0.051 0.9595
## sp_ptbBMns993 9.402e-01 1.262e+00 0.745 0.4563
## sp_ptbBMns994 -2.217e+00 1.294e+00 -1.713 0.0866 .
## sp_ptbBMns995 2.711e+00 1.194e+00 2.271 0.0231 *
## sp_ptbBMns996 -1.627e+00 1.291e+00 -1.260 0.2077
## sp_ptbBMns997 1.754e+00 1.198e+00 1.464 0.1431
## sp_ptbBMns998 -7.065e-01 1.216e+00 -0.581 0.5612
## sp_ptbBMns999 9.125e-01 1.204e+00 0.758 0.4485
## sp_ptbBMns9100 6.898e-01 1.117e+00 0.617 0.5369
## sp_ptbBMns9101 1.064e+00 1.260e+00 0.844 0.3985
## sp_ptbBMns9102 4.448e-01 1.104e+00 0.403 0.6870
## sp_ptbBMns9103 4.057e-01 1.186e+00 0.342 0.7322
## sp_ptbBMns9104 -2.158e-02 1.223e+00 -0.018 0.9859
## sp_ptbBMns9105 1.084e+00 1.279e+00 0.847 0.3968
## sp_ptbBMns9106 -6.696e-01 1.114e+00 -0.601 0.5476
## sp_ptbBMns9107 -2.649e-02 1.117e+00 -0.024 0.9811
## sp_ptbBMns9108 1.778e-01 1.136e+00 0.157 0.8756
## sp_ptbBMns9109 -6.104e-01 1.472e+00 -0.415 0.6784
## sp_ptbBMns9110 -6.935e-01 1.474e+00 -0.470 0.6381
## sp_ptbBMns9111 4.391e-01 1.362e+00 0.322 0.7471
## sp_ptbBMns9112 3.965e-01 1.293e+00 0.307 0.7592
## sp_ptbBMns9113 -1.610e-02 1.385e+00 -0.012 0.9907
## sp_ptbBMns9114 5.676e-01 1.335e+00 0.425 0.6708
## sp_ptbBMns9115 -9.371e-01 1.476e+00 -0.635 0.5255
## sp_ptbBMns9116 1.469e+00 1.248e+00 1.177 0.2392
## sp_ptbBMns9117 3.690e-01 1.254e+00 0.294 0.7685
## sp_ptbBMns9118 7.712e-01 1.061e+00 0.727 0.4675
## sp_ptbBMns9119 1.012e+00 1.016e+00 0.996 0.3192
## sp_ptbBMns9120 -1.242e-01 9.879e-01 -0.126 0.8999
## sp_ptbBMns9121 -3.186e-01 1.029e+00 -0.310 0.7568
## sp_ptbBMns9122 -7.057e-01 9.578e-01 -0.737 0.4612
## sp_ptbBMns9123 2.039e-01 9.519e-01 0.214 0.8304
## sp_ptbBMns9124 -7.572e-01 8.689e-01 -0.871 0.3835
## sp_ptbBMns9125 7.855e-01 8.895e-01 0.883 0.3772
## sp_ptbBMns9126 -1.313e+00 1.289e+00 -1.019 0.3081
## sp_ptbBMns9127 1.716e+00 1.021e+00 1.681 0.0928 .
## sp_ptbBMns9128 -4.157e-01 9.267e-01 -0.449 0.6538
## sp_ptbBMns9129 1.045e+00 8.293e-01 1.260 0.2077
## sp_ptbBMns9130 8.966e-02 7.737e-01 0.116 0.9078
## sp_ptbBMns9131 7.053e-01 7.154e-01 0.986 0.3242
## sp_ptbBMns9132 -3.648e-01 9.222e-01 -0.396 0.6924
## sp_ptbBMns9133 -1.207e-01 7.242e-01 -0.167 0.8676
## sp_ptbBMns9134 8.033e-01 7.615e-01 1.055 0.2915
## sp_ptbBMns9135 -6.018e-02 1.040e+00 -0.058 0.9539
## sp_ptbBMns9136 1.604e+00 9.782e-01 1.640 0.1010
## sp_ptbBMns9137 -1.467e+00 1.209e+00 -1.214 0.2247
## sp_ptbBMns9138 8.599e-01 1.139e+00 0.755 0.4502
## sp_ptbBMns9139 -6.148e-01 9.979e-01 -0.616 0.5378
## sp_ptbBMns9140 4.364e-01 1.092e+00 0.399 0.6895
## sp_ptbBMns9141 -1.110e+00 1.144e+00 -0.971 0.3315
## sp_ptbBMns9142 1.454e-01 1.099e+00 0.132 0.8948
## sp_ptbBMns9143 8.548e-02 1.412e+00 0.061 0.9517
## sp_ptbBMns9144 -5.755e-01 1.320e+00 -0.436 0.6629
## sp_ptbBMns9145 4.277e-01 1.082e+00 0.395 0.6925
## sp_ptbBMns9146 8.917e-01 8.709e-01 1.024 0.3059
## sp_ptbBMns9147 2.178e-02 9.749e-01 0.022 0.9822
## sp_ptbBMns9148 -1.125e+00 9.477e-01 -1.187 0.2352
## sp_ptbBMns9149 5.405e-01 8.171e-01 0.661 0.5083
## sp_ptbBMns9150 4.633e-01 9.968e-01 0.465 0.6421
## sp_ptbBMns9151 -1.844e+00 1.109e+00 -1.663 0.0963 .
## sp_ptbBMns9152 1.989e+00 1.249e+00 1.593 0.1112
## sp_ptbBMns9153 -1.179e+00 1.366e+00 -0.863 0.3879
## sp_ptbBMns9154 -1.572e-01 1.157e+00 -0.136 0.8919
## sp_ptbBMns9155 6.778e-01 9.785e-01 0.693 0.4884
## sp_ptbBMns9156 -9.260e-01 9.508e-01 -0.974 0.3301
## sp_ptbBMns9157 8.472e-01 8.521e-01 0.994 0.3201
## sp_ptbBMns9158 -9.384e-01 9.784e-01 -0.959 0.3375
## sp_ptbBMns9159 4.392e-01 7.925e-01 0.554 0.5795
## sp_ptbBMns9160 -6.427e-01 8.922e-01 -0.720 0.4714
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(27886.35) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 883.23 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3242.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 27886
## Std. Err.: 152640
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2916.077SA4m8a <- glm.nb(ptbBM ~ cb8.AH + sp_ptbBMns9,data=week); summary(SA4m8a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb8.AH + sp_ptbBMns9, data = week, init.theta = 28001.63863,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.99660 -0.75613 -0.08612 0.56152 2.36426
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -8.747e+00 1.519e+01 -0.576 0.5647
## cb8.AHv1.l1 9.691e-02 3.623e-01 0.267 0.7891
## cb8.AHv1.l2 -2.454e-01 2.437e-01 -1.007 0.3139
## cb8.AHv2.l1 1.030e+00 1.224e+00 0.841 0.4001
## cb8.AHv2.l2 -2.564e-01 8.148e-01 -0.315 0.7530
## cb8.AHv3.l1 7.993e-01 6.343e-01 1.260 0.2077
## cb8.AHv3.l2 5.329e-01 4.419e-01 1.206 0.2279
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.303e+06 1.301e+06 -1.771 0.0765 .
## sp_ptbBMns97 3.936e+01 1.613e+01 2.440 0.0147 *
## sp_ptbBMns98 -4.760e+00 3.019e+00 -1.577 0.1148
## sp_ptbBMns99 3.903e+00 2.032e+00 1.920 0.0548 .
## sp_ptbBMns910 -4.925e-01 2.008e+00 -0.245 0.8062
## sp_ptbBMns911 2.479e+00 1.733e+00 1.430 0.1526
## sp_ptbBMns912 1.317e+00 1.860e+00 0.708 0.4787
## sp_ptbBMns913 2.293e-01 1.765e+00 0.130 0.8966
## sp_ptbBMns914 8.642e-01 1.867e+00 0.463 0.6435
## sp_ptbBMns915 2.120e+00 1.705e+00 1.244 0.2137
## sp_ptbBMns916 4.225e-01 1.771e+00 0.239 0.8115
## sp_ptbBMns917 2.359e+00 1.487e+00 1.586 0.1127
## sp_ptbBMns918 -4.648e-01 1.591e+00 -0.292 0.7701
## sp_ptbBMns919 1.408e+00 1.660e+00 0.848 0.3962
## sp_ptbBMns920 1.956e-01 1.625e+00 0.120 0.9042
## sp_ptbBMns921 1.695e+00 1.690e+00 1.003 0.3159
## sp_ptbBMns922 6.964e-01 1.778e+00 0.392 0.6953
## sp_ptbBMns923 9.806e-01 1.649e+00 0.595 0.5521
## sp_ptbBMns924 8.461e-01 1.718e+00 0.493 0.6223
## sp_ptbBMns925 4.349e-01 1.659e+00 0.262 0.7932
## sp_ptbBMns926 4.005e-01 1.770e+00 0.226 0.8210
## sp_ptbBMns927 1.438e+00 1.945e+00 0.739 0.4599
## sp_ptbBMns928 1.335e+00 2.039e+00 0.655 0.5126
## sp_ptbBMns929 1.645e+00 2.437e+00 0.675 0.4997
## sp_ptbBMns930 1.872e+00 2.867e+00 0.653 0.5139
## sp_ptbBMns931 3.780e+00 3.384e+00 1.117 0.2640
## sp_ptbBMns932 2.924e+00 3.714e+00 0.787 0.4311
## sp_ptbBMns933 5.130e+00 4.680e+00 1.096 0.2730
## sp_ptbBMns934 2.905e+00 4.368e+00 0.665 0.5061
## sp_ptbBMns935 2.449e+00 4.781e+00 0.512 0.6085
## sp_ptbBMns936 2.220e+00 4.917e+00 0.451 0.6517
## sp_ptbBMns937 2.776e+00 4.571e+00 0.607 0.5437
## sp_ptbBMns938 2.809e+00 4.547e+00 0.618 0.5367
## sp_ptbBMns939 1.632e+00 4.479e+00 0.364 0.7157
## sp_ptbBMns940 2.513e+00 4.410e+00 0.570 0.5688
## sp_ptbBMns941 2.630e+00 4.216e+00 0.624 0.5327
## sp_ptbBMns942 1.029e+00 4.221e+00 0.244 0.8073
## sp_ptbBMns943 2.950e+00 4.273e+00 0.690 0.4900
## sp_ptbBMns944 2.953e+00 4.685e+00 0.630 0.5285
## sp_ptbBMns945 3.202e+00 4.641e+00 0.690 0.4902
## sp_ptbBMns946 3.576e+00 4.875e+00 0.734 0.4632
## sp_ptbBMns947 3.114e+00 4.368e+00 0.713 0.4759
## sp_ptbBMns948 1.389e+00 4.435e+00 0.313 0.7541
## sp_ptbBMns949 2.401e+00 3.874e+00 0.620 0.5355
## sp_ptbBMns950 1.301e+00 3.916e+00 0.332 0.7397
## sp_ptbBMns951 1.441e+00 3.553e+00 0.406 0.6851
## sp_ptbBMns952 1.378e+00 3.428e+00 0.402 0.6876
## sp_ptbBMns953 1.882e+00 2.937e+00 0.641 0.5217
## sp_ptbBMns954 -9.199e-01 2.941e+00 -0.313 0.7544
## sp_ptbBMns955 3.145e+00 2.779e+00 1.132 0.2577
## sp_ptbBMns956 -3.930e-02 2.869e+00 -0.014 0.9891
## sp_ptbBMns957 2.387e+00 2.718e+00 0.878 0.3799
## sp_ptbBMns958 1.384e+00 2.892e+00 0.479 0.6322
## sp_ptbBMns959 2.187e+00 2.997e+00 0.730 0.4655
## sp_ptbBMns960 1.387e+00 2.997e+00 0.463 0.6436
## sp_ptbBMns961 2.832e+00 3.222e+00 0.879 0.3795
## sp_ptbBMns962 1.594e+00 3.219e+00 0.495 0.6205
## sp_ptbBMns963 2.700e+00 3.236e+00 0.835 0.4040
## sp_ptbBMns964 1.587e+00 3.173e+00 0.500 0.6169
## sp_ptbBMns965 1.650e+00 3.146e+00 0.524 0.6000
## sp_ptbBMns966 2.247e+00 3.257e+00 0.690 0.4903
## sp_ptbBMns967 9.746e-01 3.276e+00 0.298 0.7661
## sp_ptbBMns968 1.603e+00 3.063e+00 0.523 0.6007
## sp_ptbBMns969 1.562e+00 3.101e+00 0.504 0.6145
## sp_ptbBMns970 1.461e+00 2.762e+00 0.529 0.5968
## sp_ptbBMns971 8.980e-01 2.734e+00 0.328 0.7426
## sp_ptbBMns972 1.672e+00 2.627e+00 0.636 0.5245
## sp_ptbBMns973 1.497e+00 2.503e+00 0.598 0.5498
## sp_ptbBMns974 1.672e+00 2.363e+00 0.707 0.4793
## sp_ptbBMns975 1.493e+00 2.284e+00 0.654 0.5134
## sp_ptbBMns976 8.605e-01 2.298e+00 0.375 0.7080
## sp_ptbBMns977 1.275e+00 2.264e+00 0.563 0.5733
## sp_ptbBMns978 1.645e+00 2.191e+00 0.751 0.4529
## sp_ptbBMns979 1.336e+00 2.209e+00 0.605 0.5454
## sp_ptbBMns980 1.912e+00 2.215e+00 0.863 0.3879
## sp_ptbBMns981 -5.765e-01 2.267e+00 -0.254 0.7993
## sp_ptbBMns982 2.977e+00 2.038e+00 1.460 0.1442
## sp_ptbBMns983 6.154e-01 1.872e+00 0.329 0.7423
## sp_ptbBMns984 2.502e+00 1.659e+00 1.508 0.1315
## sp_ptbBMns985 9.040e-01 1.431e+00 0.632 0.5275
## sp_ptbBMns986 1.345e+00 1.229e+00 1.094 0.2738
## sp_ptbBMns987 1.670e+00 1.108e+00 1.507 0.1319
## sp_ptbBMns988 5.525e-01 1.183e+00 0.467 0.6404
## sp_ptbBMns989 1.142e+00 1.253e+00 0.911 0.3624
## sp_ptbBMns990 -2.997e-01 1.486e+00 -0.202 0.8401
## sp_ptbBMns991 -4.538e-01 1.783e+00 -0.254 0.7992
## sp_ptbBMns992 -7.468e-01 1.842e+00 -0.405 0.6852
## sp_ptbBMns993 2.336e+00 2.220e+00 1.052 0.2928
## sp_ptbBMns994 -1.158e+00 2.169e+00 -0.534 0.5933
## sp_ptbBMns995 3.821e+00 2.155e+00 1.773 0.0762 .
## sp_ptbBMns996 -4.896e-01 2.231e+00 -0.219 0.8263
## sp_ptbBMns997 3.078e+00 2.070e+00 1.487 0.1370
## sp_ptbBMns998 4.780e-01 1.959e+00 0.244 0.8072
## sp_ptbBMns999 1.886e+00 1.865e+00 1.011 0.3121
## sp_ptbBMns9100 1.649e+00 1.714e+00 0.962 0.3361
## sp_ptbBMns9101 2.071e+00 1.704e+00 1.215 0.2242
## sp_ptbBMns9102 1.607e+00 1.713e+00 0.938 0.3482
## sp_ptbBMns9103 1.954e+00 1.828e+00 1.069 0.2852
## sp_ptbBMns9104 8.801e-01 1.790e+00 0.492 0.6230
## sp_ptbBMns9105 2.439e+00 1.868e+00 1.306 0.1915
## sp_ptbBMns9106 7.867e-01 1.847e+00 0.426 0.6702
## sp_ptbBMns9107 1.840e+00 1.830e+00 1.006 0.3146
## sp_ptbBMns9108 1.926e+00 1.809e+00 1.065 0.2869
## sp_ptbBMns9109 9.071e-01 1.776e+00 0.511 0.6094
## sp_ptbBMns9110 1.252e+00 1.533e+00 0.816 0.4143
## sp_ptbBMns9111 2.191e+00 1.494e+00 1.467 0.1425
## sp_ptbBMns9112 1.686e+00 1.353e+00 1.246 0.2127
## sp_ptbBMns9113 8.672e-01 1.355e+00 0.640 0.5223
## sp_ptbBMns9114 1.217e+00 1.388e+00 0.877 0.3806
## sp_ptbBMns9115 -4.629e-01 1.483e+00 -0.312 0.7549
## sp_ptbBMns9116 1.434e+00 1.231e+00 1.165 0.2439
## sp_ptbBMns9117 4.731e-01 1.431e+00 0.331 0.7410
## sp_ptbBMns9118 7.402e-01 1.502e+00 0.493 0.6222
## sp_ptbBMns9119 1.560e+00 1.440e+00 1.084 0.2785
## sp_ptbBMns9120 1.442e+00 1.492e+00 0.966 0.3339
## sp_ptbBMns9121 8.910e-01 1.425e+00 0.625 0.5319
## sp_ptbBMns9122 5.105e-01 1.247e+00 0.409 0.6822
## sp_ptbBMns9123 1.192e+00 1.133e+00 1.052 0.2929
## sp_ptbBMns9124 4.644e-01 1.022e+00 0.455 0.6494
## sp_ptbBMns9125 1.299e+00 1.103e+00 1.178 0.2390
## sp_ptbBMns9126 -1.601e+00 1.134e+00 -1.412 0.1579
## sp_ptbBMns9127 1.377e+00 1.088e+00 1.265 0.2058
## sp_ptbBMns9128 -8.014e-01 1.034e+00 -0.775 0.4382
## sp_ptbBMns9129 2.092e+00 1.153e+00 1.814 0.0697 .
## sp_ptbBMns9130 1.083e+00 9.915e-01 1.092 0.2749
## sp_ptbBMns9131 1.447e+00 9.052e-01 1.598 0.1100
## sp_ptbBMns9132 5.461e-01 8.390e-01 0.651 0.5151
## sp_ptbBMns9133 3.689e-01 7.832e-01 0.471 0.6376
## sp_ptbBMns9134 1.366e+00 8.384e-01 1.629 0.1033
## sp_ptbBMns9135 -3.114e-01 9.809e-01 -0.317 0.7509
## sp_ptbBMns9136 9.253e-01 1.001e+00 0.924 0.3555
## sp_ptbBMns9137 -2.305e+00 1.785e+00 -1.291 0.1968
## sp_ptbBMns9138 1.378e-02 2.510e+00 0.005 0.9956
## sp_ptbBMns9139 -1.654e+00 1.905e+00 -0.868 0.3852
## sp_ptbBMns9140 -1.702e+00 2.258e+00 -0.754 0.4510
## sp_ptbBMns9141 -3.324e+00 2.092e+00 -1.588 0.1122
## sp_ptbBMns9142 -3.192e+00 2.412e+00 -1.323 0.1858
## sp_ptbBMns9143 -3.687e+00 2.100e+00 -1.756 0.0791 .
## sp_ptbBMns9144 -3.368e+00 1.805e+00 -1.866 0.0620 .
## sp_ptbBMns9145 -3.170e+00 1.943e+00 -1.632 0.1028
## sp_ptbBMns9146 -1.565e+00 1.349e+00 -1.160 0.2460
## sp_ptbBMns9147 1.717e+00 1.141e+00 1.505 0.1324
## sp_ptbBMns9148 -5.137e-01 9.033e-01 -0.569 0.5696
## sp_ptbBMns9149 1.837e+00 9.945e-01 1.847 0.0647 .
## sp_ptbBMns9150 9.461e-01 7.984e-01 1.185 0.2360
## sp_ptbBMns9151 -1.159e+00 9.038e-01 -1.282 0.1997
## sp_ptbBMns9152 2.036e+00 8.063e-01 2.525 0.0116 *
## sp_ptbBMns9153 -1.305e+00 9.304e-01 -1.402 0.1609
## sp_ptbBMns9154 -7.181e-01 9.497e-01 -0.756 0.4495
## sp_ptbBMns9155 2.998e-01 8.625e-01 0.348 0.7282
## sp_ptbBMns9156 9.591e-02 1.004e+00 0.096 0.9239
## sp_ptbBMns9157 1.441e+00 6.841e-01 2.106 0.0352 *
## sp_ptbBMns9158 -4.762e-02 7.430e-01 -0.064 0.9489
## sp_ptbBMns9159 1.119e+00 6.208e-01 1.803 0.0714 .
## sp_ptbBMns9160 -4.896e-01 7.995e-01 -0.612 0.5402
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28001.64) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 881.54 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3240.4
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28002
## Std. Err.: 153309
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2914.388SA4m9a <- glm.nb(ptbBM ~ cb9.minT + sp_ptbBMns9,data=week); summary(SA4m9a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBMns9, data = week,
## init.theta = 28208.16849, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.98149 -0.77078 -0.08243 0.55014 2.45043
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.116e+00 1.206e+01 -0.507 0.61211
## cb9.minTv1.l1 1.228e-01 2.879e-01 0.427 0.66970
## cb9.minTv1.l2 -7.056e-02 2.212e-01 -0.319 0.74969
## cb9.minTv2.l1 8.990e-01 1.042e+00 0.863 0.38819
## cb9.minTv2.l2 -1.813e-01 7.883e-01 -0.230 0.81810
## cb9.minTv3.l1 7.360e-01 6.067e-01 1.213 0.22505
## cb9.minTv3.l2 6.745e-01 4.245e-01 1.589 0.11211
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.221e+06 1.297e+06 -1.712 0.08683 .
## sp_ptbBMns97 3.667e+01 1.588e+01 2.308 0.02098 *
## sp_ptbBMns98 -6.176e+00 2.604e+00 -2.372 0.01770 *
## sp_ptbBMns99 1.821e+00 1.230e+00 1.480 0.13889
## sp_ptbBMns910 -2.398e+00 1.224e+00 -1.959 0.05014 .
## sp_ptbBMns911 3.001e-01 1.087e+00 0.276 0.78239
## sp_ptbBMns912 -5.570e-01 1.164e+00 -0.479 0.63214
## sp_ptbBMns913 -1.816e+00 1.420e+00 -1.278 0.20117
## sp_ptbBMns914 -1.018e+00 1.144e+00 -0.890 0.37361
## sp_ptbBMns915 4.164e-01 9.768e-01 0.426 0.66992
## sp_ptbBMns916 -1.581e+00 1.038e+00 -1.523 0.12774
## sp_ptbBMns917 4.578e-01 9.727e-01 0.471 0.63792
## sp_ptbBMns918 -2.196e+00 1.108e+00 -1.981 0.04755 *
## sp_ptbBMns919 2.720e-01 1.029e+00 0.264 0.79147
## sp_ptbBMns920 -1.210e+00 1.064e+00 -1.137 0.25557
## sp_ptbBMns921 1.558e-01 1.298e+00 0.120 0.90444
## sp_ptbBMns922 -6.069e-01 1.109e+00 -0.547 0.58408
## sp_ptbBMns923 -4.708e-01 1.237e+00 -0.381 0.70343
## sp_ptbBMns924 -1.044e+00 1.166e+00 -0.896 0.37029
## sp_ptbBMns925 -1.491e+00 1.269e+00 -1.174 0.24020
## sp_ptbBMns926 -1.898e+00 1.161e+00 -1.635 0.10207
## sp_ptbBMns927 -7.602e-01 1.131e+00 -0.672 0.50137
## sp_ptbBMns928 -7.848e-01 9.480e-01 -0.828 0.40776
## sp_ptbBMns929 -1.132e+00 1.068e+00 -1.059 0.28942
## sp_ptbBMns930 -6.714e-01 1.079e+00 -0.622 0.53369
## sp_ptbBMns931 -6.953e-03 9.434e-01 -0.007 0.99412
## sp_ptbBMns932 -9.202e-01 8.492e-01 -1.084 0.27853
## sp_ptbBMns933 7.944e-02 8.590e-01 0.092 0.92632
## sp_ptbBMns934 -1.319e+00 9.606e-01 -1.374 0.16958
## sp_ptbBMns935 -2.182e+00 1.197e+00 -1.823 0.06826 .
## sp_ptbBMns936 -1.005e+00 1.138e+00 -0.883 0.37714
## sp_ptbBMns937 -2.450e-01 8.848e-01 -0.277 0.78184
## sp_ptbBMns938 4.370e-01 7.871e-01 0.555 0.57873
## sp_ptbBMns939 9.309e-02 9.989e-01 0.093 0.92575
## sp_ptbBMns940 4.986e-01 9.513e-01 0.524 0.60018
## sp_ptbBMns941 4.877e-01 9.803e-01 0.497 0.61884
## sp_ptbBMns942 -1.373e+00 1.242e+00 -1.105 0.26899
## sp_ptbBMns943 2.138e-01 1.114e+00 0.192 0.84784
## sp_ptbBMns944 3.524e-01 1.601e+00 0.220 0.82585
## sp_ptbBMns945 2.121e-01 1.548e+00 0.137 0.89097
## sp_ptbBMns946 1.555e+00 1.741e+00 0.894 0.37156
## sp_ptbBMns947 1.745e+00 1.877e+00 0.930 0.35257
## sp_ptbBMns948 9.474e-01 2.298e+00 0.412 0.68011
## sp_ptbBMns949 1.997e+00 2.059e+00 0.970 0.33208
## sp_ptbBMns950 7.548e-01 2.151e+00 0.351 0.72565
## sp_ptbBMns951 9.072e-01 1.965e+00 0.462 0.64435
## sp_ptbBMns952 6.559e-01 2.032e+00 0.323 0.74684
## sp_ptbBMns953 9.378e-01 1.542e+00 0.608 0.54300
## sp_ptbBMns954 -2.008e+00 1.488e+00 -1.350 0.17712
## sp_ptbBMns955 1.637e+00 1.320e+00 1.241 0.21479
## sp_ptbBMns956 -1.607e+00 1.185e+00 -1.356 0.17515
## sp_ptbBMns957 9.999e-01 8.692e-01 1.150 0.24999
## sp_ptbBMns958 -3.226e-01 8.655e-01 -0.373 0.70938
## sp_ptbBMns959 4.903e-01 9.790e-01 0.501 0.61652
## sp_ptbBMns960 -6.143e-01 8.926e-01 -0.688 0.49127
## sp_ptbBMns961 5.691e-01 9.357e-01 0.608 0.54308
## sp_ptbBMns962 -5.308e-01 1.025e+00 -0.518 0.60445
## sp_ptbBMns963 1.071e+00 9.216e-01 1.162 0.24510
## sp_ptbBMns964 1.630e-01 1.078e+00 0.151 0.87987
## sp_ptbBMns965 3.014e-01 1.059e+00 0.285 0.77594
## sp_ptbBMns966 1.496e+00 1.072e+00 1.395 0.16291
## sp_ptbBMns967 2.613e-01 1.313e+00 0.199 0.84221
## sp_ptbBMns968 8.579e-01 1.281e+00 0.670 0.50298
## sp_ptbBMns969 7.239e-01 1.331e+00 0.544 0.58656
## sp_ptbBMns970 3.697e-01 1.261e+00 0.293 0.76941
## sp_ptbBMns971 -5.093e-01 1.291e+00 -0.395 0.69318
## sp_ptbBMns972 6.460e-01 1.271e+00 0.508 0.61136
## sp_ptbBMns973 3.225e-01 1.176e+00 0.274 0.78400
## sp_ptbBMns974 5.431e-01 1.009e+00 0.538 0.59037
## sp_ptbBMns975 7.678e-01 1.073e+00 0.716 0.47420
## sp_ptbBMns976 -3.606e-01 9.639e-01 -0.374 0.70837
## sp_ptbBMns977 1.916e-02 9.017e-01 0.021 0.98304
## sp_ptbBMns978 9.441e-02 8.362e-01 0.113 0.91010
## sp_ptbBMns979 -4.815e-01 8.446e-01 -0.570 0.56864
## sp_ptbBMns980 1.305e-01 9.566e-01 0.136 0.89148
## sp_ptbBMns981 -2.499e+00 1.045e+00 -2.391 0.01681 *
## sp_ptbBMns982 1.168e+00 1.290e+00 0.905 0.36528
## sp_ptbBMns983 -1.725e+00 1.395e+00 -1.237 0.21622
## sp_ptbBMns984 1.176e+00 1.570e+00 0.749 0.45371
## sp_ptbBMns985 -9.352e-01 1.426e+00 -0.656 0.51210
## sp_ptbBMns986 -8.435e-01 1.381e+00 -0.611 0.54123
## sp_ptbBMns987 -5.982e-01 1.349e+00 -0.444 0.65740
## sp_ptbBMns988 -2.235e+00 1.428e+00 -1.564 0.11771
## sp_ptbBMns989 -1.803e+00 1.352e+00 -1.334 0.18224
## sp_ptbBMns990 -3.641e+00 1.542e+00 -2.362 0.01820 *
## sp_ptbBMns991 -1.970e+00 1.151e+00 -1.712 0.08698 .
## sp_ptbBMns992 -2.120e+00 1.081e+00 -1.961 0.04988 *
## sp_ptbBMns993 5.083e-01 9.271e-01 0.548 0.58355
## sp_ptbBMns994 -2.599e+00 1.049e+00 -2.478 0.01322 *
## sp_ptbBMns995 2.309e+00 8.449e-01 2.733 0.00627 **
## sp_ptbBMns996 -1.987e+00 9.634e-01 -2.062 0.03917 *
## sp_ptbBMns997 1.738e+00 9.027e-01 1.926 0.05413 .
## sp_ptbBMns998 -1.001e+00 9.224e-01 -1.085 0.27793
## sp_ptbBMns999 2.519e-01 8.924e-01 0.282 0.77774
## sp_ptbBMns9100 -8.762e-02 9.798e-01 -0.089 0.92875
## sp_ptbBMns9101 6.719e-02 9.709e-01 0.069 0.94483
## sp_ptbBMns9102 1.102e-01 1.077e+00 0.102 0.91847
## sp_ptbBMns9103 1.534e-01 1.004e+00 0.153 0.87856
## sp_ptbBMns9104 -7.642e-01 1.007e+00 -0.759 0.44795
## sp_ptbBMns9105 3.336e-01 1.032e+00 0.323 0.74636
## sp_ptbBMns9106 -1.223e+00 1.032e+00 -1.185 0.23594
## sp_ptbBMns9107 -5.118e-01 1.129e+00 -0.453 0.65021
## sp_ptbBMns9108 -2.886e-01 1.158e+00 -0.249 0.80315
## sp_ptbBMns9109 -1.160e+00 1.429e+00 -0.812 0.41684
## sp_ptbBMns9110 -1.641e+00 1.491e+00 -1.100 0.27130
## sp_ptbBMns9111 6.404e-01 1.515e+00 0.423 0.67247
## sp_ptbBMns9112 -6.971e-02 1.538e+00 -0.045 0.96384
## sp_ptbBMns9113 -1.308e+00 1.452e+00 -0.900 0.36791
## sp_ptbBMns9114 -1.140e+00 1.502e+00 -0.759 0.44800
## sp_ptbBMns9115 -3.139e+00 1.524e+00 -2.060 0.03942 *
## sp_ptbBMns9116 -1.629e+00 1.560e+00 -1.044 0.29646
## sp_ptbBMns9117 -2.156e+00 1.307e+00 -1.650 0.09899 .
## sp_ptbBMns9118 -1.275e+00 1.089e+00 -1.171 0.24172
## sp_ptbBMns9119 3.570e-02 1.162e+00 0.031 0.97550
## sp_ptbBMns9120 -3.056e-01 1.288e+00 -0.237 0.81240
## sp_ptbBMns9121 1.853e-01 1.365e+00 0.136 0.89206
## sp_ptbBMns9122 -8.597e-01 1.373e+00 -0.626 0.53135
## sp_ptbBMns9123 -6.787e-02 1.281e+00 -0.053 0.95774
## sp_ptbBMns9124 -1.156e+00 1.338e+00 -0.864 0.38763
## sp_ptbBMns9125 -5.926e-01 1.486e+00 -0.399 0.69015
## sp_ptbBMns9126 -3.942e+00 1.539e+00 -2.561 0.01044 *
## sp_ptbBMns9127 -1.201e+00 1.599e+00 -0.751 0.45253
## sp_ptbBMns9128 -2.153e+00 1.548e+00 -1.391 0.16416
## sp_ptbBMns9129 6.038e-01 1.552e+00 0.389 0.69734
## sp_ptbBMns9130 -1.323e-01 1.832e+00 -0.072 0.94243
## sp_ptbBMns9131 -3.575e-02 1.585e+00 -0.023 0.98201
## sp_ptbBMns9132 -1.122e+00 1.752e+00 -0.640 0.52196
## sp_ptbBMns9133 -1.742e+00 1.729e+00 -1.007 0.31387
## sp_ptbBMns9134 -1.064e+00 1.800e+00 -0.591 0.55454
## sp_ptbBMns9135 -2.712e+00 2.427e+00 -1.117 0.26386
## sp_ptbBMns9136 -1.678e+00 3.415e+00 -0.491 0.62320
## sp_ptbBMns9137 -3.883e+00 4.172e+00 -0.931 0.35197
## sp_ptbBMns9138 -1.559e+00 4.844e+00 -0.322 0.74752
## sp_ptbBMns9139 -3.329e+00 4.094e+00 -0.813 0.41604
## sp_ptbBMns9140 -3.860e+00 4.495e+00 -0.859 0.39048
## sp_ptbBMns9141 -6.182e+00 4.163e+00 -1.485 0.13755
## sp_ptbBMns9142 -6.819e+00 4.278e+00 -1.594 0.11101
## sp_ptbBMns9143 -8.999e+00 4.473e+00 -2.012 0.04425 *
## sp_ptbBMns9144 -8.630e+00 4.023e+00 -2.145 0.03193 *
## sp_ptbBMns9145 -6.349e+00 3.039e+00 -2.089 0.03667 *
## sp_ptbBMns9146 -3.794e+00 2.131e+00 -1.780 0.07505 .
## sp_ptbBMns9147 -1.382e+00 1.666e+00 -0.830 0.40667
## sp_ptbBMns9148 -2.846e+00 1.553e+00 -1.833 0.06681 .
## sp_ptbBMns9149 -2.840e-01 1.546e+00 -0.184 0.85422
## sp_ptbBMns9150 -5.188e-01 1.267e+00 -0.409 0.68220
## sp_ptbBMns9151 -2.734e+00 1.252e+00 -2.184 0.02898 *
## sp_ptbBMns9152 5.867e-01 9.581e-01 0.612 0.54027
## sp_ptbBMns9153 -2.225e+00 1.013e+00 -2.196 0.02806 *
## sp_ptbBMns9154 -1.415e+00 9.996e-01 -1.416 0.15691
## sp_ptbBMns9155 -2.294e-01 8.320e-01 -0.276 0.78274
## sp_ptbBMns9156 -8.082e-01 8.680e-01 -0.931 0.35182
## sp_ptbBMns9157 9.973e-01 6.755e-01 1.476 0.13983
## sp_ptbBMns9158 1.821e-01 8.491e-01 0.215 0.83015
## sp_ptbBMns9159 1.327e+00 6.846e-01 1.938 0.05267 .
## sp_ptbBMns9160 1.173e-01 8.374e-01 0.140 0.88858
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28208.17) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 878.09 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3236.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28208
## Std. Err.: 152517
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2910.936SA4m10a <- glm.nb(ptbBM ~ cb10.aveT + sp_ptbBMns9,data=week); summary(SA4m10a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb10.aveT + sp_ptbBMns9, data = week,
## init.theta = 28500.36361, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.99255 -0.74665 -0.08032 0.58198 2.36139
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.889e+01 1.913e+01 -2.033 0.042057 *
## cb10.aveTv1.l1 5.244e-01 3.954e-01 1.326 0.184765
## cb10.aveTv1.l2 5.368e-02 3.013e-01 0.178 0.858587
## cb10.aveTv2.l1 3.462e+00 1.520e+00 2.278 0.022728 *
## cb10.aveTv2.l2 2.650e-01 1.155e+00 0.230 0.818439
## cb10.aveTv3.l1 1.517e+00 6.074e-01 2.498 0.012485 *
## cb10.aveTv3.l2 3.278e-01 4.305e-01 0.762 0.446318
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.271e+06 1.295e+06 -1.754 0.079421 .
## sp_ptbBMns97 3.887e+01 1.588e+01 2.447 0.014394 *
## sp_ptbBMns98 -5.148e+00 2.647e+00 -1.945 0.051742 .
## sp_ptbBMns99 2.887e+00 1.457e+00 1.982 0.047478 *
## sp_ptbBMns910 -1.242e+00 1.405e+00 -0.885 0.376415
## sp_ptbBMns911 1.295e+00 1.058e+00 1.224 0.220919
## sp_ptbBMns912 1.409e-01 1.116e+00 0.126 0.899510
## sp_ptbBMns913 -8.068e-01 1.157e+00 -0.697 0.485704
## sp_ptbBMns914 2.169e-01 1.217e+00 0.178 0.858490
## sp_ptbBMns915 1.739e+00 1.028e+00 1.691 0.090867 .
## sp_ptbBMns916 -1.594e-01 1.069e+00 -0.149 0.881422
## sp_ptbBMns917 1.831e+00 9.974e-01 1.836 0.066417 .
## sp_ptbBMns918 -7.545e-01 1.168e+00 -0.646 0.518461
## sp_ptbBMns919 1.330e+00 1.230e+00 1.081 0.279555
## sp_ptbBMns920 -1.597e-01 1.133e+00 -0.141 0.887946
## sp_ptbBMns921 3.078e-01 9.899e-01 0.311 0.755879
## sp_ptbBMns922 2.972e-01 1.094e+00 0.272 0.785905
## sp_ptbBMns923 3.954e-01 1.032e+00 0.383 0.701536
## sp_ptbBMns924 6.640e-01 1.035e+00 0.642 0.521013
## sp_ptbBMns925 4.019e-01 1.085e+00 0.370 0.711125
## sp_ptbBMns926 1.962e-01 1.202e+00 0.163 0.870340
## sp_ptbBMns927 4.994e-01 1.193e+00 0.419 0.675541
## sp_ptbBMns928 2.800e-01 1.262e+00 0.222 0.824372
## sp_ptbBMns929 3.958e-01 1.120e+00 0.354 0.723696
## sp_ptbBMns930 -7.959e-02 1.200e+00 -0.066 0.947137
## sp_ptbBMns931 9.873e-01 1.216e+00 0.812 0.416921
## sp_ptbBMns932 3.931e-01 1.307e+00 0.301 0.763585
## sp_ptbBMns933 1.275e+00 1.366e+00 0.933 0.350794
## sp_ptbBMns934 -5.193e-02 1.339e+00 -0.039 0.969062
## sp_ptbBMns935 -2.892e-01 1.498e+00 -0.193 0.846877
## sp_ptbBMns936 -5.878e-01 1.557e+00 -0.377 0.705839
## sp_ptbBMns937 7.267e-01 1.403e+00 0.518 0.604496
## sp_ptbBMns938 1.050e+00 1.169e+00 0.898 0.369094
## sp_ptbBMns939 7.601e-01 1.138e+00 0.668 0.504254
## sp_ptbBMns940 1.100e+00 1.136e+00 0.968 0.332877
## sp_ptbBMns941 1.560e+00 1.110e+00 1.405 0.160008
## sp_ptbBMns942 -9.048e-01 1.280e+00 -0.707 0.479613
## sp_ptbBMns943 8.905e-01 1.210e+00 0.736 0.461702
## sp_ptbBMns944 7.236e-01 1.324e+00 0.547 0.584634
## sp_ptbBMns945 7.155e-01 1.435e+00 0.499 0.618001
## sp_ptbBMns946 8.766e-01 1.373e+00 0.639 0.523103
## sp_ptbBMns947 1.304e+00 1.331e+00 0.980 0.327033
## sp_ptbBMns948 4.976e-01 1.474e+00 0.338 0.735636
## sp_ptbBMns949 1.902e+00 1.295e+00 1.469 0.141809
## sp_ptbBMns950 7.490e-01 1.296e+00 0.578 0.563352
## sp_ptbBMns951 9.581e-01 1.461e+00 0.656 0.511979
## sp_ptbBMns952 1.290e+00 1.358e+00 0.950 0.341973
## sp_ptbBMns953 1.319e+00 1.309e+00 1.008 0.313628
## sp_ptbBMns954 -1.666e+00 1.610e+00 -1.035 0.300799
## sp_ptbBMns955 2.239e+00 1.397e+00 1.603 0.108984
## sp_ptbBMns956 -6.529e-01 1.269e+00 -0.515 0.606858
## sp_ptbBMns957 2.182e+00 1.129e+00 1.933 0.053191 .
## sp_ptbBMns958 7.955e-01 1.132e+00 0.703 0.482347
## sp_ptbBMns959 1.649e+00 1.256e+00 1.313 0.189254
## sp_ptbBMns960 4.127e-01 1.213e+00 0.340 0.733598
## sp_ptbBMns961 1.619e+00 1.310e+00 1.236 0.216471
## sp_ptbBMns962 7.419e-01 1.385e+00 0.535 0.592316
## sp_ptbBMns963 1.817e+00 1.343e+00 1.353 0.176115
## sp_ptbBMns964 1.490e+00 1.786e+00 0.834 0.404220
## sp_ptbBMns965 1.562e+00 1.538e+00 1.015 0.309968
## sp_ptbBMns966 2.739e+00 1.512e+00 1.811 0.070197 .
## sp_ptbBMns967 1.311e+00 1.538e+00 0.852 0.394275
## sp_ptbBMns968 1.810e+00 1.541e+00 1.174 0.240272
## sp_ptbBMns969 1.972e+00 1.565e+00 1.260 0.207628
## sp_ptbBMns970 1.438e+00 1.533e+00 0.938 0.348025
## sp_ptbBMns971 5.274e-01 1.521e+00 0.347 0.728786
## sp_ptbBMns972 1.938e+00 1.631e+00 1.188 0.234731
## sp_ptbBMns973 6.103e-01 1.665e+00 0.367 0.713907
## sp_ptbBMns974 1.353e+00 1.346e+00 1.005 0.314787
## sp_ptbBMns975 4.962e-01 1.195e+00 0.415 0.678048
## sp_ptbBMns976 1.268e-02 1.104e+00 0.011 0.990841
## sp_ptbBMns977 -1.426e-01 1.054e+00 -0.135 0.892348
## sp_ptbBMns978 1.601e-01 9.151e-01 0.175 0.861091
## sp_ptbBMns979 -4.825e-01 8.715e-01 -0.554 0.579831
## sp_ptbBMns980 2.957e-01 9.630e-01 0.307 0.758816
## sp_ptbBMns981 -2.550e+00 1.113e+00 -2.291 0.021980 *
## sp_ptbBMns982 5.948e-01 1.003e+00 0.593 0.553031
## sp_ptbBMns983 -1.926e+00 1.128e+00 -1.708 0.087645 .
## sp_ptbBMns984 1.147e+00 8.230e-01 1.394 0.163414
## sp_ptbBMns985 -4.233e-02 8.450e-01 -0.050 0.960042
## sp_ptbBMns986 7.922e-01 8.466e-01 0.936 0.349377
## sp_ptbBMns987 1.554e+00 8.227e-01 1.888 0.058960 .
## sp_ptbBMns988 3.881e-01 8.962e-01 0.433 0.665009
## sp_ptbBMns989 1.128e+00 1.077e+00 1.048 0.294855
## sp_ptbBMns990 -2.806e-01 1.333e+00 -0.210 0.833342
## sp_ptbBMns991 9.233e-02 1.578e+00 0.058 0.953351
## sp_ptbBMns992 -8.200e-02 1.539e+00 -0.053 0.957512
## sp_ptbBMns993 1.919e+00 1.451e+00 1.323 0.185903
## sp_ptbBMns994 -1.300e+00 1.400e+00 -0.929 0.353146
## sp_ptbBMns995 3.927e+00 1.177e+00 3.338 0.000844 ***
## sp_ptbBMns996 -4.783e-01 1.300e+00 -0.368 0.712997
## sp_ptbBMns997 3.150e+00 1.223e+00 2.576 0.009988 **
## sp_ptbBMns998 5.843e-01 1.250e+00 0.468 0.640065
## sp_ptbBMns999 1.359e+00 1.134e+00 1.198 0.230816
## sp_ptbBMns9100 7.645e-01 1.204e+00 0.635 0.525470
## sp_ptbBMns9101 1.251e+00 1.368e+00 0.914 0.360512
## sp_ptbBMns9102 5.847e-01 9.877e-01 0.592 0.553857
## sp_ptbBMns9103 8.794e-01 1.052e+00 0.836 0.403242
## sp_ptbBMns9104 1.380e-01 1.001e+00 0.138 0.890375
## sp_ptbBMns9105 1.535e+00 9.334e-01 1.644 0.100132
## sp_ptbBMns9106 -1.195e-01 1.034e+00 -0.116 0.907989
## sp_ptbBMns9107 4.692e-01 1.027e+00 0.457 0.647858
## sp_ptbBMns9108 5.461e-01 1.054e+00 0.518 0.604423
## sp_ptbBMns9109 -3.178e-01 1.191e+00 -0.267 0.789660
## sp_ptbBMns9110 -1.421e-01 1.147e+00 -0.124 0.901388
## sp_ptbBMns9111 1.626e+00 1.143e+00 1.422 0.154969
## sp_ptbBMns9112 1.047e+00 1.040e+00 1.007 0.314122
## sp_ptbBMns9113 6.614e-01 1.240e+00 0.533 0.593862
## sp_ptbBMns9114 9.460e-01 1.178e+00 0.803 0.421807
## sp_ptbBMns9115 -1.518e-01 1.321e+00 -0.115 0.908513
## sp_ptbBMns9116 6.110e-01 1.368e+00 0.447 0.655219
## sp_ptbBMns9117 1.920e+00 1.893e+00 1.014 0.310521
## sp_ptbBMns9118 1.846e+00 1.666e+00 1.108 0.267910
## sp_ptbBMns9119 2.393e+00 1.418e+00 1.688 0.091449 .
## sp_ptbBMns9120 1.098e+00 1.234e+00 0.889 0.373791
## sp_ptbBMns9121 2.025e+00 1.337e+00 1.515 0.129872
## sp_ptbBMns9122 1.321e+00 1.207e+00 1.095 0.273699
## sp_ptbBMns9123 2.919e+00 1.290e+00 2.263 0.023629 *
## sp_ptbBMns9124 1.613e+00 1.134e+00 1.422 0.155072
## sp_ptbBMns9125 3.396e+00 1.617e+00 2.100 0.035757 *
## sp_ptbBMns9126 -8.980e-01 1.333e+00 -0.674 0.500570
## sp_ptbBMns9127 1.928e+00 1.255e+00 1.536 0.124599
## sp_ptbBMns9128 -1.523e-02 1.213e+00 -0.013 0.989979
## sp_ptbBMns9129 2.401e+00 1.185e+00 2.026 0.042800 *
## sp_ptbBMns9130 7.839e-01 9.140e-01 0.858 0.391076
## sp_ptbBMns9131 1.938e+00 9.181e-01 2.111 0.034797 *
## sp_ptbBMns9132 1.035e+00 8.591e-01 1.205 0.228188
## sp_ptbBMns9133 9.508e-01 8.449e-01 1.125 0.260432
## sp_ptbBMns9134 1.297e+00 7.851e-01 1.652 0.098576 .
## sp_ptbBMns9135 -8.043e-01 8.279e-01 -0.971 0.331311
## sp_ptbBMns9136 1.636e+00 1.016e+00 1.611 0.107145
## sp_ptbBMns9137 -2.529e+00 1.115e+00 -2.268 0.023355 *
## sp_ptbBMns9138 -6.895e-01 1.406e+00 -0.491 0.623723
## sp_ptbBMns9139 -1.061e+00 1.084e+00 -0.979 0.327377
## sp_ptbBMns9140 -1.118e+00 1.402e+00 -0.797 0.425257
## sp_ptbBMns9141 -1.805e+00 1.175e+00 -1.537 0.124328
## sp_ptbBMns9142 -1.156e+00 1.241e+00 -0.931 0.351841
## sp_ptbBMns9143 -2.122e+00 1.351e+00 -1.571 0.116126
## sp_ptbBMns9144 -1.972e+00 1.222e+00 -1.614 0.106560
## sp_ptbBMns9145 -2.506e+00 1.704e+00 -1.471 0.141402
## sp_ptbBMns9146 -7.122e-01 1.198e+00 -0.595 0.552000
## sp_ptbBMns9147 1.084e+00 1.072e+00 1.011 0.311841
## sp_ptbBMns9148 -6.529e-01 1.044e+00 -0.626 0.531617
## sp_ptbBMns9149 1.851e+00 1.147e+00 1.614 0.106571
## sp_ptbBMns9150 1.656e+00 1.115e+00 1.485 0.137528
## sp_ptbBMns9151 -3.497e-01 1.180e+00 -0.296 0.766966
## sp_ptbBMns9152 2.738e+00 1.040e+00 2.632 0.008495 **
## sp_ptbBMns9153 -2.790e-01 1.224e+00 -0.228 0.819745
## sp_ptbBMns9154 4.921e-01 1.270e+00 0.388 0.698382
## sp_ptbBMns9155 1.885e+00 1.095e+00 1.722 0.085075 .
## sp_ptbBMns9156 1.256e-01 1.025e+00 0.123 0.902493
## sp_ptbBMns9157 1.960e+00 8.117e-01 2.415 0.015730 *
## sp_ptbBMns9158 3.293e-01 8.882e-01 0.371 0.710780
## sp_ptbBMns9159 1.381e+00 6.386e-01 2.162 0.030588 *
## sp_ptbBMns9160 -2.466e-02 8.037e-01 -0.031 0.975520
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28500.36) family taken to be 1)
##
## Null deviance: 1101.3 on 886 degrees of freedom
## Residual deviance: 879.1 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3237.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28500
## Std. Err.: 156423
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2911.944SA4m11a <- glm.nb(ptbBM ~ cb11.maxT + sp_ptbBMns9,data=week); summary(SA4m11a)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached##
## Call:
## glm.nb(formula = ptbBM ~ cb11.maxT + sp_ptbBMns9, data = week,
## init.theta = 28744.36734, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.98154 -0.72377 -0.09618 0.58025 2.36288
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -4.741e+01 1.962e+01 -2.417 0.01565 *
## cb11.maxTv1.l1 8.208e-01 4.422e-01 1.856 0.06342 .
## cb11.maxTv1.l2 1.779e-01 3.263e-01 0.545 0.58566
## cb11.maxTv2.l1 4.004e+00 1.528e+00 2.620 0.00879 **
## cb11.maxTv2.l2 3.753e-01 1.131e+00 0.332 0.74007
## cb11.maxTv3.l1 1.533e+00 5.799e-01 2.643 0.00821 **
## cb11.maxTv3.l2 2.700e-01 4.221e-01 0.640 0.52245
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.123e+06 1.297e+06 -1.637 0.10171
## sp_ptbBMns97 3.700e+01 1.577e+01 2.346 0.01896 *
## sp_ptbBMns98 -5.584e+00 2.615e+00 -2.135 0.03277 *
## sp_ptbBMns99 2.365e+00 1.660e+00 1.425 0.15428
## sp_ptbBMns910 -1.943e+00 1.620e+00 -1.199 0.23049
## sp_ptbBMns911 7.827e-01 1.188e+00 0.659 0.51008
## sp_ptbBMns912 -1.683e-01 1.081e+00 -0.156 0.87628
## sp_ptbBMns913 -1.186e+00 1.094e+00 -1.084 0.27845
## sp_ptbBMns914 -2.196e-01 1.045e+00 -0.210 0.83355
## sp_ptbBMns915 1.517e+00 8.332e-01 1.820 0.06874 .
## sp_ptbBMns916 -2.923e-01 8.078e-01 -0.362 0.71752
## sp_ptbBMns917 1.457e+00 7.972e-01 1.828 0.06751 .
## sp_ptbBMns918 -2.161e+00 1.207e+00 -1.791 0.07327 .
## sp_ptbBMns919 1.291e-01 1.348e+00 0.096 0.92369
## sp_ptbBMns920 -1.447e+00 1.256e+00 -1.152 0.24931
## sp_ptbBMns921 -7.857e-01 1.169e+00 -0.672 0.50171
## sp_ptbBMns922 -8.841e-01 1.124e+00 -0.787 0.43143
## sp_ptbBMns923 -4.054e-01 1.112e+00 -0.365 0.71542
## sp_ptbBMns924 -2.442e-01 1.062e+00 -0.230 0.81803
## sp_ptbBMns925 -5.734e-01 1.134e+00 -0.505 0.61324
## sp_ptbBMns926 -8.553e-01 1.141e+00 -0.750 0.45339
## sp_ptbBMns927 -1.239e-01 1.416e+00 -0.088 0.93026
## sp_ptbBMns928 -2.024e-01 1.420e+00 -0.143 0.88668
## sp_ptbBMns929 -3.416e-01 1.457e+00 -0.234 0.81468
## sp_ptbBMns930 -9.792e-01 1.193e+00 -0.821 0.41184
## sp_ptbBMns931 3.729e-02 1.155e+00 0.032 0.97425
## sp_ptbBMns932 -2.340e-01 1.044e+00 -0.224 0.82270
## sp_ptbBMns933 5.727e-01 1.100e+00 0.521 0.60258
## sp_ptbBMns934 -5.260e-01 1.075e+00 -0.489 0.62455
## sp_ptbBMns935 -6.250e-01 1.255e+00 -0.498 0.61840
## sp_ptbBMns936 -1.393e+00 1.414e+00 -0.985 0.32459
## sp_ptbBMns937 -4.295e-01 1.269e+00 -0.339 0.73496
## sp_ptbBMns938 3.258e-02 1.077e+00 0.030 0.97588
## sp_ptbBMns939 -2.653e-01 9.228e-01 -0.287 0.77375
## sp_ptbBMns940 5.811e-01 9.936e-01 0.585 0.55865
## sp_ptbBMns941 5.927e-01 8.825e-01 0.672 0.50179
## sp_ptbBMns942 -1.427e+00 1.115e+00 -1.281 0.20036
## sp_ptbBMns943 2.375e-01 9.468e-01 0.251 0.80194
## sp_ptbBMns944 -2.956e-01 1.001e+00 -0.295 0.76772
## sp_ptbBMns945 -1.073e+00 1.389e+00 -0.772 0.43992
## sp_ptbBMns946 -5.170e-01 1.376e+00 -0.376 0.70702
## sp_ptbBMns947 -1.066e-01 1.156e+00 -0.092 0.92656
## sp_ptbBMns948 -7.881e-01 1.182e+00 -0.667 0.50496
## sp_ptbBMns949 3.873e-01 9.692e-01 0.400 0.68947
## sp_ptbBMns950 -1.674e-01 9.367e-01 -0.179 0.85815
## sp_ptbBMns951 -1.508e-01 1.052e+00 -0.143 0.88595
## sp_ptbBMns952 1.827e-01 9.100e-01 0.201 0.84092
## sp_ptbBMns953 3.535e-01 1.018e+00 0.347 0.72853
## sp_ptbBMns954 -2.534e+00 1.441e+00 -1.759 0.07863 .
## sp_ptbBMns955 1.225e+00 1.338e+00 0.915 0.36004
## sp_ptbBMns956 -1.541e+00 1.128e+00 -1.366 0.17189
## sp_ptbBMns957 1.135e+00 9.918e-01 1.145 0.25229
## sp_ptbBMns958 -7.206e-03 9.944e-01 -0.007 0.99422
## sp_ptbBMns959 5.229e-01 9.296e-01 0.562 0.57379
## sp_ptbBMns960 -4.073e-01 9.766e-01 -0.417 0.67666
## sp_ptbBMns961 3.710e-01 1.023e+00 0.363 0.71677
## sp_ptbBMns962 -1.119e-01 9.840e-01 -0.114 0.90945
## sp_ptbBMns963 4.230e-01 1.130e+00 0.374 0.70813
## sp_ptbBMns964 -3.027e-01 1.608e+00 -0.188 0.85071
## sp_ptbBMns965 1.650e-01 1.123e+00 0.147 0.88314
## sp_ptbBMns966 1.259e+00 1.047e+00 1.202 0.22937
## sp_ptbBMns967 1.291e-01 9.658e-01 0.134 0.89364
## sp_ptbBMns968 1.180e+00 1.052e+00 1.122 0.26196
## sp_ptbBMns969 1.129e+00 9.672e-01 1.167 0.24308
## sp_ptbBMns970 9.378e-01 1.079e+00 0.869 0.38460
## sp_ptbBMns971 -9.640e-02 1.148e+00 -0.084 0.93305
## sp_ptbBMns972 1.125e+00 1.306e+00 0.862 0.38889
## sp_ptbBMns973 -5.143e-01 1.500e+00 -0.343 0.73167
## sp_ptbBMns974 4.624e-01 1.127e+00 0.410 0.68147
## sp_ptbBMns975 -5.315e-01 1.126e+00 -0.472 0.63675
## sp_ptbBMns976 -1.170e+00 1.156e+00 -1.012 0.31145
## sp_ptbBMns977 -1.606e+00 1.434e+00 -1.120 0.26266
## sp_ptbBMns978 -9.981e-01 1.221e+00 -0.817 0.41365
## sp_ptbBMns979 -1.752e+00 1.196e+00 -1.464 0.14308
## sp_ptbBMns980 -9.335e-01 1.175e+00 -0.795 0.42685
## sp_ptbBMns981 -4.386e+00 1.505e+00 -2.914 0.00356 **
## sp_ptbBMns982 -1.020e+00 1.514e+00 -0.674 0.50046
## sp_ptbBMns983 -3.453e+00 1.600e+00 -2.159 0.03087 *
## sp_ptbBMns984 -5.790e-01 1.121e+00 -0.517 0.60536
## sp_ptbBMns985 -1.391e+00 1.061e+00 -1.310 0.19008
## sp_ptbBMns986 -2.935e-02 9.635e-01 -0.030 0.97570
## sp_ptbBMns987 8.764e-01 9.155e-01 0.957 0.33840
## sp_ptbBMns988 2.256e-01 9.838e-01 0.229 0.81863
## sp_ptbBMns989 5.414e-01 1.089e+00 0.497 0.61924
## sp_ptbBMns990 -5.729e-01 1.478e+00 -0.388 0.69820
## sp_ptbBMns991 -1.549e-01 1.607e+00 -0.096 0.92322
## sp_ptbBMns992 -7.687e-01 1.493e+00 -0.515 0.60677
## sp_ptbBMns993 1.261e+00 1.330e+00 0.948 0.34298
## sp_ptbBMns994 -1.887e+00 1.248e+00 -1.512 0.13066
## sp_ptbBMns995 3.080e+00 9.403e-01 3.276 0.00105 **
## sp_ptbBMns996 -9.238e-01 1.004e+00 -0.920 0.35750
## sp_ptbBMns997 2.466e+00 9.016e-01 2.735 0.00624 **
## sp_ptbBMns998 1.403e-01 9.811e-01 0.143 0.88632
## sp_ptbBMns999 4.654e-01 9.826e-01 0.474 0.63579
## sp_ptbBMns9100 -3.230e-01 1.202e+00 -0.269 0.78811
## sp_ptbBMns9101 3.482e-01 1.341e+00 0.260 0.79513
## sp_ptbBMns9102 -3.046e-01 1.051e+00 -0.290 0.77189
## sp_ptbBMns9103 -2.494e-01 1.067e+00 -0.234 0.81510
## sp_ptbBMns9104 -8.009e-01 9.865e-01 -0.812 0.41685
## sp_ptbBMns9105 5.946e-01 8.930e-01 0.666 0.50550
## sp_ptbBMns9106 -1.057e+00 9.234e-01 -1.145 0.25224
## sp_ptbBMns9107 -5.149e-01 9.822e-01 -0.524 0.60014
## sp_ptbBMns9108 -4.025e-01 1.241e+00 -0.324 0.74561
## sp_ptbBMns9109 -1.574e+00 1.394e+00 -1.130 0.25857
## sp_ptbBMns9110 -1.228e+00 1.287e+00 -0.954 0.34005
## sp_ptbBMns9111 3.066e-01 9.973e-01 0.307 0.75852
## sp_ptbBMns9112 7.737e-02 9.228e-01 0.084 0.93318
## sp_ptbBMns9113 -1.791e-01 1.107e+00 -0.162 0.87147
## sp_ptbBMns9114 2.864e-01 1.041e+00 0.275 0.78327
## sp_ptbBMns9115 -6.369e-01 1.158e+00 -0.550 0.58230
## sp_ptbBMns9116 -1.309e-01 1.272e+00 -0.103 0.91806
## sp_ptbBMns9117 1.950e+00 2.113e+00 0.923 0.35592
## sp_ptbBMns9118 1.919e+00 2.094e+00 0.916 0.35943
## sp_ptbBMns9119 2.545e+00 1.783e+00 1.428 0.15334
## sp_ptbBMns9120 1.780e+00 1.657e+00 1.074 0.28270
## sp_ptbBMns9121 2.468e+00 1.496e+00 1.650 0.09902 .
## sp_ptbBMns9122 1.942e+00 1.438e+00 1.350 0.17695
## sp_ptbBMns9123 3.735e+00 1.554e+00 2.403 0.01627 *
## sp_ptbBMns9124 2.342e+00 1.436e+00 1.631 0.10292
## sp_ptbBMns9125 4.463e+00 1.964e+00 2.273 0.02304 *
## sp_ptbBMns9126 -2.074e-01 1.645e+00 -0.126 0.89966
## sp_ptbBMns9127 2.175e+00 1.788e+00 1.216 0.22398
## sp_ptbBMns9128 6.745e-01 1.455e+00 0.464 0.64299
## sp_ptbBMns9129 2.382e+00 1.280e+00 1.861 0.06270 .
## sp_ptbBMns9130 1.838e+00 1.136e+00 1.618 0.10556
## sp_ptbBMns9131 2.664e+00 1.129e+00 2.361 0.01825 *
## sp_ptbBMns9132 2.139e+00 1.097e+00 1.951 0.05111 .
## sp_ptbBMns9133 1.805e+00 1.021e+00 1.767 0.07723 .
## sp_ptbBMns9134 2.596e+00 1.114e+00 2.330 0.01978 *
## sp_ptbBMns9135 -4.862e-01 9.535e-01 -0.510 0.61014
## sp_ptbBMns9136 1.150e+00 1.348e+00 0.853 0.39347
## sp_ptbBMns9137 -1.897e+00 1.141e+00 -1.662 0.09651 .
## sp_ptbBMns9138 5.237e-01 9.476e-01 0.553 0.58054
## sp_ptbBMns9139 -7.132e-01 9.609e-01 -0.742 0.45795
## sp_ptbBMns9140 -2.289e-01 1.072e+00 -0.213 0.83095
## sp_ptbBMns9141 -9.342e-01 8.720e-01 -1.071 0.28401
## sp_ptbBMns9142 1.699e-01 8.460e-01 0.201 0.84084
## sp_ptbBMns9143 -1.127e+00 9.143e-01 -1.232 0.21783
## sp_ptbBMns9144 -1.326e+00 9.767e-01 -1.358 0.17454
## sp_ptbBMns9145 -1.369e+00 1.388e+00 -0.986 0.32417
## sp_ptbBMns9146 -5.664e-01 1.203e+00 -0.471 0.63764
## sp_ptbBMns9147 -2.003e-01 1.050e+00 -0.191 0.84874
## sp_ptbBMns9148 -1.413e+00 1.120e+00 -1.261 0.20714
## sp_ptbBMns9149 6.642e-01 1.023e+00 0.649 0.51637
## sp_ptbBMns9150 7.631e-01 1.137e+00 0.671 0.50226
## sp_ptbBMns9151 -1.388e+00 1.142e+00 -1.215 0.22432
## sp_ptbBMns9152 1.851e+00 1.020e+00 1.814 0.06960 .
## sp_ptbBMns9153 -8.127e-01 1.249e+00 -0.651 0.51513
## sp_ptbBMns9154 -2.238e-01 1.450e+00 -0.154 0.87735
## sp_ptbBMns9155 1.094e+00 1.258e+00 0.870 0.38456
## sp_ptbBMns9156 -5.989e-01 1.103e+00 -0.543 0.58700
## sp_ptbBMns9157 1.436e+00 9.340e-01 1.537 0.12427
## sp_ptbBMns9158 -2.205e-01 9.078e-01 -0.243 0.80807
## sp_ptbBMns9159 1.126e+00 6.600e-01 1.706 0.08798 .
## sp_ptbBMns9160 -9.001e-02 8.106e-01 -0.111 0.91158
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28744.37) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 877.91 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3236.8
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28744
## Std. Err.: 157749
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2910.759##to check model diag for univariate models
options(na.action="na.exclude")
library(dplyr) ##make sure lags are dplyr lags
##for SA4m1a avgWindSp ######
scatter.smooth(predict(SA4m1a, type='response'), rstandard(SA4m1a, type='deviance'), col='gray')
SA4m1a.resid<-residuals(SA4m1a, type="deviance")
SA4m1a.pred<-predict(SA4m1a, type="response")
length(SA4m1a.resid); length(SA4m1a.pred)## [1] 939## [1] 939pacf(SA4m1a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-8,10,13,14,18,25
library(dplyr)
#ensure that the lags are dplyr lags
SA4m1a.ac<-update(SA4m1a,.~.+lag(SA4m1a.resid,1)+lag(SA4m1a.resid,2)+lag(SA4m1a.resid,3)+ lag(SA4m1a.resid,4)+
lag(SA4m1a.resid,5)+lag(SA4m1a.resid,6)+lag(SA4m1a.resid,7)+ lag(SA4m1a.resid,8)+
lag(SA4m1a.resid,10)+lag(SA4m1a.resid,13)+lag(SA4m1a.resid,14)+ lag(SA4m1a.resid,18)+
lag(SA4m1a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA4m1a.resid_ac<-residuals(SA4m1a.ac, type="deviance")
SA4m1a.pred_ac<-predict(SA4m1a.ac, type="response")
pacf(SA4m1a.resid_ac,na.action = na.omit) 
length(SA4m1a.pred_ac)## [1] 939length(SA4m1a.resid_ac)## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m1a.pred,lwd=1, col="blue")
plot(week$time,SA4m1a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m1a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m1a.pred_ac,lwd=1, col="blue")
plot(week$time,SA4m1a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m1a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA4m1a <- crosspred(cb1.avgWindSp, SA4m1a.ac, cen = 4.5, by=0.1,cumul=TRUE)
##for SA4m2a sun ######
summary(SA4m2a)##
## Call:
## glm.nb(formula = ptbBM ~ cb2.sun + sp_ptbBMns9, data = week,
## init.theta = 28424.25298, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.9828 -0.7391 -0.0994 0.5556 2.3919
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -1.125e+01 1.038e+01 -1.084 0.27835
## cb2.sunv1.l1 2.434e-01 2.307e-01 1.055 0.29129
## cb2.sunv1.l2 1.060e-01 1.564e-01 0.678 0.49795
## cb2.sunv2.l1 1.031e+00 9.082e-01 1.135 0.25622
## cb2.sunv2.l2 1.159e+00 6.101e-01 1.899 0.05755 .
## cb2.sunv3.l1 4.292e-01 3.567e-01 1.203 0.22887
## cb2.sunv3.l2 5.088e-01 2.453e-01 2.074 0.03806 *
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.239e+06 1.296e+06 -1.728 0.08400 .
## sp_ptbBMns97 3.635e+01 1.582e+01 2.298 0.02158 *
## sp_ptbBMns98 -6.689e+00 2.596e+00 -2.576 0.00999 **
## sp_ptbBMns99 1.756e+00 1.136e+00 1.545 0.12241
## sp_ptbBMns910 -2.399e+00 1.078e+00 -2.227 0.02597 *
## sp_ptbBMns911 1.254e-01 8.729e-01 0.144 0.88578
## sp_ptbBMns912 -1.196e+00 9.339e-01 -1.281 0.20016
## sp_ptbBMns913 -2.273e+00 1.130e+00 -2.011 0.04435 *
## sp_ptbBMns914 -1.407e+00 9.607e-01 -1.465 0.14297
## sp_ptbBMns915 2.076e-01 8.460e-01 0.245 0.80615
## sp_ptbBMns916 -2.006e+00 9.510e-01 -2.110 0.03488 *
## sp_ptbBMns917 2.165e-01 9.396e-01 0.230 0.81774
## sp_ptbBMns918 -2.726e+00 1.028e+00 -2.651 0.00803 **
## sp_ptbBMns919 -5.791e-01 9.760e-01 -0.593 0.55294
## sp_ptbBMns920 -1.928e+00 9.922e-01 -1.943 0.05201 .
## sp_ptbBMns921 -1.184e+00 9.633e-01 -1.229 0.21910
## sp_ptbBMns922 -1.508e+00 9.485e-01 -1.590 0.11194
## sp_ptbBMns923 -1.469e+00 9.723e-01 -1.511 0.13073
## sp_ptbBMns924 -1.260e+00 9.694e-01 -1.299 0.19381
## sp_ptbBMns925 -1.499e+00 9.466e-01 -1.583 0.11340
## sp_ptbBMns926 -1.340e+00 9.483e-01 -1.413 0.15771
## sp_ptbBMns927 -7.217e-01 9.184e-01 -0.786 0.43196
## sp_ptbBMns928 -9.442e-01 9.482e-01 -0.996 0.31934
## sp_ptbBMns929 -4.435e-01 8.934e-01 -0.496 0.61960
## sp_ptbBMns930 -1.686e+00 1.047e+00 -1.611 0.10727
## sp_ptbBMns931 -3.652e-01 9.047e-01 -0.404 0.68644
## sp_ptbBMns932 -1.260e+00 9.404e-01 -1.340 0.18017
## sp_ptbBMns933 -3.668e-01 9.655e-01 -0.380 0.70400
## sp_ptbBMns934 -1.814e+00 1.111e+00 -1.633 0.10238
## sp_ptbBMns935 -2.090e+00 1.213e+00 -1.724 0.08475 .
## sp_ptbBMns936 -2.465e+00 1.179e+00 -2.090 0.03663 *
## sp_ptbBMns937 -1.588e+00 1.036e+00 -1.534 0.12510
## sp_ptbBMns938 -1.359e+00 1.014e+00 -1.341 0.18008
## sp_ptbBMns939 -1.309e+00 9.918e-01 -1.320 0.18678
## sp_ptbBMns940 -1.325e+00 9.657e-01 -1.372 0.16997
## sp_ptbBMns941 -9.250e-01 1.022e+00 -0.905 0.36532
## sp_ptbBMns942 -3.238e+00 1.159e+00 -2.795 0.00520 **
## sp_ptbBMns943 -8.410e-01 1.065e+00 -0.790 0.42962
## sp_ptbBMns944 -1.474e+00 1.067e+00 -1.382 0.16710
## sp_ptbBMns945 -8.655e-01 1.046e+00 -0.828 0.40787
## sp_ptbBMns946 -4.608e-01 1.069e+00 -0.431 0.66646
## sp_ptbBMns947 1.300e-01 9.917e-01 0.131 0.89569
## sp_ptbBMns948 -1.161e+00 1.034e+00 -1.122 0.26180
## sp_ptbBMns949 4.275e-01 1.015e+00 0.421 0.67367
## sp_ptbBMns950 -5.964e-01 9.662e-01 -0.617 0.53705
## sp_ptbBMns951 -2.767e-01 1.121e+00 -0.247 0.80496
## sp_ptbBMns952 3.809e-02 1.021e+00 0.037 0.97023
## sp_ptbBMns953 3.482e-02 9.616e-01 0.036 0.97111
## sp_ptbBMns954 -2.932e+00 1.282e+00 -2.288 0.02215 *
## sp_ptbBMns955 9.048e-01 9.086e-01 0.996 0.31933
## sp_ptbBMns956 -2.077e+00 9.882e-01 -2.102 0.03555 *
## sp_ptbBMns957 5.147e-01 7.965e-01 0.646 0.51816
## sp_ptbBMns958 -7.988e-01 7.988e-01 -1.000 0.31729
## sp_ptbBMns959 -1.440e-01 8.403e-01 -0.171 0.86390
## sp_ptbBMns960 -1.660e+00 9.099e-01 -1.825 0.06807 .
## sp_ptbBMns961 -5.358e-01 9.192e-01 -0.583 0.55996
## sp_ptbBMns962 -7.944e-01 8.590e-01 -0.925 0.35511
## sp_ptbBMns963 -1.070e+00 1.033e+00 -1.036 0.30005
## sp_ptbBMns964 -1.438e+00 1.015e+00 -1.416 0.15665
## sp_ptbBMns965 -9.726e-01 8.519e-01 -1.142 0.25362
## sp_ptbBMns966 -1.323e-02 8.888e-01 -0.015 0.98812
## sp_ptbBMns967 -1.059e+00 8.651e-01 -1.224 0.22094
## sp_ptbBMns968 -1.670e-01 8.180e-01 -0.204 0.83821
## sp_ptbBMns969 -4.125e-01 8.371e-01 -0.493 0.62215
## sp_ptbBMns970 -6.337e-01 8.189e-01 -0.774 0.43896
## sp_ptbBMns971 -1.243e+00 9.257e-01 -1.342 0.17948
## sp_ptbBMns972 -5.112e-01 9.588e-01 -0.533 0.59389
## sp_ptbBMns973 -1.258e+00 1.061e+00 -1.185 0.23608
## sp_ptbBMns974 -6.282e-01 9.161e-01 -0.686 0.49290
## sp_ptbBMns975 -8.092e-01 9.000e-01 -0.899 0.36857
## sp_ptbBMns976 -1.142e+00 9.394e-01 -1.216 0.22415
## sp_ptbBMns977 -6.875e-01 9.328e-01 -0.737 0.46109
## sp_ptbBMns978 -6.369e-01 8.952e-01 -0.712 0.47676
## sp_ptbBMns979 -7.283e-01 9.670e-01 -0.753 0.45136
## sp_ptbBMns980 -4.043e-01 9.878e-01 -0.409 0.68232
## sp_ptbBMns981 -3.477e+00 1.116e+00 -3.116 0.00183 **
## sp_ptbBMns982 -1.247e-01 1.106e+00 -0.113 0.91019
## sp_ptbBMns983 -2.341e+00 9.438e-01 -2.480 0.01314 *
## sp_ptbBMns984 -2.473e-01 8.781e-01 -0.282 0.77823
## sp_ptbBMns985 -1.748e+00 8.917e-01 -1.960 0.04996 *
## sp_ptbBMns986 -8.484e-01 8.022e-01 -1.058 0.29024
## sp_ptbBMns987 -3.977e-01 8.184e-01 -0.486 0.62705
## sp_ptbBMns988 -1.223e+00 8.762e-01 -1.395 0.16293
## sp_ptbBMns989 -1.643e-01 9.096e-01 -0.181 0.85670
## sp_ptbBMns990 -2.234e+00 1.157e+00 -1.931 0.05353 .
## sp_ptbBMns991 -6.531e-01 1.155e+00 -0.565 0.57193
## sp_ptbBMns992 -1.426e+00 1.120e+00 -1.274 0.20261
## sp_ptbBMns993 6.754e-04 1.098e+00 0.001 0.99951
## sp_ptbBMns994 -3.031e+00 1.201e+00 -2.524 0.01159 *
## sp_ptbBMns995 1.553e+00 9.898e-01 1.569 0.11654
## sp_ptbBMns996 -2.400e+00 1.145e+00 -2.096 0.03609 *
## sp_ptbBMns997 1.102e+00 8.923e-01 1.235 0.21694
## sp_ptbBMns998 -1.167e+00 1.087e+00 -1.074 0.28291
## sp_ptbBMns999 -8.227e-02 9.847e-01 -0.084 0.93342
## sp_ptbBMns9100 -5.286e-01 9.792e-01 -0.540 0.58929
## sp_ptbBMns9101 -2.418e-01 9.222e-01 -0.262 0.79319
## sp_ptbBMns9102 -6.337e-01 9.022e-01 -0.702 0.48245
## sp_ptbBMns9103 -4.900e-01 9.647e-01 -0.508 0.61150
## sp_ptbBMns9104 -1.566e+00 1.073e+00 -1.460 0.14440
## sp_ptbBMns9105 -2.984e-01 9.225e-01 -0.323 0.74632
## sp_ptbBMns9106 -1.601e+00 9.242e-01 -1.732 0.08329 .
## sp_ptbBMns9107 -1.122e+00 9.275e-01 -1.210 0.22635
## sp_ptbBMns9108 -1.060e+00 9.221e-01 -1.150 0.25026
## sp_ptbBMns9109 -2.078e+00 1.029e+00 -2.019 0.04353 *
## sp_ptbBMns9110 -1.924e+00 1.036e+00 -1.858 0.06323 .
## sp_ptbBMns9111 -9.111e-01 9.746e-01 -0.935 0.34985
## sp_ptbBMns9112 -6.603e-01 9.356e-01 -0.706 0.48031
## sp_ptbBMns9113 -1.141e+00 9.790e-01 -1.166 0.24371
## sp_ptbBMns9114 -6.295e-01 1.068e+00 -0.589 0.55565
## sp_ptbBMns9115 -2.257e+00 1.150e+00 -1.963 0.04961 *
## sp_ptbBMns9116 8.388e-02 1.106e+00 0.076 0.93956
## sp_ptbBMns9117 -1.634e+00 1.183e+00 -1.382 0.16702
## sp_ptbBMns9118 -5.094e-01 9.866e-01 -0.516 0.60562
## sp_ptbBMns9119 -2.716e-01 9.377e-01 -0.290 0.77204
## sp_ptbBMns9120 -7.155e-01 9.668e-01 -0.740 0.45928
## sp_ptbBMns9121 -9.457e-01 9.888e-01 -0.956 0.33886
## sp_ptbBMns9122 -9.169e-01 9.541e-01 -0.961 0.33652
## sp_ptbBMns9123 3.078e-01 8.610e-01 0.357 0.72074
## sp_ptbBMns9124 -2.439e-01 8.824e-01 -0.276 0.78225
## sp_ptbBMns9125 1.124e+00 8.879e-01 1.266 0.20542
## sp_ptbBMns9126 -2.761e+00 1.251e+00 -2.208 0.02728 *
## sp_ptbBMns9127 9.327e-01 9.325e-01 1.000 0.31723
## sp_ptbBMns9128 -1.399e+00 9.608e-01 -1.456 0.14545
## sp_ptbBMns9129 2.051e-01 8.646e-01 0.237 0.81245
## sp_ptbBMns9130 -7.154e-01 8.332e-01 -0.859 0.39052
## sp_ptbBMns9131 -1.643e-01 8.124e-01 -0.202 0.83974
## sp_ptbBMns9132 -8.468e-01 8.552e-01 -0.990 0.32205
## sp_ptbBMns9133 -9.074e-01 8.403e-01 -1.080 0.28023
## sp_ptbBMns9134 -1.541e-01 8.236e-01 -0.187 0.85160
## sp_ptbBMns9135 -1.666e+00 1.140e+00 -1.460 0.14418
## sp_ptbBMns9136 1.136e-01 1.009e+00 0.113 0.91036
## sp_ptbBMns9137 -2.555e+00 1.060e+00 -2.411 0.01593 *
## sp_ptbBMns9138 -8.608e-02 1.014e+00 -0.085 0.93237
## sp_ptbBMns9139 -2.040e+00 1.126e+00 -1.813 0.06991 .
## sp_ptbBMns9140 -1.018e+00 1.016e+00 -1.002 0.31638
## sp_ptbBMns9141 -2.089e+00 1.136e+00 -1.839 0.06592 .
## sp_ptbBMns9142 -8.530e-01 1.143e+00 -0.746 0.45558
## sp_ptbBMns9143 -1.798e+00 1.098e+00 -1.636 0.10174
## sp_ptbBMns9144 -9.280e-01 9.054e-01 -1.025 0.30540
## sp_ptbBMns9145 -3.655e-01 8.980e-01 -0.407 0.68404
## sp_ptbBMns9146 2.468e-01 8.397e-01 0.294 0.76886
## sp_ptbBMns9147 2.747e-01 8.572e-01 0.320 0.74859
## sp_ptbBMns9148 -9.975e-01 9.537e-01 -1.046 0.29559
## sp_ptbBMns9149 4.079e-01 8.253e-01 0.494 0.62114
## sp_ptbBMns9150 5.353e-01 8.135e-01 0.658 0.51055
## sp_ptbBMns9151 -2.074e+00 9.954e-01 -2.084 0.03720 *
## sp_ptbBMns9152 1.112e+00 9.127e-01 1.218 0.22313
## sp_ptbBMns9153 -1.555e+00 1.059e+00 -1.467 0.14224
## sp_ptbBMns9154 -1.293e+00 1.098e+00 -1.177 0.23900
## sp_ptbBMns9155 4.692e-01 8.283e-01 0.566 0.57106
## sp_ptbBMns9156 -1.261e+00 9.526e-01 -1.324 0.18545
## sp_ptbBMns9157 9.371e-01 7.257e-01 1.291 0.19659
## sp_ptbBMns9158 1.106e-01 8.014e-01 0.138 0.89025
## sp_ptbBMns9159 1.323e+00 6.384e-01 2.072 0.03825 *
## sp_ptbBMns9160 3.149e-01 8.500e-01 0.371 0.71100
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28424.25) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 880.04 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3238.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28424
## Std. Err.: 155485
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2912.888scatter.smooth(predict(SA4m2a, type='response'), rstandard(SA4m2a, type='deviance'), col='gray')
SA4m2a.resid<-residuals(SA4m2a, type="deviance")
SA4m2a.pred<-predict(SA4m2a, type="response")
length(SA4m2a.resid); length(SA4m2a.pred)## [1] 939## [1] 939pacf(SA4m2a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-8,10,13,14,18,25
SA4m2a.ac<-update(SA4m2a,.~.+lag(SA4m2a.resid,1)+lag(SA4m2a.resid,2)+lag(SA4m2a.resid,3)+lag(SA4m2a.resid,4)+
lag(SA4m2a.resid,5)+lag(SA4m2a.resid,6)+lag(SA4m2a.resid,7)+lag(SA4m2a.resid,8)+
lag(SA4m2a.resid,10)+lag(SA4m2a.resid,13)+lag(SA4m2a.resid,14)+lag(SA4m2a.resid,18)+
lag(SA4m2a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA4m2a.resid_ac<-residuals(SA4m2a.ac, type="deviance")
SA4m2a.pred_ac<-predict(SA4m2a.ac, type="response")
pacf(SA4m2a.resid_ac,na.action = na.omit) 
length(SA4m2a.pred_ac); length(SA4m2a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m2a.pred,lwd=1, col="blue")
plot(week$time,SA4m2a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m2a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m2a.pred_ac,lwd=1, col="blue")
plot(week$time,SA4m2a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m2a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA4m2a <- crosspred(cb2.sun, SA4m2a.ac, cen = 50.7, by=0.1,cumul=TRUE)
##for SA4m3a RF ######
summary(SA4m3a)##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + sp_ptbBMns9, data = week, init.theta = 27894.8583,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -3.02133 -0.74382 -0.09557 0.54575 2.35117
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.948e+00 2.934e+00 1.005 0.31501
## cb3.RFv1.l1 9.686e-02 2.864e-01 0.338 0.73520
## cb3.RFv1.l2 1.589e-01 2.070e-01 0.768 0.44264
## cb3.RFv2.l1 -3.992e-01 4.456e-01 -0.896 0.37035
## cb3.RFv2.l2 -2.651e-01 3.376e-01 -0.785 0.43230
## cb3.RFv3.l1 -5.042e-01 7.143e-01 -0.706 0.48025
## cb3.RFv3.l2 -6.828e-01 5.734e-01 -1.191 0.23372
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.357e+06 1.300e+06 -1.813 0.06986 .
## sp_ptbBMns97 3.787e+01 1.592e+01 2.379 0.01735 *
## sp_ptbBMns98 -5.758e+00 2.589e+00 -2.224 0.02612 *
## sp_ptbBMns99 1.847e+00 1.182e+00 1.563 0.11813
## sp_ptbBMns910 -2.293e+00 1.148e+00 -1.997 0.04577 *
## sp_ptbBMns911 2.182e-01 9.607e-01 0.227 0.82028
## sp_ptbBMns912 -8.498e-01 1.008e+00 -0.843 0.39925
## sp_ptbBMns913 -1.834e+00 1.213e+00 -1.512 0.13065
## sp_ptbBMns914 -1.324e+00 1.002e+00 -1.321 0.18642
## sp_ptbBMns915 5.348e-01 8.999e-01 0.594 0.55232
## sp_ptbBMns916 -1.118e+00 9.688e-01 -1.154 0.24850
## sp_ptbBMns917 1.250e+00 9.787e-01 1.277 0.20157
## sp_ptbBMns918 -1.828e+00 9.907e-01 -1.846 0.06495 .
## sp_ptbBMns919 3.541e-01 9.572e-01 0.370 0.71143
## sp_ptbBMns920 -1.023e+00 9.529e-01 -1.073 0.28305
## sp_ptbBMns921 -4.079e-01 1.050e+00 -0.388 0.69766
## sp_ptbBMns922 -5.090e-01 9.598e-01 -0.530 0.59591
## sp_ptbBMns923 -4.322e-01 1.039e+00 -0.416 0.67745
## sp_ptbBMns924 -4.027e-01 9.151e-01 -0.440 0.65991
## sp_ptbBMns925 -3.799e-01 1.026e+00 -0.370 0.71120
## sp_ptbBMns926 -5.069e-01 1.107e+00 -0.458 0.64711
## sp_ptbBMns927 2.938e-01 9.782e-01 0.300 0.76390
## sp_ptbBMns928 -5.779e-02 9.901e-01 -0.058 0.95346
## sp_ptbBMns929 1.678e-01 9.555e-01 0.176 0.86062
## sp_ptbBMns930 -1.052e+00 1.036e+00 -1.015 0.31019
## sp_ptbBMns931 2.377e-01 9.884e-01 0.240 0.80996
## sp_ptbBMns932 -5.926e-01 1.025e+00 -0.578 0.56332
## sp_ptbBMns933 5.785e-01 9.896e-01 0.585 0.55887
## sp_ptbBMns934 -6.121e-01 1.136e+00 -0.539 0.59004
## sp_ptbBMns935 -1.031e+00 1.270e+00 -0.812 0.41688
## sp_ptbBMns936 -1.505e+00 1.227e+00 -1.226 0.22016
## sp_ptbBMns937 -4.289e-01 1.073e+00 -0.400 0.68949
## sp_ptbBMns938 -1.691e-01 1.015e+00 -0.167 0.86762
## sp_ptbBMns939 -5.811e-01 9.827e-01 -0.591 0.55426
## sp_ptbBMns940 -1.795e-01 8.988e-01 -0.200 0.84174
## sp_ptbBMns941 1.227e-01 8.850e-01 0.139 0.88969
## sp_ptbBMns942 -2.054e+00 1.144e+00 -1.796 0.07251 .
## sp_ptbBMns943 -4.974e-02 1.014e+00 -0.049 0.96088
## sp_ptbBMns944 -4.024e-01 1.009e+00 -0.399 0.69004
## sp_ptbBMns945 -2.114e-01 9.694e-01 -0.218 0.82735
## sp_ptbBMns946 1.148e-01 7.885e-01 0.146 0.88421
## sp_ptbBMns947 4.545e-01 8.474e-01 0.536 0.59173
## sp_ptbBMns948 -7.332e-01 9.364e-01 -0.783 0.43363
## sp_ptbBMns949 2.993e-01 8.674e-01 0.345 0.73005
## sp_ptbBMns950 -6.380e-01 8.704e-01 -0.733 0.46356
## sp_ptbBMns951 -2.096e-01 9.113e-01 -0.230 0.81811
## sp_ptbBMns952 -3.174e-01 8.653e-01 -0.367 0.71372
## sp_ptbBMns953 3.667e-01 9.442e-01 0.388 0.69770
## sp_ptbBMns954 -2.666e+00 1.211e+00 -2.202 0.02769 *
## sp_ptbBMns955 1.030e+00 9.031e-01 1.141 0.25399
## sp_ptbBMns956 -1.992e+00 1.029e+00 -1.936 0.05289 .
## sp_ptbBMns957 9.172e-01 8.668e-01 1.058 0.29001
## sp_ptbBMns958 -3.320e-01 9.339e-01 -0.356 0.72221
## sp_ptbBMns959 7.954e-02 9.261e-01 0.086 0.93156
## sp_ptbBMns960 -6.055e-01 9.197e-01 -0.658 0.51033
## sp_ptbBMns961 5.414e-01 8.351e-01 0.648 0.51680
## sp_ptbBMns962 -3.832e-01 9.052e-01 -0.423 0.67209
## sp_ptbBMns963 6.460e-01 8.941e-01 0.723 0.46994
## sp_ptbBMns964 -3.667e-01 9.555e-01 -0.384 0.70112
## sp_ptbBMns965 -1.644e-02 9.169e-01 -0.018 0.98570
## sp_ptbBMns966 1.025e+00 8.827e-01 1.161 0.24573
## sp_ptbBMns967 -1.347e-01 9.190e-01 -0.147 0.88350
## sp_ptbBMns968 8.422e-02 9.397e-01 0.090 0.92859
## sp_ptbBMns969 2.055e-01 8.579e-01 0.239 0.81072
## sp_ptbBMns970 7.969e-03 1.026e+00 0.008 0.99381
## sp_ptbBMns971 -3.617e-01 9.740e-01 -0.371 0.71035
## sp_ptbBMns972 -1.677e-01 1.481e+00 -0.113 0.90984
## sp_ptbBMns973 -2.000e-01 1.111e+00 -0.180 0.85711
## sp_ptbBMns974 -2.422e-02 1.150e+00 -0.021 0.98319
## sp_ptbBMns975 -2.304e-01 1.029e+00 -0.224 0.82288
## sp_ptbBMns976 -5.481e-01 1.090e+00 -0.503 0.61508
## sp_ptbBMns977 -3.042e-01 1.057e+00 -0.288 0.77353
## sp_ptbBMns978 4.897e-01 1.083e+00 0.452 0.65112
## sp_ptbBMns979 -9.123e-02 9.662e-01 -0.094 0.92478
## sp_ptbBMns980 1.521e+00 1.261e+00 1.207 0.22760
## sp_ptbBMns981 -2.661e+00 1.439e+00 -1.849 0.06441 .
## sp_ptbBMns982 1.057e+00 1.188e+00 0.890 0.37366
## sp_ptbBMns983 -1.197e+00 1.176e+00 -1.017 0.30901
## sp_ptbBMns984 7.683e-01 1.003e+00 0.766 0.44362
## sp_ptbBMns985 -1.430e-01 9.796e-01 -0.146 0.88392
## sp_ptbBMns986 3.161e-01 1.056e+00 0.299 0.76474
## sp_ptbBMns987 1.222e+00 9.325e-01 1.310 0.19007
## sp_ptbBMns988 1.622e-01 9.192e-01 0.176 0.85992
## sp_ptbBMns989 1.697e+00 1.135e+00 1.495 0.13495
## sp_ptbBMns990 -7.799e-01 1.066e+00 -0.731 0.46453
## sp_ptbBMns991 -1.089e-01 1.182e+00 -0.092 0.92656
## sp_ptbBMns992 -5.863e-01 1.086e+00 -0.540 0.58914
## sp_ptbBMns993 6.395e-01 1.040e+00 0.615 0.53861
## sp_ptbBMns994 -2.090e+00 1.090e+00 -1.917 0.05528 .
## sp_ptbBMns995 2.146e+00 7.855e-01 2.733 0.00628 **
## sp_ptbBMns996 -1.994e+00 9.217e-01 -2.163 0.03050 *
## sp_ptbBMns997 1.572e+00 8.663e-01 1.815 0.06951 .
## sp_ptbBMns998 -8.712e-01 9.340e-01 -0.933 0.35096
## sp_ptbBMns999 5.508e-01 9.083e-01 0.606 0.54423
## sp_ptbBMns9100 5.301e-02 9.213e-01 0.058 0.95412
## sp_ptbBMns9101 8.362e-01 1.015e+00 0.824 0.41003
## sp_ptbBMns9102 2.306e-02 9.601e-01 0.024 0.98084
## sp_ptbBMns9103 1.229e-01 9.622e-01 0.128 0.89835
## sp_ptbBMns9104 -7.700e-01 9.947e-01 -0.774 0.43889
## sp_ptbBMns9105 6.733e-01 9.290e-01 0.725 0.46855
## sp_ptbBMns9106 -1.026e+00 8.759e-01 -1.172 0.24124
## sp_ptbBMns9107 -1.474e-01 9.026e-01 -0.163 0.87024
## sp_ptbBMns9108 1.231e-01 9.031e-01 0.136 0.89155
## sp_ptbBMns9109 -1.087e+00 9.526e-01 -1.141 0.25398
## sp_ptbBMns9110 -5.011e-01 9.454e-01 -0.530 0.59607
## sp_ptbBMns9111 3.906e-01 7.887e-01 0.495 0.62046
## sp_ptbBMns9112 1.801e-02 7.899e-01 0.023 0.98181
## sp_ptbBMns9113 -6.057e-01 8.730e-01 -0.694 0.48779
## sp_ptbBMns9114 1.171e-01 8.667e-01 0.135 0.89250
## sp_ptbBMns9115 -1.479e+00 9.867e-01 -1.499 0.13383
## sp_ptbBMns9116 6.474e-01 8.990e-01 0.720 0.47143
## sp_ptbBMns9117 -6.790e-01 1.135e+00 -0.598 0.54980
## sp_ptbBMns9118 -2.411e-01 8.929e-01 -0.270 0.78717
## sp_ptbBMns9119 2.320e-01 9.023e-01 0.257 0.79712
## sp_ptbBMns9120 -3.652e-01 9.762e-01 -0.374 0.70832
## sp_ptbBMns9121 -3.310e-01 1.005e+00 -0.329 0.74193
## sp_ptbBMns9122 -4.428e-01 9.982e-01 -0.444 0.65732
## sp_ptbBMns9123 7.278e-01 8.426e-01 0.864 0.38773
## sp_ptbBMns9124 1.082e-01 8.082e-01 0.134 0.89346
## sp_ptbBMns9125 1.312e+00 9.685e-01 1.354 0.17560
## sp_ptbBMns9126 -2.001e+00 1.180e+00 -1.695 0.09008 .
## sp_ptbBMns9127 1.239e+00 9.767e-01 1.268 0.20473
## sp_ptbBMns9128 -1.227e+00 1.017e+00 -1.206 0.22791
## sp_ptbBMns9129 4.688e-01 8.973e-01 0.523 0.60132
## sp_ptbBMns9130 -4.456e-01 8.369e-01 -0.532 0.59442
## sp_ptbBMns9131 2.306e-01 8.374e-01 0.275 0.78306
## sp_ptbBMns9132 -5.815e-01 8.991e-01 -0.647 0.51776
## sp_ptbBMns9133 -2.212e-01 8.472e-01 -0.261 0.79405
## sp_ptbBMns9134 2.898e-01 8.919e-01 0.325 0.74523
## sp_ptbBMns9135 -1.103e+00 1.063e+00 -1.038 0.29921
## sp_ptbBMns9136 7.587e-01 1.066e+00 0.712 0.47652
## sp_ptbBMns9137 -2.127e+00 1.063e+00 -2.001 0.04538 *
## sp_ptbBMns9138 7.072e-01 9.204e-01 0.768 0.44226
## sp_ptbBMns9139 -7.257e-01 9.152e-01 -0.793 0.42778
## sp_ptbBMns9140 2.327e-01 9.661e-01 0.241 0.80962
## sp_ptbBMns9141 -6.055e-01 9.020e-01 -0.671 0.50208
## sp_ptbBMns9142 7.941e-01 8.757e-01 0.907 0.36453
## sp_ptbBMns9143 -5.558e-01 1.095e+00 -0.507 0.61187
## sp_ptbBMns9144 -6.372e-01 1.034e+00 -0.616 0.53772
## sp_ptbBMns9145 -8.785e-02 8.828e-01 -0.100 0.92073
## sp_ptbBMns9146 4.012e-01 8.120e-01 0.494 0.62122
## sp_ptbBMns9147 5.099e-02 8.994e-01 0.057 0.95479
## sp_ptbBMns9148 -1.021e+00 9.667e-01 -1.056 0.29112
## sp_ptbBMns9149 5.346e-01 8.010e-01 0.667 0.50450
## sp_ptbBMns9150 5.459e-01 8.342e-01 0.654 0.51282
## sp_ptbBMns9151 -1.376e+00 9.235e-01 -1.490 0.13622
## sp_ptbBMns9152 1.518e+00 9.095e-01 1.669 0.09505 .
## sp_ptbBMns9153 -1.257e+00 1.129e+00 -1.113 0.26564
## sp_ptbBMns9154 -5.614e-01 1.014e+00 -0.554 0.57970
## sp_ptbBMns9155 6.101e-01 8.630e-01 0.707 0.47959
## sp_ptbBMns9156 -1.200e+00 8.969e-01 -1.338 0.18089
## sp_ptbBMns9157 9.732e-01 7.682e-01 1.267 0.20519
## sp_ptbBMns9158 -5.766e-01 8.480e-01 -0.680 0.49657
## sp_ptbBMns9159 8.594e-01 6.349e-01 1.354 0.17585
## sp_ptbBMns9160 -1.185e-01 8.126e-01 -0.146 0.88405
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(27894.86) family taken to be 1)
##
## Null deviance: 1101.3 on 886 degrees of freedom
## Residual deviance: 884.4 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3243.2
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 27895
## Std. Err.: 153572
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2917.245scatter.smooth(predict(SA4m3a, type='response'), rstandard(SA4m3a, type='deviance'), col='gray')
SA4m3a.resid<-residuals(SA4m3a, type="deviance")
SA4m3a.pred<-predict(SA4m3a, type="response")
length(SA4m3a.resid); length(SA4m3a.pred)## [1] 939## [1] 939pacf(SA4m3a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-8,10,13,14,18,25
SA4m3a.ac<-update(SA4m3a,.~.+lag(SA4m3a.resid,1)+lag(SA4m3a.resid,2)+lag(SA4m3a.resid,3)+lag(SA4m3a.resid,4)+
lag(SA4m3a.resid,5)+lag(SA4m3a.resid,6)+lag(SA4m3a.resid,7)+lag(SA4m3a.resid,8)+
lag(SA4m3a.resid,10)+lag(SA4m3a.resid,13)+lag(SA4m3a.resid,14)+lag(SA4m3a.resid,18)+
lag(SA4m3a.resid,25)) ## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedSA4m3a.resid_ac<-residuals(SA4m3a.ac, type="deviance")
SA4m3a.pred_ac<-predict(SA4m3a.ac, type="response")
pacf(SA4m3a.resid_ac,na.action = na.omit) 
length(SA4m3a.pred_ac); length(SA4m3a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m3a.pred,lwd=1, col="blue")
plot(week$time,SA4m3a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m3a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m3a.pred_ac,lwd=1, col="blue")
plot(week$time,SA4m3a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m3a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
##for SA4m5a minRH ######
summary(SA4m5a)##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBMns9, data = week,
## init.theta = 28286.77676, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.86355 -0.72927 -0.08996 0.56738 2.41082
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.906e+01 1.163e+01 1.639 0.10129
## cb5.minRHv1.l1 -1.304e-01 2.747e-01 -0.475 0.63498
## cb5.minRHv1.l2 -2.200e-02 1.886e-01 -0.117 0.90712
## cb5.minRHv2.l1 -1.762e+00 1.013e+00 -1.739 0.08201 .
## cb5.minRHv2.l2 -8.531e-01 7.616e-01 -1.120 0.26265
## cb5.minRHv3.l1 -1.028e+00 6.518e-01 -1.577 0.11477
## cb5.minRHv3.l2 -6.854e-01 5.151e-01 -1.330 0.18336
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.263e+06 1.299e+06 -1.742 0.08146 .
## sp_ptbBMns97 3.683e+01 1.582e+01 2.327 0.01996 *
## sp_ptbBMns98 -6.531e+00 2.605e+00 -2.507 0.01217 *
## sp_ptbBMns99 1.621e+00 1.208e+00 1.342 0.17958
## sp_ptbBMns910 -2.418e+00 1.118e+00 -2.162 0.03062 *
## sp_ptbBMns911 1.790e-01 1.031e+00 0.174 0.86211
## sp_ptbBMns912 -1.147e+00 1.039e+00 -1.104 0.26953
## sp_ptbBMns913 -1.856e+00 1.121e+00 -1.656 0.09782 .
## sp_ptbBMns914 -1.838e+00 1.137e+00 -1.616 0.10614
## sp_ptbBMns915 6.812e-02 9.566e-01 0.071 0.94323
## sp_ptbBMns916 -1.675e+00 9.506e-01 -1.762 0.07807 .
## sp_ptbBMns917 3.401e-01 1.046e+00 0.325 0.74508
## sp_ptbBMns918 -2.271e+00 1.201e+00 -1.891 0.05866 .
## sp_ptbBMns919 1.720e-02 1.303e+00 0.013 0.98947
## sp_ptbBMns920 -1.662e+00 1.346e+00 -1.235 0.21696
## sp_ptbBMns921 -9.859e-01 1.436e+00 -0.687 0.49237
## sp_ptbBMns922 -1.392e+00 1.236e+00 -1.127 0.25987
## sp_ptbBMns923 -5.537e-01 1.252e+00 -0.442 0.65831
## sp_ptbBMns924 -6.076e-01 1.163e+00 -0.523 0.60131
## sp_ptbBMns925 -6.465e-01 1.178e+00 -0.549 0.58327
## sp_ptbBMns926 -7.508e-01 1.200e+00 -0.625 0.53164
## sp_ptbBMns927 -2.175e-01 1.183e+00 -0.184 0.85408
## sp_ptbBMns928 -3.544e-01 1.032e+00 -0.343 0.73127
## sp_ptbBMns929 -3.508e-01 1.102e+00 -0.318 0.75019
## sp_ptbBMns930 -1.521e+00 1.037e+00 -1.467 0.14226
## sp_ptbBMns931 -3.213e-01 9.513e-01 -0.338 0.73559
## sp_ptbBMns932 -9.824e-01 8.611e-01 -1.141 0.25393
## sp_ptbBMns933 -4.346e-02 8.577e-01 -0.051 0.95959
## sp_ptbBMns934 -1.222e+00 9.605e-01 -1.273 0.20315
## sp_ptbBMns935 -9.898e-01 1.042e+00 -0.950 0.34216
## sp_ptbBMns936 -2.169e+00 1.147e+00 -1.892 0.05855 .
## sp_ptbBMns937 -6.879e-01 1.002e+00 -0.687 0.49229
## sp_ptbBMns938 -4.001e-01 1.034e+00 -0.387 0.69881
## sp_ptbBMns939 -1.014e+00 1.096e+00 -0.926 0.35450
## sp_ptbBMns940 -6.083e-01 1.253e+00 -0.485 0.62746
## sp_ptbBMns941 -5.224e-01 1.429e+00 -0.365 0.71474
## sp_ptbBMns942 -2.988e+00 1.638e+00 -1.823 0.06825 .
## sp_ptbBMns943 -7.512e-01 1.436e+00 -0.523 0.60084
## sp_ptbBMns944 -1.629e+00 1.606e+00 -1.014 0.31039
## sp_ptbBMns945 -1.604e+00 1.697e+00 -0.945 0.34465
## sp_ptbBMns946 -1.075e+00 1.817e+00 -0.591 0.55424
## sp_ptbBMns947 -9.659e-01 1.648e+00 -0.586 0.55787
## sp_ptbBMns948 -1.653e+00 1.520e+00 -1.087 0.27696
## sp_ptbBMns949 -3.564e-01 1.274e+00 -0.280 0.77960
## sp_ptbBMns950 -9.420e-01 1.382e+00 -0.682 0.49535
## sp_ptbBMns951 -6.569e-01 1.153e+00 -0.570 0.56893
## sp_ptbBMns952 -7.187e-01 1.130e+00 -0.636 0.52469
## sp_ptbBMns953 -2.003e-01 1.143e+00 -0.175 0.86094
## sp_ptbBMns954 -3.393e+00 1.326e+00 -2.560 0.01047 *
## sp_ptbBMns955 3.670e-01 1.056e+00 0.347 0.72823
## sp_ptbBMns956 -2.553e+00 1.099e+00 -2.324 0.02014 *
## sp_ptbBMns957 1.640e-01 9.895e-01 0.166 0.86839
## sp_ptbBMns958 -1.290e+00 1.050e+00 -1.229 0.21913
## sp_ptbBMns959 -4.310e-01 1.018e+00 -0.423 0.67195
## sp_ptbBMns960 -1.451e+00 1.145e+00 -1.268 0.20483
## sp_ptbBMns961 -4.434e-01 1.007e+00 -0.440 0.65962
## sp_ptbBMns962 -1.250e+00 9.236e-01 -1.354 0.17586
## sp_ptbBMns963 -1.914e-01 1.196e+00 -0.160 0.87281
## sp_ptbBMns964 -1.658e+00 1.251e+00 -1.325 0.18506
## sp_ptbBMns965 -1.034e+00 1.072e+00 -0.965 0.33477
## sp_ptbBMns966 -3.991e-01 1.204e+00 -0.332 0.74016
## sp_ptbBMns967 -1.111e+00 1.122e+00 -0.990 0.32213
## sp_ptbBMns968 -2.587e-01 1.056e+00 -0.245 0.80645
## sp_ptbBMns969 -2.235e-01 1.039e+00 -0.215 0.82974
## sp_ptbBMns970 -4.242e-01 1.022e+00 -0.415 0.67805
## sp_ptbBMns971 -9.195e-01 1.178e+00 -0.781 0.43493
## sp_ptbBMns972 -4.197e-01 1.131e+00 -0.371 0.71054
## sp_ptbBMns973 -8.902e-01 1.276e+00 -0.698 0.48526
## sp_ptbBMns974 -3.806e-01 1.029e+00 -0.370 0.71147
## sp_ptbBMns975 -8.280e-01 1.082e+00 -0.766 0.44396
## sp_ptbBMns976 -1.365e+00 1.109e+00 -1.231 0.21837
## sp_ptbBMns977 -1.311e+00 1.285e+00 -1.020 0.30766
## sp_ptbBMns978 -4.455e-01 1.142e+00 -0.390 0.69649
## sp_ptbBMns979 -1.285e+00 1.193e+00 -1.078 0.28114
## sp_ptbBMns980 6.618e-02 1.165e+00 0.057 0.95470
## sp_ptbBMns981 -3.669e+00 1.440e+00 -2.549 0.01080 *
## sp_ptbBMns982 -2.878e-01 1.557e+00 -0.185 0.85336
## sp_ptbBMns983 -2.932e+00 1.543e+00 -1.900 0.05738 .
## sp_ptbBMns984 -6.059e-01 1.183e+00 -0.512 0.60848
## sp_ptbBMns985 -1.415e+00 1.032e+00 -1.372 0.17013
## sp_ptbBMns986 -4.478e-01 8.471e-01 -0.529 0.59708
## sp_ptbBMns987 2.025e-01 7.203e-01 0.281 0.77865
## sp_ptbBMns988 -5.911e-01 7.728e-01 -0.765 0.44436
## sp_ptbBMns989 3.798e-01 8.214e-01 0.462 0.64382
## sp_ptbBMns990 -1.358e+00 1.071e+00 -1.268 0.20482
## sp_ptbBMns991 -9.822e-01 1.100e+00 -0.893 0.37176
## sp_ptbBMns992 -1.244e+00 1.096e+00 -1.134 0.25675
## sp_ptbBMns993 -3.896e-03 9.957e-01 -0.004 0.99688
## sp_ptbBMns994 -2.976e+00 1.101e+00 -2.703 0.00688 **
## sp_ptbBMns995 1.941e+00 8.847e-01 2.194 0.02821 *
## sp_ptbBMns996 -2.024e+00 9.568e-01 -2.115 0.03444 *
## sp_ptbBMns997 1.275e+00 9.183e-01 1.388 0.16499
## sp_ptbBMns998 -8.197e-01 8.338e-01 -0.983 0.32557
## sp_ptbBMns999 -1.236e-02 9.248e-01 -0.013 0.98934
## sp_ptbBMns9100 -2.289e-01 1.087e+00 -0.211 0.83317
## sp_ptbBMns9101 4.613e-01 1.126e+00 0.410 0.68212
## sp_ptbBMns9102 -2.225e-01 9.623e-01 -0.231 0.81715
## sp_ptbBMns9103 -5.088e-01 1.071e+00 -0.475 0.63466
## sp_ptbBMns9104 -1.203e+00 9.557e-01 -1.259 0.20817
## sp_ptbBMns9105 1.598e-01 8.993e-01 0.178 0.85894
## sp_ptbBMns9106 -1.414e+00 8.653e-01 -1.634 0.10231
## sp_ptbBMns9107 -1.008e+00 9.536e-01 -1.058 0.29027
## sp_ptbBMns9108 -6.837e-01 1.030e+00 -0.664 0.50669
## sp_ptbBMns9109 -1.964e+00 1.167e+00 -1.682 0.09251 .
## sp_ptbBMns9110 -1.402e+00 1.250e+00 -1.121 0.26214
## sp_ptbBMns9111 -6.531e-01 1.097e+00 -0.595 0.55173
## sp_ptbBMns9112 -1.731e-01 8.714e-01 -0.199 0.84255
## sp_ptbBMns9113 -9.763e-01 9.151e-01 -1.067 0.28603
## sp_ptbBMns9114 -3.351e-01 9.518e-01 -0.352 0.72482
## sp_ptbBMns9115 -1.755e+00 9.626e-01 -1.823 0.06834 .
## sp_ptbBMns9116 3.913e-02 9.940e-01 0.039 0.96859
## sp_ptbBMns9117 -1.076e+00 1.770e+00 -0.608 0.54316
## sp_ptbBMns9118 -2.628e-01 1.610e+00 -0.163 0.87037
## sp_ptbBMns9119 6.114e-01 1.422e+00 0.430 0.66726
## sp_ptbBMns9120 1.230e-01 1.458e+00 0.084 0.93276
## sp_ptbBMns9121 4.687e-01 1.279e+00 0.366 0.71405
## sp_ptbBMns9122 4.459e-01 1.271e+00 0.351 0.72580
## sp_ptbBMns9123 1.926e+00 1.279e+00 1.506 0.13216
## sp_ptbBMns9124 1.280e+00 1.401e+00 0.913 0.36112
## sp_ptbBMns9125 2.705e+00 1.551e+00 1.744 0.08120 .
## sp_ptbBMns9126 -2.027e+00 1.602e+00 -1.265 0.20588
## sp_ptbBMns9127 1.121e+00 1.366e+00 0.821 0.41164
## sp_ptbBMns9128 -7.239e-01 1.414e+00 -0.512 0.60864
## sp_ptbBMns9129 9.896e-01 1.227e+00 0.806 0.42007
## sp_ptbBMns9130 2.314e-01 1.205e+00 0.192 0.84776
## sp_ptbBMns9131 1.209e+00 1.131e+00 1.069 0.28486
## sp_ptbBMns9132 6.909e-01 1.144e+00 0.604 0.54581
## sp_ptbBMns9133 6.994e-01 1.094e+00 0.639 0.52263
## sp_ptbBMns9134 1.304e+00 1.058e+00 1.232 0.21795
## sp_ptbBMns9135 -1.291e+00 9.715e-01 -1.329 0.18399
## sp_ptbBMns9136 9.430e-01 1.174e+00 0.803 0.42170
## sp_ptbBMns9137 -1.738e+00 1.065e+00 -1.631 0.10279
## sp_ptbBMns9138 6.439e-01 9.503e-01 0.678 0.49803
## sp_ptbBMns9139 -6.649e-01 9.068e-01 -0.733 0.46340
## sp_ptbBMns9140 4.075e-01 8.531e-01 0.478 0.63291
## sp_ptbBMns9141 -1.645e-01 7.605e-01 -0.216 0.82877
## sp_ptbBMns9142 1.213e+00 7.427e-01 1.633 0.10243
## sp_ptbBMns9143 1.172e-01 7.664e-01 0.153 0.87849
## sp_ptbBMns9144 -2.523e-01 7.804e-01 -0.323 0.74644
## sp_ptbBMns9145 -2.263e-01 1.034e+00 -0.219 0.82672
## sp_ptbBMns9146 1.717e-01 9.100e-01 0.189 0.85033
## sp_ptbBMns9147 -2.372e-02 1.043e+00 -0.023 0.98185
## sp_ptbBMns9148 -1.026e+00 1.010e+00 -1.016 0.30977
## sp_ptbBMns9149 5.527e-01 8.572e-01 0.645 0.51906
## sp_ptbBMns9150 4.723e-01 1.013e+00 0.466 0.64091
## sp_ptbBMns9151 -1.703e+00 1.014e+00 -1.679 0.09319 .
## sp_ptbBMns9152 1.350e+00 9.003e-01 1.499 0.13381
## sp_ptbBMns9153 -1.157e+00 1.115e+00 -1.038 0.29930
## sp_ptbBMns9154 -1.236e+00 1.282e+00 -0.964 0.33499
## sp_ptbBMns9155 4.781e-01 1.024e+00 0.467 0.64055
## sp_ptbBMns9156 -1.416e+00 1.155e+00 -1.226 0.22017
## sp_ptbBMns9157 9.910e-01 8.707e-01 1.138 0.25505
## sp_ptbBMns9158 -5.751e-01 8.481e-01 -0.678 0.49772
## sp_ptbBMns9159 1.020e+00 6.377e-01 1.599 0.10981
## sp_ptbBMns9160 -4.082e-03 7.966e-01 -0.005 0.99591
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28286.78) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 881.61 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3240.5
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28287
## Std. Err.: 154283
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2914.458scatter.smooth(predict(SA4m5a, type='response'), rstandard(SA4m5a, type='deviance'), col='gray')
SA4m5a.resid<-residuals(SA4m5a, type="deviance")
SA4m5a.pred<-predict(SA4m5a, type="response")
length(SA4m5a.resid); length(SA4m5a.pred)## [1] 939## [1] 939pacf(SA4m5a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-8,10,13,14,18,25
#ensure that the lags are dplyr lags
SA4m5a.ac<-update(SA4m5a,.~.+lag(SA4m5a.resid,1)+lag(SA4m5a.resid,2)+lag(SA4m5a.resid,3)+lag(SA4m5a.resid,4)+
lag(SA4m5a.resid,5)+lag(SA4m5a.resid,6)+lag(SA4m5a.resid,7)+lag(SA4m5a.resid,8)+
lag(SA4m5a.resid,10)+lag(SA4m5a.resid,13)+lag(SA4m5a.resid,14)+lag(SA4m5a.resid,18)+
lag(SA4m5a.resid,25)) ## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(SA4m5a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb5.minRH + sp_ptbBMns9 + lag(SA4m5a.resid,
## 1) + lag(SA4m5a.resid, 2) + lag(SA4m5a.resid, 3) + lag(SA4m5a.resid,
## 4) + lag(SA4m5a.resid, 5) + lag(SA4m5a.resid, 6) + lag(SA4m5a.resid,
## 7) + lag(SA4m5a.resid, 8) + lag(SA4m5a.resid, 10) + lag(SA4m5a.resid,
## 13) + lag(SA4m5a.resid, 14) + lag(SA4m5a.resid, 18) + lag(SA4m5a.resid,
## 25), data = week, init.theta = 49737.72241, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.89419 -0.61565 -0.04401 0.40764 2.49940
##
## Coefficients: (12 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 28.722885 12.302623 2.335 0.019559 *
## cb5.minRHv1.l1 0.075485 0.287055 0.263 0.792578
## cb5.minRHv1.l2 -0.105766 0.197923 -0.534 0.593078
## cb5.minRHv2.l1 -2.857385 1.077417 -2.652 0.008000 **
## cb5.minRHv2.l2 -1.186519 0.799728 -1.484 0.137901
## cb5.minRHv3.l1 -1.484263 0.700165 -2.120 0.034016 *
## cb5.minRHv3.l2 -0.661764 0.537405 -1.231 0.218171
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 NA NA NA NA
## sp_ptbBMns97 NA NA NA NA
## sp_ptbBMns98 NA NA NA NA
## sp_ptbBMns99 NA NA NA NA
## sp_ptbBMns910 NA NA NA NA
## sp_ptbBMns911 -14.769767 38.296836 -0.386 0.699744
## sp_ptbBMns912 -0.786733 4.231540 -0.186 0.852506
## sp_ptbBMns913 -0.322188 1.993877 -0.162 0.871630
## sp_ptbBMns914 -4.157518 1.258595 -3.303 0.000956 ***
## sp_ptbBMns915 1.571253 1.032416 1.522 0.128029
## sp_ptbBMns916 -1.492452 1.035814 -1.441 0.149627
## sp_ptbBMns917 1.307842 1.088748 1.201 0.229660
## sp_ptbBMns918 -2.023702 1.220827 -1.658 0.097388 .
## sp_ptbBMns919 1.956830 1.389484 1.408 0.159038
## sp_ptbBMns920 -0.029634 1.407442 -0.021 0.983202
## sp_ptbBMns921 -0.396038 1.479597 -0.268 0.788957
## sp_ptbBMns922 -0.868449 1.373831 -0.632 0.527297
## sp_ptbBMns923 0.977139 1.322896 0.739 0.460128
## sp_ptbBMns924 0.007398 1.214611 0.006 0.995140
## sp_ptbBMns925 2.277832 1.192688 1.910 0.056155 .
## sp_ptbBMns926 -3.112317 1.338355 -2.325 0.020046 *
## sp_ptbBMns927 2.517539 1.275621 1.974 0.048430 *
## sp_ptbBMns928 -2.192973 1.083261 -2.024 0.042927 *
## sp_ptbBMns929 1.029059 1.159167 0.888 0.374671
## sp_ptbBMns930 -2.877498 1.024951 -2.807 0.004994 **
## sp_ptbBMns931 -0.634328 1.026144 -0.618 0.536466
## sp_ptbBMns932 -0.863960 0.924219 -0.935 0.349891
## sp_ptbBMns933 -1.484996 0.918487 -1.617 0.105925
## sp_ptbBMns934 0.129344 1.065203 0.121 0.903353
## sp_ptbBMns935 -3.300930 1.009053 -3.271 0.001070 **
## sp_ptbBMns936 -1.752033 1.216532 -1.440 0.149814
## sp_ptbBMns937 -1.803400 1.034398 -1.743 0.081259 .
## sp_ptbBMns938 1.230119 1.068927 1.151 0.249816
## sp_ptbBMns939 -0.689609 1.158609 -0.595 0.551707
## sp_ptbBMns940 0.858483 1.305828 0.657 0.510908
## sp_ptbBMns941 0.337184 1.461956 0.231 0.817595
## sp_ptbBMns942 -1.408007 1.681675 -0.837 0.402444
## sp_ptbBMns943 0.150037 1.509929 0.099 0.920847
## sp_ptbBMns944 0.515330 1.677914 0.307 0.758748
## sp_ptbBMns945 -0.933315 1.752548 -0.533 0.594347
## sp_ptbBMns946 0.429524 1.912986 0.225 0.822344
## sp_ptbBMns947 1.131613 1.722291 0.657 0.511156
## sp_ptbBMns948 -1.915894 1.603625 -1.195 0.232194
## sp_ptbBMns949 1.217189 1.358521 0.896 0.370271
## sp_ptbBMns950 0.075912 1.420790 0.053 0.957390
## sp_ptbBMns951 0.920428 1.249095 0.737 0.461198
## sp_ptbBMns952 -0.917011 1.194127 -0.768 0.442526
## sp_ptbBMns953 1.129165 1.181083 0.956 0.339051
## sp_ptbBMns954 -4.022570 1.481181 -2.716 0.006612 **
## sp_ptbBMns955 0.813817 1.106580 0.735 0.462075
## sp_ptbBMns956 -2.692936 1.104544 -2.438 0.014767 *
## sp_ptbBMns957 0.323664 1.014453 0.319 0.749687
## sp_ptbBMns958 -1.610053 1.105428 -1.456 0.145255
## sp_ptbBMns959 0.719855 1.132679 0.636 0.525081
## sp_ptbBMns960 -2.114112 1.184200 -1.785 0.074218 .
## sp_ptbBMns961 1.336266 1.042883 1.281 0.200081
## sp_ptbBMns962 -2.569698 0.959315 -2.679 0.007391 **
## sp_ptbBMns963 1.534712 1.261559 1.217 0.223787
## sp_ptbBMns964 -1.138986 1.282548 -0.888 0.374506
## sp_ptbBMns965 -0.429663 1.110430 -0.387 0.698805
## sp_ptbBMns966 -0.071822 1.273016 -0.056 0.955008
## sp_ptbBMns967 0.908321 1.182195 0.768 0.442289
## sp_ptbBMns968 -0.314221 1.093858 -0.287 0.773914
## sp_ptbBMns969 2.506180 1.075126 2.331 0.019750 *
## sp_ptbBMns970 -0.547037 1.034935 -0.529 0.597103
## sp_ptbBMns971 0.205907 1.282910 0.161 0.872487
## sp_ptbBMns972 -0.601332 1.190097 -0.505 0.613362
## sp_ptbBMns973 0.815597 1.312468 0.621 0.534322
## sp_ptbBMns974 -0.448114 1.050541 -0.427 0.669704
## sp_ptbBMns975 -0.254257 1.087279 -0.234 0.815104
## sp_ptbBMns976 -1.040326 1.203242 -0.865 0.387257
## sp_ptbBMns977 -0.992738 1.354306 -0.733 0.463544
## sp_ptbBMns978 -0.586706 1.145590 -0.512 0.608551
## sp_ptbBMns979 -0.805393 1.233837 -0.653 0.513914
## sp_ptbBMns980 0.053490 1.219183 0.044 0.965005
## sp_ptbBMns981 -2.046169 1.495464 -1.368 0.171234
## sp_ptbBMns982 -0.615771 1.622969 -0.379 0.704383
## sp_ptbBMns983 -1.477080 1.602371 -0.922 0.356628
## sp_ptbBMns984 -1.606234 1.197363 -1.341 0.179766
## sp_ptbBMns985 -0.154162 1.090801 -0.141 0.887610
## sp_ptbBMns986 -0.526693 0.896742 -0.587 0.556975
## sp_ptbBMns987 0.107464 0.802819 0.134 0.893515
## sp_ptbBMns988 -0.362137 0.779376 -0.465 0.642182
## sp_ptbBMns989 0.906695 0.833853 1.087 0.276879
## sp_ptbBMns990 -1.367997 1.135590 -1.205 0.228335
## sp_ptbBMns991 -0.440617 1.180246 -0.373 0.708906
## sp_ptbBMns992 -1.582069 1.116738 -1.417 0.156574
## sp_ptbBMns993 -0.109816 1.060339 -0.104 0.917513
## sp_ptbBMns994 -2.124036 1.018789 -2.085 0.037082 *
## sp_ptbBMns995 2.180255 0.944213 2.309 0.020940 *
## sp_ptbBMns996 -1.244995 1.125238 -1.106 0.268541
## sp_ptbBMns997 0.446782 0.982866 0.455 0.649418
## sp_ptbBMns998 1.285885 0.914995 1.405 0.159918
## sp_ptbBMns999 -2.076746 0.966596 -2.149 0.031673 *
## sp_ptbBMns9100 2.602312 1.122159 2.319 0.020394 *
## sp_ptbBMns9101 0.075044 1.190730 0.063 0.949748
## sp_ptbBMns9102 2.257253 1.005397 2.245 0.024759 *
## sp_ptbBMns9103 -1.358007 1.161180 -1.170 0.242200
## sp_ptbBMns9104 0.072476 1.027744 0.071 0.943780
## sp_ptbBMns9105 -0.767994 0.940294 -0.817 0.414066
## sp_ptbBMns9106 0.854957 0.879915 0.972 0.331232
## sp_ptbBMns9107 -4.627037 1.016929 -4.550 5.36e-06 ***
## sp_ptbBMns9108 1.607451 1.091189 1.473 0.140719
## sp_ptbBMns9109 -3.789151 1.172010 -3.233 0.001225 **
## sp_ptbBMns9110 -1.809785 1.358419 -1.332 0.182770
## sp_ptbBMns9111 -1.259706 1.165740 -1.081 0.279872
## sp_ptbBMns9112 -0.453857 0.897407 -0.506 0.613037
## sp_ptbBMns9113 -0.535347 0.984523 -0.544 0.586604
## sp_ptbBMns9114 -2.133275 0.968700 -2.202 0.027651 *
## sp_ptbBMns9115 -2.100321 0.991121 -2.119 0.034079 *
## sp_ptbBMns9116 -2.131461 1.011988 -2.106 0.035186 *
## sp_ptbBMns9117 -0.295285 1.809736 -0.163 0.870389
## sp_ptbBMns9118 -0.137970 1.715337 -0.080 0.935893
## sp_ptbBMns9119 1.250875 1.484265 0.843 0.399364
## sp_ptbBMns9120 0.495603 1.528224 0.324 0.745711
## sp_ptbBMns9121 1.124502 1.311599 0.857 0.391250
## sp_ptbBMns9122 -1.528356 1.359132 -1.125 0.260797
## sp_ptbBMns9123 2.925830 1.294060 2.261 0.023761 *
## sp_ptbBMns9124 -0.424598 1.546215 -0.275 0.783620
## sp_ptbBMns9125 1.999795 1.602810 1.248 0.212148
## sp_ptbBMns9126 -4.879673 1.652017 -2.954 0.003139 **
## sp_ptbBMns9127 1.439142 1.425717 1.009 0.312775
## sp_ptbBMns9128 -2.413323 1.452268 -1.662 0.096561 .
## sp_ptbBMns9129 1.805976 1.310680 1.378 0.168236
## sp_ptbBMns9130 -0.770726 1.230522 -0.626 0.531091
## sp_ptbBMns9131 0.530325 1.224646 0.433 0.664983
## sp_ptbBMns9132 0.058158 1.236027 0.047 0.962471
## sp_ptbBMns9133 -1.090184 1.133755 -0.962 0.336266
## sp_ptbBMns9134 0.475375 1.072035 0.443 0.657453
## sp_ptbBMns9135 -1.015606 1.058487 -0.959 0.337313
## sp_ptbBMns9136 0.294566 1.207844 0.244 0.807325
## sp_ptbBMns9137 -2.991454 1.192595 -2.508 0.012129 *
## sp_ptbBMns9138 -0.330843 1.024038 -0.323 0.746637
## sp_ptbBMns9139 -0.803634 0.937320 -0.857 0.391238
## sp_ptbBMns9140 0.624924 0.868789 0.719 0.471953
## sp_ptbBMns9141 0.368163 0.769533 0.478 0.632348
## sp_ptbBMns9142 1.294684 0.793520 1.632 0.102770
## sp_ptbBMns9143 1.317326 0.792522 1.662 0.096474 .
## sp_ptbBMns9144 -1.814026 0.779015 -2.329 0.019880 *
## sp_ptbBMns9145 0.783542 1.060342 0.739 0.459936
## sp_ptbBMns9146 -1.319689 0.939053 -1.405 0.159920
## sp_ptbBMns9147 0.371851 1.072666 0.347 0.728847
## sp_ptbBMns9148 -2.325171 1.128955 -2.060 0.039439 *
## sp_ptbBMns9149 0.773930 0.900411 0.860 0.390049
## sp_ptbBMns9150 -0.266130 1.040138 -0.256 0.798059
## sp_ptbBMns9151 -1.997875 1.056352 -1.891 0.058585 .
## sp_ptbBMns9152 0.188689 0.933402 0.202 0.839798
## sp_ptbBMns9153 -0.317538 1.160630 -0.274 0.784399
## sp_ptbBMns9154 -3.942883 1.333287 -2.957 0.003104 **
## sp_ptbBMns9155 0.065459 1.079525 0.061 0.951649
## sp_ptbBMns9156 -2.445956 1.234173 -1.982 0.047495 *
## sp_ptbBMns9157 0.461075 0.890471 0.518 0.604606
## sp_ptbBMns9158 -0.814359 0.871169 -0.935 0.349898
## sp_ptbBMns9159 1.111883 0.636554 1.747 0.080685 .
## sp_ptbBMns9160 0.672346 0.852091 0.789 0.430080
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## lag(SA4m5a.resid, 1) -0.551954 0.034137 -16.169 < 2e-16 ***
## lag(SA4m5a.resid, 2) -0.619133 0.040644 -15.233 < 2e-16 ***
## lag(SA4m5a.resid, 3) -0.710911 0.044748 -15.887 < 2e-16 ***
## lag(SA4m5a.resid, 4) -0.673674 0.047430 -14.203 < 2e-16 ***
## lag(SA4m5a.resid, 5) -0.609605 0.046769 -13.034 < 2e-16 ***
## lag(SA4m5a.resid, 6) -0.480079 0.043619 -11.006 < 2e-16 ***
## lag(SA4m5a.resid, 7) -0.342502 0.038079 -8.994 < 2e-16 ***
## lag(SA4m5a.resid, 8) -0.212727 0.032213 -6.604 4.01e-11 ***
## lag(SA4m5a.resid, 10) -0.018939 0.026314 -0.720 0.471692
## lag(SA4m5a.resid, 13) 0.069382 0.026861 2.583 0.009794 **
## lag(SA4m5a.resid, 14) 0.061922 0.026584 2.329 0.019842 *
## lag(SA4m5a.resid, 18) -0.062049 0.024993 -2.483 0.013040 *
## lag(SA4m5a.resid, 25) -0.025852 0.025121 -1.029 0.303421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(49737.72) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 469.66 on 692 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2793.6
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 49738
## Std. Err.: 185210
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2451.582SA4m5a.resid_ac<-residuals(SA4m5a.ac, type="deviance")
SA4m5a.pred_ac<-predict(SA4m5a.ac, type="response")
pacf(SA4m5a.resid_ac,na.action = na.omit) 
length(SA4m5a.pred_ac); length(SA4m5a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m5a.pred,lwd=1, col="blue")
plot(week$time,SA4m5a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m5a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m5a.pred_ac,lwd=1, col="blue")
plot(week$time,SA4m5a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m5a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose reponse and slices
pred.SA4m5a <- crosspred(cb5.minRH, SA4m5a.ac, cen = 63, by=0.1,cumul=TRUE)
##for SA4m9a minT ######
summary(SA4m9a)##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBMns9, data = week,
## init.theta = 28208.16849, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.98149 -0.77078 -0.08243 0.55014 2.45043
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -6.116e+00 1.206e+01 -0.507 0.61211
## cb9.minTv1.l1 1.228e-01 2.879e-01 0.427 0.66970
## cb9.minTv1.l2 -7.056e-02 2.212e-01 -0.319 0.74969
## cb9.minTv2.l1 8.990e-01 1.042e+00 0.863 0.38819
## cb9.minTv2.l2 -1.813e-01 7.883e-01 -0.230 0.81810
## cb9.minTv3.l1 7.360e-01 6.067e-01 1.213 0.22505
## cb9.minTv3.l2 6.745e-01 4.245e-01 1.589 0.11211
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.221e+06 1.297e+06 -1.712 0.08683 .
## sp_ptbBMns97 3.667e+01 1.588e+01 2.308 0.02098 *
## sp_ptbBMns98 -6.176e+00 2.604e+00 -2.372 0.01770 *
## sp_ptbBMns99 1.821e+00 1.230e+00 1.480 0.13889
## sp_ptbBMns910 -2.398e+00 1.224e+00 -1.959 0.05014 .
## sp_ptbBMns911 3.001e-01 1.087e+00 0.276 0.78239
## sp_ptbBMns912 -5.570e-01 1.164e+00 -0.479 0.63214
## sp_ptbBMns913 -1.816e+00 1.420e+00 -1.278 0.20117
## sp_ptbBMns914 -1.018e+00 1.144e+00 -0.890 0.37361
## sp_ptbBMns915 4.164e-01 9.768e-01 0.426 0.66992
## sp_ptbBMns916 -1.581e+00 1.038e+00 -1.523 0.12774
## sp_ptbBMns917 4.578e-01 9.727e-01 0.471 0.63792
## sp_ptbBMns918 -2.196e+00 1.108e+00 -1.981 0.04755 *
## sp_ptbBMns919 2.720e-01 1.029e+00 0.264 0.79147
## sp_ptbBMns920 -1.210e+00 1.064e+00 -1.137 0.25557
## sp_ptbBMns921 1.558e-01 1.298e+00 0.120 0.90444
## sp_ptbBMns922 -6.069e-01 1.109e+00 -0.547 0.58408
## sp_ptbBMns923 -4.708e-01 1.237e+00 -0.381 0.70343
## sp_ptbBMns924 -1.044e+00 1.166e+00 -0.896 0.37029
## sp_ptbBMns925 -1.491e+00 1.269e+00 -1.174 0.24020
## sp_ptbBMns926 -1.898e+00 1.161e+00 -1.635 0.10207
## sp_ptbBMns927 -7.602e-01 1.131e+00 -0.672 0.50137
## sp_ptbBMns928 -7.848e-01 9.480e-01 -0.828 0.40776
## sp_ptbBMns929 -1.132e+00 1.068e+00 -1.059 0.28942
## sp_ptbBMns930 -6.714e-01 1.079e+00 -0.622 0.53369
## sp_ptbBMns931 -6.953e-03 9.434e-01 -0.007 0.99412
## sp_ptbBMns932 -9.202e-01 8.492e-01 -1.084 0.27853
## sp_ptbBMns933 7.944e-02 8.590e-01 0.092 0.92632
## sp_ptbBMns934 -1.319e+00 9.606e-01 -1.374 0.16958
## sp_ptbBMns935 -2.182e+00 1.197e+00 -1.823 0.06826 .
## sp_ptbBMns936 -1.005e+00 1.138e+00 -0.883 0.37714
## sp_ptbBMns937 -2.450e-01 8.848e-01 -0.277 0.78184
## sp_ptbBMns938 4.370e-01 7.871e-01 0.555 0.57873
## sp_ptbBMns939 9.309e-02 9.989e-01 0.093 0.92575
## sp_ptbBMns940 4.986e-01 9.513e-01 0.524 0.60018
## sp_ptbBMns941 4.877e-01 9.803e-01 0.497 0.61884
## sp_ptbBMns942 -1.373e+00 1.242e+00 -1.105 0.26899
## sp_ptbBMns943 2.138e-01 1.114e+00 0.192 0.84784
## sp_ptbBMns944 3.524e-01 1.601e+00 0.220 0.82585
## sp_ptbBMns945 2.121e-01 1.548e+00 0.137 0.89097
## sp_ptbBMns946 1.555e+00 1.741e+00 0.894 0.37156
## sp_ptbBMns947 1.745e+00 1.877e+00 0.930 0.35257
## sp_ptbBMns948 9.474e-01 2.298e+00 0.412 0.68011
## sp_ptbBMns949 1.997e+00 2.059e+00 0.970 0.33208
## sp_ptbBMns950 7.548e-01 2.151e+00 0.351 0.72565
## sp_ptbBMns951 9.072e-01 1.965e+00 0.462 0.64435
## sp_ptbBMns952 6.559e-01 2.032e+00 0.323 0.74684
## sp_ptbBMns953 9.378e-01 1.542e+00 0.608 0.54300
## sp_ptbBMns954 -2.008e+00 1.488e+00 -1.350 0.17712
## sp_ptbBMns955 1.637e+00 1.320e+00 1.241 0.21479
## sp_ptbBMns956 -1.607e+00 1.185e+00 -1.356 0.17515
## sp_ptbBMns957 9.999e-01 8.692e-01 1.150 0.24999
## sp_ptbBMns958 -3.226e-01 8.655e-01 -0.373 0.70938
## sp_ptbBMns959 4.903e-01 9.790e-01 0.501 0.61652
## sp_ptbBMns960 -6.143e-01 8.926e-01 -0.688 0.49127
## sp_ptbBMns961 5.691e-01 9.357e-01 0.608 0.54308
## sp_ptbBMns962 -5.308e-01 1.025e+00 -0.518 0.60445
## sp_ptbBMns963 1.071e+00 9.216e-01 1.162 0.24510
## sp_ptbBMns964 1.630e-01 1.078e+00 0.151 0.87987
## sp_ptbBMns965 3.014e-01 1.059e+00 0.285 0.77594
## sp_ptbBMns966 1.496e+00 1.072e+00 1.395 0.16291
## sp_ptbBMns967 2.613e-01 1.313e+00 0.199 0.84221
## sp_ptbBMns968 8.579e-01 1.281e+00 0.670 0.50298
## sp_ptbBMns969 7.239e-01 1.331e+00 0.544 0.58656
## sp_ptbBMns970 3.697e-01 1.261e+00 0.293 0.76941
## sp_ptbBMns971 -5.093e-01 1.291e+00 -0.395 0.69318
## sp_ptbBMns972 6.460e-01 1.271e+00 0.508 0.61136
## sp_ptbBMns973 3.225e-01 1.176e+00 0.274 0.78400
## sp_ptbBMns974 5.431e-01 1.009e+00 0.538 0.59037
## sp_ptbBMns975 7.678e-01 1.073e+00 0.716 0.47420
## sp_ptbBMns976 -3.606e-01 9.639e-01 -0.374 0.70837
## sp_ptbBMns977 1.916e-02 9.017e-01 0.021 0.98304
## sp_ptbBMns978 9.441e-02 8.362e-01 0.113 0.91010
## sp_ptbBMns979 -4.815e-01 8.446e-01 -0.570 0.56864
## sp_ptbBMns980 1.305e-01 9.566e-01 0.136 0.89148
## sp_ptbBMns981 -2.499e+00 1.045e+00 -2.391 0.01681 *
## sp_ptbBMns982 1.168e+00 1.290e+00 0.905 0.36528
## sp_ptbBMns983 -1.725e+00 1.395e+00 -1.237 0.21622
## sp_ptbBMns984 1.176e+00 1.570e+00 0.749 0.45371
## sp_ptbBMns985 -9.352e-01 1.426e+00 -0.656 0.51210
## sp_ptbBMns986 -8.435e-01 1.381e+00 -0.611 0.54123
## sp_ptbBMns987 -5.982e-01 1.349e+00 -0.444 0.65740
## sp_ptbBMns988 -2.235e+00 1.428e+00 -1.564 0.11771
## sp_ptbBMns989 -1.803e+00 1.352e+00 -1.334 0.18224
## sp_ptbBMns990 -3.641e+00 1.542e+00 -2.362 0.01820 *
## sp_ptbBMns991 -1.970e+00 1.151e+00 -1.712 0.08698 .
## sp_ptbBMns992 -2.120e+00 1.081e+00 -1.961 0.04988 *
## sp_ptbBMns993 5.083e-01 9.271e-01 0.548 0.58355
## sp_ptbBMns994 -2.599e+00 1.049e+00 -2.478 0.01322 *
## sp_ptbBMns995 2.309e+00 8.449e-01 2.733 0.00627 **
## sp_ptbBMns996 -1.987e+00 9.634e-01 -2.062 0.03917 *
## sp_ptbBMns997 1.738e+00 9.027e-01 1.926 0.05413 .
## sp_ptbBMns998 -1.001e+00 9.224e-01 -1.085 0.27793
## sp_ptbBMns999 2.519e-01 8.924e-01 0.282 0.77774
## sp_ptbBMns9100 -8.762e-02 9.798e-01 -0.089 0.92875
## sp_ptbBMns9101 6.719e-02 9.709e-01 0.069 0.94483
## sp_ptbBMns9102 1.102e-01 1.077e+00 0.102 0.91847
## sp_ptbBMns9103 1.534e-01 1.004e+00 0.153 0.87856
## sp_ptbBMns9104 -7.642e-01 1.007e+00 -0.759 0.44795
## sp_ptbBMns9105 3.336e-01 1.032e+00 0.323 0.74636
## sp_ptbBMns9106 -1.223e+00 1.032e+00 -1.185 0.23594
## sp_ptbBMns9107 -5.118e-01 1.129e+00 -0.453 0.65021
## sp_ptbBMns9108 -2.886e-01 1.158e+00 -0.249 0.80315
## sp_ptbBMns9109 -1.160e+00 1.429e+00 -0.812 0.41684
## sp_ptbBMns9110 -1.641e+00 1.491e+00 -1.100 0.27130
## sp_ptbBMns9111 6.404e-01 1.515e+00 0.423 0.67247
## sp_ptbBMns9112 -6.971e-02 1.538e+00 -0.045 0.96384
## sp_ptbBMns9113 -1.308e+00 1.452e+00 -0.900 0.36791
## sp_ptbBMns9114 -1.140e+00 1.502e+00 -0.759 0.44800
## sp_ptbBMns9115 -3.139e+00 1.524e+00 -2.060 0.03942 *
## sp_ptbBMns9116 -1.629e+00 1.560e+00 -1.044 0.29646
## sp_ptbBMns9117 -2.156e+00 1.307e+00 -1.650 0.09899 .
## sp_ptbBMns9118 -1.275e+00 1.089e+00 -1.171 0.24172
## sp_ptbBMns9119 3.570e-02 1.162e+00 0.031 0.97550
## sp_ptbBMns9120 -3.056e-01 1.288e+00 -0.237 0.81240
## sp_ptbBMns9121 1.853e-01 1.365e+00 0.136 0.89206
## sp_ptbBMns9122 -8.597e-01 1.373e+00 -0.626 0.53135
## sp_ptbBMns9123 -6.787e-02 1.281e+00 -0.053 0.95774
## sp_ptbBMns9124 -1.156e+00 1.338e+00 -0.864 0.38763
## sp_ptbBMns9125 -5.926e-01 1.486e+00 -0.399 0.69015
## sp_ptbBMns9126 -3.942e+00 1.539e+00 -2.561 0.01044 *
## sp_ptbBMns9127 -1.201e+00 1.599e+00 -0.751 0.45253
## sp_ptbBMns9128 -2.153e+00 1.548e+00 -1.391 0.16416
## sp_ptbBMns9129 6.038e-01 1.552e+00 0.389 0.69734
## sp_ptbBMns9130 -1.323e-01 1.832e+00 -0.072 0.94243
## sp_ptbBMns9131 -3.575e-02 1.585e+00 -0.023 0.98201
## sp_ptbBMns9132 -1.122e+00 1.752e+00 -0.640 0.52196
## sp_ptbBMns9133 -1.742e+00 1.729e+00 -1.007 0.31387
## sp_ptbBMns9134 -1.064e+00 1.800e+00 -0.591 0.55454
## sp_ptbBMns9135 -2.712e+00 2.427e+00 -1.117 0.26386
## sp_ptbBMns9136 -1.678e+00 3.415e+00 -0.491 0.62320
## sp_ptbBMns9137 -3.883e+00 4.172e+00 -0.931 0.35197
## sp_ptbBMns9138 -1.559e+00 4.844e+00 -0.322 0.74752
## sp_ptbBMns9139 -3.329e+00 4.094e+00 -0.813 0.41604
## sp_ptbBMns9140 -3.860e+00 4.495e+00 -0.859 0.39048
## sp_ptbBMns9141 -6.182e+00 4.163e+00 -1.485 0.13755
## sp_ptbBMns9142 -6.819e+00 4.278e+00 -1.594 0.11101
## sp_ptbBMns9143 -8.999e+00 4.473e+00 -2.012 0.04425 *
## sp_ptbBMns9144 -8.630e+00 4.023e+00 -2.145 0.03193 *
## sp_ptbBMns9145 -6.349e+00 3.039e+00 -2.089 0.03667 *
## sp_ptbBMns9146 -3.794e+00 2.131e+00 -1.780 0.07505 .
## sp_ptbBMns9147 -1.382e+00 1.666e+00 -0.830 0.40667
## sp_ptbBMns9148 -2.846e+00 1.553e+00 -1.833 0.06681 .
## sp_ptbBMns9149 -2.840e-01 1.546e+00 -0.184 0.85422
## sp_ptbBMns9150 -5.188e-01 1.267e+00 -0.409 0.68220
## sp_ptbBMns9151 -2.734e+00 1.252e+00 -2.184 0.02898 *
## sp_ptbBMns9152 5.867e-01 9.581e-01 0.612 0.54027
## sp_ptbBMns9153 -2.225e+00 1.013e+00 -2.196 0.02806 *
## sp_ptbBMns9154 -1.415e+00 9.996e-01 -1.416 0.15691
## sp_ptbBMns9155 -2.294e-01 8.320e-01 -0.276 0.78274
## sp_ptbBMns9156 -8.082e-01 8.680e-01 -0.931 0.35182
## sp_ptbBMns9157 9.973e-01 6.755e-01 1.476 0.13983
## sp_ptbBMns9158 1.821e-01 8.491e-01 0.215 0.83015
## sp_ptbBMns9159 1.327e+00 6.846e-01 1.938 0.05267 .
## sp_ptbBMns9160 1.173e-01 8.374e-01 0.140 0.88858
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(28208.17) family taken to be 1)
##
## Null deviance: 1101.28 on 886 degrees of freedom
## Residual deviance: 878.09 on 725 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3236.9
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 28208
## Std. Err.: 152517
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2910.936scatter.smooth(predict(SA4m9a, type='response'), rstandard(SA4m9a, type='deviance'), col='gray')
SA4m9a.resid<-residuals(SA4m9a, type="deviance")
SA4m9a.pred<-predict(SA4m9a, type="response")
length(SA4m9a.resid); length(SA4m9a.pred)## [1] 939## [1] 939pacf(SA4m9a.resid,na.action=na.omit) #PACF for residuals, sig lags from 1-8,10,13,14,18,25
#ensure that the lags are dplyr lags
SA4m9a.ac<-update(SA4m9a,.~.+lag(SA4m9a.resid,1)+lag(SA4m9a.resid,2)+lag(SA4m9a.resid,3)+lag(SA4m9a.resid,4)+
lag(SA4m9a.resid,5)+lag(SA4m9a.resid,6)+lag(SA4m9a.resid,7)+lag(SA4m9a.resid,8)+
lag(SA4m9a.resid,10)+lag(SA4m9a.resid,13)+lag(SA4m9a.resid,14)+lag(SA4m9a.resid,18)+
lag(SA4m9a.resid,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(SA4m9a.ac)##
## Call:
## glm.nb(formula = ptbBM ~ cb9.minT + sp_ptbBMns9 + lag(SA4m9a.resid,
## 1) + lag(SA4m9a.resid, 2) + lag(SA4m9a.resid, 3) + lag(SA4m9a.resid,
## 4) + lag(SA4m9a.resid, 5) + lag(SA4m9a.resid, 6) + lag(SA4m9a.resid,
## 7) + lag(SA4m9a.resid, 8) + lag(SA4m9a.resid, 10) + lag(SA4m9a.resid,
## 13) + lag(SA4m9a.resid, 14) + lag(SA4m9a.resid, 18) + lag(SA4m9a.resid,
## 25), data = week, init.theta = 50176.13618, link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.89417 -0.59043 -0.04314 0.40785 2.49068
##
## Coefficients: (12 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -18.965817 12.708556 -1.492 0.135603
## cb9.minTv1.l1 0.042698 0.298851 0.143 0.886389
## cb9.minTv1.l2 -0.288251 0.229299 -1.257 0.208719
## cb9.minTv2.l1 1.905932 1.092643 1.744 0.081101 .
## cb9.minTv2.l2 -0.044162 0.818657 -0.054 0.956979
## cb9.minTv3.l1 0.860358 0.615733 1.397 0.162326
## cb9.minTv3.l2 0.911255 0.442281 2.060 0.039365 *
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 NA NA NA NA
## sp_ptbBMns97 NA NA NA NA
## sp_ptbBMns98 NA NA NA NA
## sp_ptbBMns99 NA NA NA NA
## sp_ptbBMns910 NA NA NA NA
## sp_ptbBMns911 -12.139619 38.451934 -0.316 0.752223
## sp_ptbBMns912 -0.095244 4.272465 -0.022 0.982215
## sp_ptbBMns913 0.142751 2.146700 0.066 0.946982
## sp_ptbBMns914 -3.003779 1.264652 -2.375 0.017540 *
## sp_ptbBMns915 1.954110 1.038886 1.881 0.059976 .
## sp_ptbBMns916 -1.348720 1.125038 -1.199 0.230597
## sp_ptbBMns917 1.316965 1.003265 1.313 0.189291
## sp_ptbBMns918 -2.188746 1.107357 -1.977 0.048093 *
## sp_ptbBMns919 1.862703 1.087029 1.714 0.086607 .
## sp_ptbBMns920 -0.532542 1.112303 -0.479 0.632100
## sp_ptbBMns921 0.544167 1.307031 0.416 0.677162
## sp_ptbBMns922 -0.916649 1.187585 -0.772 0.440198
## sp_ptbBMns923 0.237666 1.286771 0.185 0.853465
## sp_ptbBMns924 -1.127065 1.184429 -0.952 0.341316
## sp_ptbBMns925 1.166552 1.284726 0.908 0.363870
## sp_ptbBMns926 -3.694087 1.263925 -2.923 0.003470 **
## sp_ptbBMns927 2.117403 1.214657 1.743 0.081297 .
## sp_ptbBMns928 -1.960419 0.969161 -2.023 0.043094 *
## sp_ptbBMns929 0.852930 1.119696 0.762 0.446208
## sp_ptbBMns930 -1.567511 1.060637 -1.478 0.139436
## sp_ptbBMns931 1.054600 1.018302 1.036 0.300367
## sp_ptbBMns932 -1.050034 0.897846 -1.170 0.242201
## sp_ptbBMns933 -0.102382 0.905134 -0.113 0.909942
## sp_ptbBMns934 0.128282 1.059257 0.121 0.903608
## sp_ptbBMns935 -3.476481 1.178678 -2.949 0.003183 **
## sp_ptbBMns936 -0.507711 1.196145 -0.424 0.671233
## sp_ptbBMns937 -1.183904 0.882711 -1.341 0.179851
## sp_ptbBMns938 0.931726 0.792605 1.176 0.239785
## sp_ptbBMns939 -1.026507 1.060667 -0.968 0.333148
## sp_ptbBMns940 -0.331469 0.988090 -0.335 0.737275
## sp_ptbBMns941 0.135412 0.974829 0.139 0.889522
## sp_ptbBMns942 -1.604744 1.256092 -1.278 0.201401
## sp_ptbBMns943 0.253156 1.177200 0.215 0.829729
## sp_ptbBMns944 1.334617 1.634664 0.816 0.414244
## sp_ptbBMns945 0.545096 1.605266 0.340 0.734183
## sp_ptbBMns946 1.512614 1.783403 0.848 0.396348
## sp_ptbBMns947 3.092383 1.968043 1.571 0.116113
## sp_ptbBMns948 -0.379744 2.415499 -0.157 0.875078
## sp_ptbBMns949 2.466945 2.161419 1.141 0.253722
## sp_ptbBMns950 0.280503 2.221561 0.126 0.899523
## sp_ptbBMns951 1.264298 2.053637 0.616 0.538133
## sp_ptbBMns952 -0.648296 2.134739 -0.304 0.761365
## sp_ptbBMns953 0.960630 1.589996 0.604 0.545730
## sp_ptbBMns954 -3.419690 1.622771 -2.107 0.035090 *
## sp_ptbBMns955 1.153917 1.370019 0.842 0.399641
## sp_ptbBMns956 -2.657094 1.178899 -2.254 0.024204 *
## sp_ptbBMns957 0.286405 0.876754 0.327 0.743921
## sp_ptbBMns958 -1.514465 0.926053 -1.635 0.101966
## sp_ptbBMns959 -0.059577 1.066805 -0.056 0.955465
## sp_ptbBMns960 -1.991878 0.919092 -2.167 0.030218 *
## sp_ptbBMns961 0.542858 0.954292 0.569 0.569452
## sp_ptbBMns962 -2.499885 1.047187 -2.387 0.016975 *
## sp_ptbBMns963 0.639583 0.943612 0.678 0.497897
## sp_ptbBMns964 0.001834 1.103583 0.002 0.998674
## sp_ptbBMns965 -0.503167 1.070545 -0.470 0.638348
## sp_ptbBMns966 0.704757 1.135823 0.620 0.534941
## sp_ptbBMns967 0.377396 1.369631 0.276 0.782897
## sp_ptbBMns968 -0.861183 1.320640 -0.652 0.514340
## sp_ptbBMns969 1.444518 1.357815 1.064 0.287394
## sp_ptbBMns970 -1.621899 1.267203 -1.280 0.200578
## sp_ptbBMns971 -0.887922 1.350725 -0.657 0.510945
## sp_ptbBMns972 -0.987549 1.322830 -0.747 0.455339
## sp_ptbBMns973 0.433134 1.198431 0.361 0.717787
## sp_ptbBMns974 -0.771728 1.016786 -0.759 0.447860
## sp_ptbBMns975 0.089725 1.073516 0.084 0.933390
## sp_ptbBMns976 -0.830278 1.017195 -0.816 0.414361
## sp_ptbBMns977 -0.305876 0.949635 -0.322 0.747378
## sp_ptbBMns978 -0.556447 0.804544 -0.692 0.489169
## sp_ptbBMns979 -0.642455 0.881464 -0.729 0.466094
## sp_ptbBMns980 0.148225 0.980678 0.151 0.879861
## sp_ptbBMns981 -1.522755 1.084676 -1.404 0.160355
## sp_ptbBMns982 2.006476 1.329212 1.510 0.131165
## sp_ptbBMns983 0.764143 1.422705 0.537 0.591195
## sp_ptbBMns984 1.658282 1.599535 1.037 0.299863
## sp_ptbBMns985 1.324173 1.459628 0.907 0.364302
## sp_ptbBMns986 0.451127 1.428831 0.316 0.752206
## sp_ptbBMns987 0.485149 1.396997 0.347 0.728381
## sp_ptbBMns988 -0.681164 1.454214 -0.468 0.639494
## sp_ptbBMns989 -0.531261 1.359753 -0.391 0.696016
## sp_ptbBMns990 -2.815512 1.614774 -1.744 0.081230 .
## sp_ptbBMns991 -1.025986 1.208953 -0.849 0.396073
## sp_ptbBMns992 -2.207810 1.091183 -2.023 0.043040 *
## sp_ptbBMns993 -0.434673 0.981833 -0.443 0.657972
## sp_ptbBMns994 -2.547986 0.954278 -2.670 0.007584 **
## sp_ptbBMns995 1.139960 0.891821 1.278 0.201166
## sp_ptbBMns996 -2.639737 1.096995 -2.406 0.016113 *
## sp_ptbBMns997 -0.292164 0.966719 -0.302 0.762482
## sp_ptbBMns998 0.108307 0.964658 0.112 0.910606
## sp_ptbBMns999 -2.540814 0.902000 -2.817 0.004849 **
## sp_ptbBMns9100 2.085444 1.002867 2.079 0.037573 *
## sp_ptbBMns9101 -0.571993 1.000249 -0.572 0.567423
## sp_ptbBMns9102 2.343383 1.103835 2.123 0.033758 *
## sp_ptbBMns9103 -0.913958 1.083503 -0.844 0.398937
## sp_ptbBMns9104 0.233789 1.063532 0.220 0.826009
## sp_ptbBMns9105 -0.713628 1.063137 -0.671 0.502063
## sp_ptbBMns9106 1.510566 1.050858 1.437 0.150587
## sp_ptbBMns9107 -4.216047 1.188696 -3.547 0.000390 ***
## sp_ptbBMns9108 2.922010 1.217595 2.400 0.016403 *
## sp_ptbBMns9109 -2.121077 1.421578 -1.492 0.135684
## sp_ptbBMns9110 0.740314 1.581911 0.468 0.639794
## sp_ptbBMns9111 1.601086 1.559169 1.027 0.304475
## sp_ptbBMns9112 1.799927 1.578597 1.140 0.254200
## sp_ptbBMns9113 0.921874 1.521454 0.606 0.544570
## sp_ptbBMns9114 -0.750635 1.543590 -0.486 0.626760
## sp_ptbBMns9115 -1.347845 1.567854 -0.860 0.389968
## sp_ptbBMns9116 -2.004590 1.587654 -1.263 0.206729
## sp_ptbBMns9117 0.381224 1.317644 0.289 0.772335
## sp_ptbBMns9118 -0.653738 1.140032 -0.573 0.566348
## sp_ptbBMns9119 2.482485 1.209847 2.052 0.040179 *
## sp_ptbBMns9120 0.747926 1.337580 0.559 0.576050
## sp_ptbBMns9121 2.407968 1.376327 1.750 0.080194 .
## sp_ptbBMns9122 -1.590661 1.440266 -1.104 0.269410
## sp_ptbBMns9123 3.216113 1.334470 2.410 0.015951 *
## sp_ptbBMns9124 -1.020322 1.437827 -0.710 0.477935
## sp_ptbBMns9125 1.049062 1.590923 0.659 0.509636
## sp_ptbBMns9126 -5.324582 1.566639 -3.399 0.000677 ***
## sp_ptbBMns9127 2.065843 1.650372 1.252 0.210663
## sp_ptbBMns9128 -1.966180 1.576272 -1.247 0.212265
## sp_ptbBMns9129 4.317141 1.596894 2.703 0.006862 **
## sp_ptbBMns9130 1.263012 1.894213 0.667 0.504917
## sp_ptbBMns9131 1.947628 1.642833 1.186 0.235808
## sp_ptbBMns9132 0.924659 1.853327 0.499 0.617837
## sp_ptbBMns9133 -0.899619 1.765313 -0.510 0.610326
## sp_ptbBMns9134 0.730431 1.848765 0.395 0.692775
## sp_ptbBMns9135 0.450162 2.488523 0.181 0.856450
## sp_ptbBMns9136 1.481120 3.466953 0.427 0.669226
## sp_ptbBMns9137 -1.976795 4.094256 -0.483 0.629222
## sp_ptbBMns9138 1.870856 4.903506 0.382 0.702807
## sp_ptbBMns9139 -0.411540 4.120826 -0.100 0.920449
## sp_ptbBMns9140 -0.134206 4.523789 -0.030 0.976333
## sp_ptbBMns9141 -4.116837 4.203150 -0.979 0.327350
## sp_ptbBMns9142 -5.180346 4.324920 -1.198 0.230999
## sp_ptbBMns9143 -7.707475 4.568402 -1.687 0.091579 .
## sp_ptbBMns9144 -9.155379 4.097944 -2.234 0.025474 *
## sp_ptbBMns9145 -5.937373 3.131043 -1.896 0.057921 .
## sp_ptbBMns9146 -3.749743 2.177761 -1.722 0.085100 .
## sp_ptbBMns9147 0.902221 1.681791 0.536 0.591638
## sp_ptbBMns9148 -2.081104 1.662726 -1.252 0.210708
## sp_ptbBMns9149 1.735947 1.605857 1.081 0.279693
## sp_ptbBMns9150 1.199533 1.289328 0.930 0.352187
## sp_ptbBMns9151 -0.408581 1.293483 -0.316 0.752096
## sp_ptbBMns9152 0.816103 0.984190 0.829 0.406984
## sp_ptbBMns9153 -0.077527 1.049670 -0.074 0.941123
## sp_ptbBMns9154 -3.002558 0.996590 -3.013 0.002588 **
## sp_ptbBMns9155 0.335386 0.885869 0.379 0.704988
## sp_ptbBMns9156 -1.641902 0.891910 -1.841 0.065639 .
## sp_ptbBMns9157 0.804385 0.669970 1.201 0.229895
## sp_ptbBMns9158 0.076465 0.861543 0.089 0.929278
## sp_ptbBMns9159 0.534181 0.685612 0.779 0.435903
## sp_ptbBMns9160 1.123958 0.897474 1.252 0.210440
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## lag(SA4m9a.resid, 1) -0.553774 0.034096 -16.242 < 2e-16 ***
## lag(SA4m9a.resid, 2) -0.619100 0.040761 -15.189 < 2e-16 ***
## lag(SA4m9a.resid, 3) -0.719466 0.045209 -15.914 < 2e-16 ***
## lag(SA4m9a.resid, 4) -0.682643 0.047927 -14.243 < 2e-16 ***
## lag(SA4m9a.resid, 5) -0.609254 0.046681 -13.051 < 2e-16 ***
## lag(SA4m9a.resid, 6) -0.475135 0.043667 -10.881 < 2e-16 ***
## lag(SA4m9a.resid, 7) -0.340978 0.038294 -8.904 < 2e-16 ***
## lag(SA4m9a.resid, 8) -0.213168 0.032166 -6.627 3.42e-11 ***
## lag(SA4m9a.resid, 10) -0.019507 0.026333 -0.741 0.458832
## lag(SA4m9a.resid, 13) 0.065844 0.026789 2.458 0.013977 *
## lag(SA4m9a.resid, 14) 0.053737 0.026590 2.021 0.043287 *
## lag(SA4m9a.resid, 18) -0.055298 0.025182 -2.196 0.028097 *
## lag(SA4m9a.resid, 25) -0.026175 0.025086 -1.043 0.296756
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(50176.14) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 468.22 on 692 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2792.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 50176
## Std. Err.: 188217
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2450.144SA4m9a.resid_ac<-residuals(SA4m9a.ac, type="deviance")
SA4m9a.pred_ac<-predict(SA4m9a.ac, type="response")
pacf(SA4m9a.resid_ac,na.action = na.omit) 
length(SA4m9a.pred_ac); length(SA4m9a.resid_ac)## [1] 939## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m9a.pred,lwd=1, col="blue")
plot(week$time,SA4m9a.resid)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m9a.pred, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,SA4m9a.pred_ac,lwd=1, col="blue")
plot(week$time,SA4m9a.resid_ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(SA4m9a.pred_ac, week$ptbBM)
abline(coef = c(0,1), col="red")
#plotting the dose response and slices
pred.SA4m9a <- crosspred(cb9.minT, SA4m9a.ac, cen = 24.0, by=0.1,cumul=TRUE)
###final SA #4 model #####
mod_fullSA4bm<-glm.nb(ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun + cb1.avgWindSp + sp_ptbBMns9, data = week)## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullSA4bm)##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBMns9, data = week, init.theta = 30471.03757,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.84821 -0.76844 -0.08142 0.57790 2.24418
##
## Coefficients: (7 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.296e+00 2.277e+01 -0.145 0.88492
## cb3.RFv1.l1 -1.526e-01 3.231e-01 -0.472 0.63685
## cb3.RFv1.l2 2.368e-01 2.426e-01 0.976 0.32902
## cb3.RFv2.l1 -5.855e-02 5.217e-01 -0.112 0.91065
## cb3.RFv2.l2 4.336e-03 3.970e-01 0.011 0.99128
## cb3.RFv3.l1 1.188e-01 8.125e-01 0.146 0.88373
## cb3.RFv3.l2 -4.948e-01 6.404e-01 -0.773 0.43975
## cb9.minTv1.l1 -1.073e-01 3.231e-01 -0.332 0.73989
## cb9.minTv1.l2 -1.151e-01 2.549e-01 -0.452 0.65162
## cb9.minTv2.l1 3.301e-01 1.109e+00 0.298 0.76590
## cb9.minTv2.l2 -3.867e-01 8.821e-01 -0.438 0.66111
## cb9.minTv3.l1 6.390e-01 6.557e-01 0.975 0.32978
## cb9.minTv3.l2 7.947e-01 4.764e-01 1.668 0.09525 .
## cb5.minRHv1.l1 1.693e-01 3.328e-01 0.509 0.61101
## cb5.minRHv1.l2 8.210e-02 2.197e-01 0.374 0.70871
## cb5.minRHv2.l1 -1.611e+00 1.103e+00 -1.460 0.14424
## cb5.minRHv2.l2 -7.693e-02 8.404e-01 -0.092 0.92706
## cb5.minRHv3.l1 -9.266e-01 7.715e-01 -1.201 0.22973
## cb5.minRHv3.l2 -3.750e-01 6.183e-01 -0.607 0.54416
## cb2.sunv1.l1 1.395e-01 2.631e-01 0.530 0.59596
## cb2.sunv1.l2 8.456e-03 1.849e-01 0.046 0.96353
## cb2.sunv2.l1 1.194e+00 1.046e+00 1.142 0.25355
## cb2.sunv2.l2 1.158e+00 7.252e-01 1.596 0.11044
## cb2.sunv3.l1 4.705e-01 4.074e-01 1.155 0.24810
## cb2.sunv3.l2 6.325e-01 2.769e-01 2.284 0.02236 *
## cb1.avgWindSpv1.l1 3.798e-01 3.614e-01 1.051 0.29335
## cb1.avgWindSpv1.l2 6.282e-01 2.638e-01 2.381 0.01727 *
## cb1.avgWindSpv2.l1 6.302e-01 8.362e-01 0.754 0.45105
## cb1.avgWindSpv2.l2 6.530e-01 6.004e-01 1.088 0.27670
## cb1.avgWindSpv3.l1 1.164e-01 1.000e+00 0.116 0.90738
## cb1.avgWindSpv3.l2 -1.082e-01 6.439e-01 -0.168 0.86650
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 -2.607e+06 1.318e+06 -1.979 0.04787 *
## sp_ptbBMns97 4.081e+01 1.621e+01 2.517 0.01183 *
## sp_ptbBMns98 -9.407e+00 3.112e+00 -3.023 0.00251 **
## sp_ptbBMns99 -4.761e-01 2.611e+00 -0.182 0.85532
## sp_ptbBMns910 -4.725e+00 2.684e+00 -1.760 0.07838 .
## sp_ptbBMns911 -2.241e+00 2.599e+00 -0.862 0.38854
## sp_ptbBMns912 -2.697e+00 2.311e+00 -1.167 0.24309
## sp_ptbBMns913 -4.508e+00 2.324e+00 -1.940 0.05238 .
## sp_ptbBMns914 -3.318e+00 2.131e+00 -1.557 0.11941
## sp_ptbBMns915 -1.550e+00 2.055e+00 -0.754 0.45074
## sp_ptbBMns916 -4.129e+00 1.993e+00 -2.072 0.03828 *
## sp_ptbBMns917 -1.512e+00 2.291e+00 -0.660 0.50935
## sp_ptbBMns918 -4.185e+00 1.977e+00 -2.117 0.03425 *
## sp_ptbBMns919 -1.410e+00 1.945e+00 -0.725 0.46850
## sp_ptbBMns920 -1.282e+00 1.836e+00 -0.698 0.48499
## sp_ptbBMns921 -2.880e-01 2.032e+00 -0.142 0.88728
## sp_ptbBMns922 -1.834e+00 1.747e+00 -1.050 0.29384
## sp_ptbBMns923 -7.884e-01 1.813e+00 -0.435 0.66374
## sp_ptbBMns924 -1.505e+00 1.735e+00 -0.867 0.38576
## sp_ptbBMns925 -2.328e+00 1.779e+00 -1.309 0.19061
## sp_ptbBMns926 -3.133e+00 1.843e+00 -1.700 0.08913 .
## sp_ptbBMns927 -1.087e+00 2.003e+00 -0.543 0.58727
## sp_ptbBMns928 -2.283e+00 2.004e+00 -1.139 0.25452
## sp_ptbBMns929 -2.910e+00 1.861e+00 -1.564 0.11792
## sp_ptbBMns930 -2.785e+00 1.798e+00 -1.549 0.12135
## sp_ptbBMns931 -2.857e+00 1.659e+00 -1.722 0.08501 .
## sp_ptbBMns932 -2.949e+00 1.445e+00 -2.040 0.04132 *
## sp_ptbBMns933 -1.799e+00 1.476e+00 -1.219 0.22273
## sp_ptbBMns934 -3.365e+00 1.617e+00 -2.080 0.03749 *
## sp_ptbBMns935 -4.536e+00 1.857e+00 -2.443 0.01456 *
## sp_ptbBMns936 -2.562e+00 1.801e+00 -1.422 0.15489
## sp_ptbBMns937 -1.352e+00 2.013e+00 -0.672 0.50165
## sp_ptbBMns938 -1.889e+00 1.782e+00 -1.060 0.28923
## sp_ptbBMns939 -1.968e+00 1.870e+00 -1.052 0.29270
## sp_ptbBMns940 -1.861e+00 1.841e+00 -1.011 0.31212
## sp_ptbBMns941 -2.441e+00 1.984e+00 -1.230 0.21866
## sp_ptbBMns942 -3.518e+00 2.143e+00 -1.642 0.10062
## sp_ptbBMns943 -1.427e+00 2.036e+00 -0.701 0.48355
## sp_ptbBMns944 -1.660e+00 2.500e+00 -0.664 0.50665
## sp_ptbBMns945 -5.579e-01 2.492e+00 -0.224 0.82289
## sp_ptbBMns946 1.588e+00 2.806e+00 0.566 0.57141
## sp_ptbBMns947 1.311e+00 2.804e+00 0.467 0.64019
## sp_ptbBMns948 -9.557e-01 3.079e+00 -0.310 0.75624
## sp_ptbBMns949 -6.422e-01 2.809e+00 -0.229 0.81914
## sp_ptbBMns950 -1.111e+00 2.979e+00 -0.373 0.70934
## sp_ptbBMns951 -2.009e+00 2.766e+00 -0.726 0.46765
## sp_ptbBMns952 -1.483e+00 2.812e+00 -0.527 0.59797
## sp_ptbBMns953 -2.175e+00 2.518e+00 -0.864 0.38781
## sp_ptbBMns954 -4.587e+00 2.494e+00 -1.839 0.06588 .
## sp_ptbBMns955 -7.911e-01 2.290e+00 -0.345 0.72978
## sp_ptbBMns956 -4.476e+00 2.381e+00 -1.880 0.06011 .
## sp_ptbBMns957 -7.836e-01 2.123e+00 -0.369 0.71211
## sp_ptbBMns958 -2.941e+00 2.189e+00 -1.344 0.17903
## sp_ptbBMns959 -2.207e+00 2.181e+00 -1.012 0.31155
## sp_ptbBMns960 -3.449e+00 2.268e+00 -1.521 0.12827
## sp_ptbBMns961 -2.784e+00 2.313e+00 -1.204 0.22871
## sp_ptbBMns962 -3.475e+00 2.438e+00 -1.425 0.15405
## sp_ptbBMns963 -2.666e+00 2.501e+00 -1.066 0.28645
## sp_ptbBMns964 -3.202e+00 2.489e+00 -1.286 0.19832
## sp_ptbBMns965 -3.153e+00 2.207e+00 -1.429 0.15298
## sp_ptbBMns966 -2.085e+00 2.261e+00 -0.922 0.35636
## sp_ptbBMns967 -3.432e+00 2.229e+00 -1.539 0.12370
## sp_ptbBMns968 -2.118e+00 2.087e+00 -1.015 0.31021
## sp_ptbBMns969 -2.244e+00 2.118e+00 -1.060 0.28923
## sp_ptbBMns970 -1.818e+00 1.988e+00 -0.914 0.36057
## sp_ptbBMns971 -1.554e+00 1.971e+00 -0.789 0.43029
## sp_ptbBMns972 -1.955e+00 2.332e+00 -0.838 0.40185
## sp_ptbBMns973 -1.829e+00 2.127e+00 -0.860 0.38977
## sp_ptbBMns974 -1.330e+00 1.964e+00 -0.677 0.49836
## sp_ptbBMns975 -1.689e+00 1.855e+00 -0.910 0.36258
## sp_ptbBMns976 -3.066e+00 1.767e+00 -1.735 0.08269 .
## sp_ptbBMns977 -3.148e+00 1.913e+00 -1.646 0.09982 .
## sp_ptbBMns978 -1.719e+00 1.822e+00 -0.944 0.34541
## sp_ptbBMns979 -2.029e+00 1.776e+00 -1.143 0.25324
## sp_ptbBMns980 -4.471e-01 2.115e+00 -0.211 0.83259
## sp_ptbBMns981 -4.447e+00 2.288e+00 -1.943 0.05197 .
## sp_ptbBMns982 -1.022e+00 2.720e+00 -0.376 0.70707
## sp_ptbBMns983 -3.070e+00 2.772e+00 -1.107 0.26808
## sp_ptbBMns984 -7.179e-01 2.889e+00 -0.248 0.80375
## sp_ptbBMns985 -3.352e+00 2.832e+00 -1.184 0.23646
## sp_ptbBMns986 -3.731e+00 2.876e+00 -1.297 0.19457
## sp_ptbBMns987 -3.404e+00 2.791e+00 -1.220 0.22253
## sp_ptbBMns988 -5.656e+00 2.971e+00 -1.904 0.05692 .
## sp_ptbBMns989 -4.229e+00 2.913e+00 -1.452 0.14659
## sp_ptbBMns990 -8.505e+00 2.855e+00 -2.979 0.00289 **
## sp_ptbBMns991 -5.170e+00 2.710e+00 -1.908 0.05641 .
## sp_ptbBMns992 -5.355e+00 2.547e+00 -2.103 0.03549 *
## sp_ptbBMns993 -2.310e+00 2.363e+00 -0.977 0.32838
## sp_ptbBMns994 -5.370e+00 2.386e+00 -2.251 0.02441 *
## sp_ptbBMns995 -5.531e-01 2.220e+00 -0.249 0.80321
## sp_ptbBMns996 -4.336e+00 2.235e+00 -1.940 0.05239 .
## sp_ptbBMns997 -2.721e-01 2.239e+00 -0.122 0.90329
## sp_ptbBMns998 -2.522e+00 2.261e+00 -1.115 0.26465
## sp_ptbBMns999 -2.125e+00 2.337e+00 -0.909 0.36327
## sp_ptbBMns9100 -2.159e+00 2.550e+00 -0.846 0.39730
## sp_ptbBMns9101 -1.914e+00 2.446e+00 -0.782 0.43398
## sp_ptbBMns9102 -2.132e+00 2.496e+00 -0.854 0.39298
## sp_ptbBMns9103 -2.728e+00 2.433e+00 -1.122 0.26206
## sp_ptbBMns9104 -4.218e+00 2.548e+00 -1.655 0.09788 .
## sp_ptbBMns9105 -3.481e+00 2.410e+00 -1.444 0.14864
## sp_ptbBMns9106 -4.828e+00 2.372e+00 -2.036 0.04179 *
## sp_ptbBMns9107 -3.614e+00 2.361e+00 -1.531 0.12586
## sp_ptbBMns9108 -3.663e+00 2.621e+00 -1.397 0.16227
## sp_ptbBMns9109 -4.746e+00 2.860e+00 -1.659 0.09707 .
## sp_ptbBMns9110 -5.442e+00 2.955e+00 -1.842 0.06552 .
## sp_ptbBMns9111 -3.618e+00 2.844e+00 -1.272 0.20331
## sp_ptbBMns9112 -3.503e+00 2.765e+00 -1.267 0.20526
## sp_ptbBMns9113 -4.843e+00 2.634e+00 -1.839 0.06591 .
## sp_ptbBMns9114 -4.993e+00 2.781e+00 -1.795 0.07260 .
## sp_ptbBMns9115 -7.199e+00 2.746e+00 -2.621 0.00876 **
## sp_ptbBMns9116 -5.326e+00 2.886e+00 -1.845 0.06500 .
## sp_ptbBMns9117 -5.769e+00 2.888e+00 -1.998 0.04575 *
## sp_ptbBMns9118 -4.099e+00 2.951e+00 -1.389 0.16482
## sp_ptbBMns9119 -2.362e+00 2.785e+00 -0.848 0.39638
## sp_ptbBMns9120 -2.275e+00 2.893e+00 -0.787 0.43157
## sp_ptbBMns9121 -1.895e+00 2.704e+00 -0.701 0.48338
## sp_ptbBMns9122 -3.191e+00 2.797e+00 -1.141 0.25381
## sp_ptbBMns9123 -1.619e+00 2.736e+00 -0.592 0.55400
## sp_ptbBMns9124 -2.966e+00 2.844e+00 -1.043 0.29702
## sp_ptbBMns9125 -1.214e+00 3.040e+00 -0.399 0.68978
## sp_ptbBMns9126 -6.804e+00 3.172e+00 -2.145 0.03195 *
## sp_ptbBMns9127 -3.985e+00 3.243e+00 -1.229 0.21921
## sp_ptbBMns9128 -5.199e+00 3.262e+00 -1.594 0.11104
## sp_ptbBMns9129 -1.852e+00 3.212e+00 -0.577 0.56413
## sp_ptbBMns9130 -3.431e+00 3.177e+00 -1.080 0.28021
## sp_ptbBMns9131 -3.524e+00 2.850e+00 -1.237 0.21621
## sp_ptbBMns9132 -4.968e+00 2.843e+00 -1.747 0.08057 .
## sp_ptbBMns9133 -5.264e+00 2.697e+00 -1.951 0.05100 .
## sp_ptbBMns9134 -3.982e+00 2.667e+00 -1.493 0.13543
## sp_ptbBMns9135 -5.507e+00 3.192e+00 -1.725 0.08449 .
## sp_ptbBMns9136 -4.394e+00 4.297e+00 -1.023 0.30651
## sp_ptbBMns9137 -5.360e+00 4.725e+00 -1.135 0.25658
## sp_ptbBMns9138 -2.379e+00 5.385e+00 -0.442 0.65871
## sp_ptbBMns9139 -4.822e+00 4.569e+00 -1.055 0.29128
## sp_ptbBMns9140 -5.513e+00 5.026e+00 -1.097 0.27269
## sp_ptbBMns9141 -9.182e+00 4.738e+00 -1.938 0.05261 .
## sp_ptbBMns9142 -9.504e+00 4.961e+00 -1.916 0.05538 .
## sp_ptbBMns9143 -1.295e+01 5.168e+00 -2.506 0.01220 *
## sp_ptbBMns9144 -1.076e+01 4.612e+00 -2.332 0.01968 *
## sp_ptbBMns9145 -7.512e+00 3.759e+00 -1.998 0.04568 *
## sp_ptbBMns9146 -4.764e+00 2.728e+00 -1.747 0.08072 .
## sp_ptbBMns9147 -2.760e-01 2.217e+00 -0.125 0.90092
## sp_ptbBMns9148 -4.058e+00 2.106e+00 -1.927 0.05396 .
## sp_ptbBMns9149 -1.006e-01 1.936e+00 -0.052 0.95856
## sp_ptbBMns9150 -1.046e+00 1.767e+00 -0.592 0.55366
## sp_ptbBMns9151 -2.844e+00 1.654e+00 -1.719 0.08556 .
## sp_ptbBMns9152 4.884e-01 1.473e+00 0.332 0.74018
## sp_ptbBMns9153 -2.468e+00 1.560e+00 -1.582 0.11364
## sp_ptbBMns9154 -1.870e+00 1.734e+00 -1.079 0.28072
## sp_ptbBMns9155 -6.397e-01 1.427e+00 -0.448 0.65397
## sp_ptbBMns9156 -1.179e+00 1.449e+00 -0.814 0.41586
## sp_ptbBMns9157 5.553e-01 1.062e+00 0.523 0.60110
## sp_ptbBMns9158 -4.888e-01 1.190e+00 -0.411 0.68132
## sp_ptbBMns9159 1.231e+00 8.676e-01 1.419 0.15588
## sp_ptbBMns9160 1.066e+00 9.675e-01 1.102 0.27060
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(30471.04) family taken to be 1)
##
## Null deviance: 1101.29 on 886 degrees of freedom
## Residual deviance: 855.84 on 701 degrees of freedom
## (52 observations deleted due to missingness)
## AIC: 3262.7
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 30471
## Std. Err.: 155130
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2888.682pred.fullbm<-predict(mod_fullSA4bm, type="response") #fitted
resid.fullbm<-residuals(mod_fullSA4bm, type="deviance") #residuals deviance
length(pred.fullbm)## [1] 939length(week$ptbBM)## [1] 939length(resid.fullbm)## [1] 939pacf(resid.fullbm,na.action=na.omit) #PACF for residuals, sig lags from 1-8, 13,14,18,25
#ensure that the lags are dplyr lags
mod_fullSA4bm.ac<-update(mod_fullSA4bm,.~.+lag(resid.fullbm,1)+lag(resid.fullbm,2)+lag(resid.fullbm,3)+lag(resid.fullbm,4)+
lag(resid.fullbm,5)+lag(resid.fullbm,6)+lag(resid.fullbm,7)+lag(resid.fullbm,8)+
lag(resid.fullbm,13)+lag(resid.fullbm,14)+lag(resid.fullbm,18)+lag(resid.fullbm,25))## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reached
## Warning in theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace =
## control$trace > : iteration limit reachedsummary(mod_fullSA4bm.ac)##aic=3045##
## Call:
## glm.nb(formula = ptbBM ~ cb3.RF + cb9.minT + cb5.minRH + cb2.sun +
## cb1.avgWindSp + sp_ptbBMns9 + lag(resid.fullbm, 1) + lag(resid.fullbm,
## 2) + lag(resid.fullbm, 3) + lag(resid.fullbm, 4) + lag(resid.fullbm,
## 5) + lag(resid.fullbm, 6) + lag(resid.fullbm, 7) + lag(resid.fullbm,
## 8) + lag(resid.fullbm, 13) + lag(resid.fullbm, 14) + lag(resid.fullbm,
## 18) + lag(resid.fullbm, 25), data = week, init.theta = 50488.68859,
## link = log)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.87885 -0.57786 -0.05941 0.38893 2.46344
##
## Coefficients: (12 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -11.30911 24.25267 -0.466 0.64100
## cb3.RFv1.l1 -0.19657 0.34504 -0.570 0.56887
## cb3.RFv1.l2 0.05467 0.25616 0.213 0.83100
## cb3.RFv2.l1 -0.73378 0.55307 -1.327 0.18460
## cb3.RFv2.l2 0.11113 0.42444 0.262 0.79345
## cb3.RFv3.l1 -0.17843 0.87425 -0.204 0.83827
## cb3.RFv3.l2 -0.32989 0.68874 -0.479 0.63196
## cb9.minTv1.l1 -0.29060 0.33158 -0.876 0.38080
## cb9.minTv1.l2 -0.57532 0.26733 -2.152 0.03139 *
## cb9.minTv2.l1 1.27847 1.16764 1.095 0.27355
## cb9.minTv2.l2 -0.87078 0.91203 -0.955 0.33969
## cb9.minTv3.l1 0.50584 0.66996 0.755 0.45023
## cb9.minTv3.l2 0.67780 0.49253 1.376 0.16877
## cb5.minRHv1.l1 0.52249 0.34845 1.499 0.13375
## cb5.minRHv1.l2 -0.04863 0.22828 -0.213 0.83131
## cb5.minRHv2.l1 -1.80463 1.15231 -1.566 0.11733
## cb5.minRHv2.l2 -0.49089 0.88486 -0.555 0.57905
## cb5.minRHv3.l1 -0.74076 0.82397 -0.899 0.36864
## cb5.minRHv3.l2 -0.18071 0.64912 -0.278 0.78072
## cb2.sunv1.l1 0.54159 0.27529 1.967 0.04914 *
## cb2.sunv1.l2 0.28949 0.19252 1.504 0.13266
## cb2.sunv2.l1 0.80684 1.09366 0.738 0.46067
## cb2.sunv2.l2 2.30718 0.76628 3.011 0.00260 **
## cb2.sunv3.l1 0.12663 0.42498 0.298 0.76573
## cb2.sunv3.l2 0.74208 0.29074 2.552 0.01070 *
## cb1.avgWindSpv1.l1 0.15709 0.37948 0.414 0.67890
## cb1.avgWindSpv1.l2 0.22463 0.27885 0.806 0.42049
## cb1.avgWindSpv2.l1 0.51731 0.90357 0.573 0.56697
## cb1.avgWindSpv2.l2 0.82739 0.63857 1.296 0.19508
## cb1.avgWindSpv3.l1 -0.34728 1.06604 -0.326 0.74460
## cb1.avgWindSpv3.l2 0.61802 0.70050 0.882 0.37764
## sp_ptbBMns91 NA NA NA NA
## sp_ptbBMns92 NA NA NA NA
## sp_ptbBMns93 NA NA NA NA
## sp_ptbBMns94 NA NA NA NA
## sp_ptbBMns95 NA NA NA NA
## sp_ptbBMns96 NA NA NA NA
## sp_ptbBMns97 NA NA NA NA
## sp_ptbBMns98 NA NA NA NA
## sp_ptbBMns99 NA NA NA NA
## sp_ptbBMns910 NA NA NA NA
## sp_ptbBMns911 -25.88834 39.36677 -0.658 0.51078
## sp_ptbBMns912 -1.38911 4.82719 -0.288 0.77352
## sp_ptbBMns913 -1.53181 2.94289 -0.521 0.60271
## sp_ptbBMns914 -5.15615 2.28658 -2.255 0.02414 *
## sp_ptbBMns915 1.41004 2.17290 0.649 0.51639
## sp_ptbBMns916 -3.12455 2.11279 -1.479 0.13917
## sp_ptbBMns917 0.18464 2.37296 0.078 0.93798
## sp_ptbBMns918 -3.20268 2.06694 -1.549 0.12127
## sp_ptbBMns919 1.34182 2.07511 0.647 0.51787
## sp_ptbBMns920 0.72385 1.93050 0.375 0.70770
## sp_ptbBMns921 -0.19245 2.05945 -0.093 0.92555
## sp_ptbBMns922 -0.99414 1.88363 -0.528 0.59765
## sp_ptbBMns923 0.89099 1.88457 0.473 0.63637
## sp_ptbBMns924 -0.22997 1.79461 -0.128 0.89803
## sp_ptbBMns925 2.38502 1.83017 1.303 0.19252
## sp_ptbBMns926 -3.37216 1.96718 -1.714 0.08649 .
## sp_ptbBMns927 4.05608 2.12429 1.909 0.05621 .
## sp_ptbBMns928 -2.44548 2.11645 -1.155 0.24790
## sp_ptbBMns929 -0.30415 1.91919 -0.158 0.87408
## sp_ptbBMns930 -2.82499 1.86108 -1.518 0.12903
## sp_ptbBMns931 -1.44310 1.75619 -0.822 0.41123
## sp_ptbBMns932 -2.02485 1.54447 -1.311 0.18985
## sp_ptbBMns933 -1.77945 1.56034 -1.140 0.25411
## sp_ptbBMns934 -1.59591 1.79546 -0.889 0.37408
## sp_ptbBMns935 -5.97247 1.90548 -3.134 0.00172 **
## sp_ptbBMns936 -3.04624 1.93354 -1.575 0.11515
## sp_ptbBMns937 -3.45029 2.13229 -1.618 0.10564
## sp_ptbBMns938 -2.14586 1.86191 -1.153 0.24911
## sp_ptbBMns939 -3.78757 2.00337 -1.891 0.05868 .
## sp_ptbBMns940 -2.40141 1.88326 -1.275 0.20226
## sp_ptbBMns941 -1.58734 2.03302 -0.781 0.43493
## sp_ptbBMns942 -2.70463 2.20448 -1.227 0.21987
## sp_ptbBMns943 0.07429 2.12836 0.035 0.97215
## sp_ptbBMns944 1.99915 2.61371 0.765 0.44435
## sp_ptbBMns945 3.51296 2.61464 1.344 0.17909
## sp_ptbBMns946 3.46808 2.92089 1.187 0.23509
## sp_ptbBMns947 4.55365 2.93881 1.549 0.12127
## sp_ptbBMns948 -1.29763 3.23033 -0.402 0.68790
## sp_ptbBMns949 0.13708 2.94343 0.047 0.96285
## sp_ptbBMns950 -0.69009 3.11114 -0.222 0.82446
## sp_ptbBMns951 -1.23703 2.90208 -0.426 0.66992
## sp_ptbBMns952 -2.64770 2.97435 -0.890 0.37337
## sp_ptbBMns953 -2.37066 2.64713 -0.896 0.37049
## sp_ptbBMns954 -6.79978 2.68799 -2.530 0.01142 *
## sp_ptbBMns955 -1.89444 2.43374 -0.778 0.43633
## sp_ptbBMns956 -5.26916 2.45836 -2.143 0.03208 *
## sp_ptbBMns957 -1.85322 2.23215 -0.830 0.40640
## sp_ptbBMns958 -2.28562 2.30896 -0.990 0.32223
## sp_ptbBMns959 -1.37009 2.31096 -0.593 0.55327
## sp_ptbBMns960 -1.94185 2.34829 -0.827 0.40828
## sp_ptbBMns961 -0.99603 2.41207 -0.413 0.67965
## sp_ptbBMns962 -2.48945 2.53609 -0.982 0.32629
## sp_ptbBMns963 -0.87463 2.59403 -0.337 0.73599
## sp_ptbBMns964 -0.26013 2.56326 -0.101 0.91917
## sp_ptbBMns965 -1.85369 2.29725 -0.807 0.41971
## sp_ptbBMns966 0.26930 2.36072 0.114 0.90918
## sp_ptbBMns967 -0.87315 2.30516 -0.379 0.70485
## sp_ptbBMns968 -0.16227 2.14151 -0.076 0.93960
## sp_ptbBMns969 0.88286 2.18005 0.405 0.68550
## sp_ptbBMns970 -1.96867 2.05423 -0.958 0.33789
## sp_ptbBMns971 -1.58899 2.07558 -0.766 0.44394
## sp_ptbBMns972 -2.51003 2.45676 -1.022 0.30693
## sp_ptbBMns973 -2.21711 2.21398 -1.001 0.31663
## sp_ptbBMns974 -2.54224 2.05670 -1.236 0.21643
## sp_ptbBMns975 -3.22945 1.91892 -1.683 0.09238 .
## sp_ptbBMns976 -2.45461 1.83661 -1.336 0.18139
## sp_ptbBMns977 -3.04596 1.99093 -1.530 0.12604
## sp_ptbBMns978 -0.89191 1.87629 -0.475 0.63453
## sp_ptbBMns979 -1.41829 1.84070 -0.771 0.44099
## sp_ptbBMns980 0.32315 2.16592 0.149 0.88140
## sp_ptbBMns981 -3.58307 2.42266 -1.479 0.13914
## sp_ptbBMns982 1.37888 2.84341 0.485 0.62772
## sp_ptbBMns983 0.41311 2.86582 0.144 0.88538
## sp_ptbBMns984 0.69384 2.99407 0.232 0.81674
## sp_ptbBMns985 0.26943 2.92717 0.092 0.92666
## sp_ptbBMns986 -0.24565 2.99560 -0.082 0.93465
## sp_ptbBMns987 -0.25978 2.89055 -0.090 0.92839
## sp_ptbBMns988 -1.12369 3.07379 -0.366 0.71469
## sp_ptbBMns989 -0.71857 3.01330 -0.238 0.81152
## sp_ptbBMns990 -4.64914 2.98929 -1.555 0.11988
## sp_ptbBMns991 -1.76414 2.87745 -0.613 0.53982
## sp_ptbBMns992 -4.02263 2.66836 -1.508 0.13168
## sp_ptbBMns993 -1.34984 2.49271 -0.542 0.58815
## sp_ptbBMns994 -4.20685 2.45993 -1.710 0.08724 .
## sp_ptbBMns995 -0.57718 2.35282 -0.245 0.80621
## sp_ptbBMns996 -3.27371 2.41084 -1.358 0.17449
## sp_ptbBMns997 -0.41506 2.36458 -0.176 0.86066
## sp_ptbBMns998 0.36007 2.38298 0.151 0.87990
## sp_ptbBMns999 -2.83749 2.43837 -1.164 0.24455
## sp_ptbBMns9100 2.58145 2.67259 0.966 0.33409
## sp_ptbBMns9101 -0.74238 2.55687 -0.290 0.77155
## sp_ptbBMns9102 2.12281 2.61123 0.813 0.41625
## sp_ptbBMns9103 -1.31676 2.56554 -0.513 0.60778
## sp_ptbBMns9104 -0.88385 2.66541 -0.332 0.74019
## sp_ptbBMns9105 -1.67034 2.53930 -0.658 0.51067
## sp_ptbBMns9106 -0.96597 2.47159 -0.391 0.69592
## sp_ptbBMns9107 -6.02795 2.49701 -2.414 0.01578 *
## sp_ptbBMns9108 1.38217 2.73095 0.506 0.61278
## sp_ptbBMns9109 -4.37762 2.97591 -1.471 0.14129
## sp_ptbBMns9110 -1.28424 3.10874 -0.413 0.67953
## sp_ptbBMns9111 -1.58145 2.98407 -0.530 0.59614
## sp_ptbBMns9112 -0.08749 2.88955 -0.030 0.97585
## sp_ptbBMns9113 -1.19256 2.77744 -0.429 0.66765
## sp_ptbBMns9114 -2.35909 2.92240 -0.807 0.41952
## sp_ptbBMns9115 -3.91280 2.89528 -1.351 0.17655
## sp_ptbBMns9116 -3.71012 3.01593 -1.230 0.21863
## sp_ptbBMns9117 -2.90729 3.01075 -0.966 0.33423
## sp_ptbBMns9118 -2.15619 3.13201 -0.688 0.49118
## sp_ptbBMns9119 -0.79814 2.92438 -0.273 0.78491
## sp_ptbBMns9120 -0.52277 3.02438 -0.173 0.86277
## sp_ptbBMns9121 0.01152 2.80056 0.004 0.99672
## sp_ptbBMns9122 -3.62220 2.91210 -1.244 0.21356
## sp_ptbBMns9123 1.17451 2.82997 0.415 0.67812
## sp_ptbBMns9124 -2.79335 3.00663 -0.929 0.35286
## sp_ptbBMns9125 0.11572 3.14172 0.037 0.97062
## sp_ptbBMns9126 -7.00045 3.32945 -2.103 0.03550 *
## sp_ptbBMns9127 -0.42268 3.40170 -0.124 0.90111
## sp_ptbBMns9128 -4.76249 3.42555 -1.390 0.16444
## sp_ptbBMns9129 0.86368 3.37709 0.256 0.79815
## sp_ptbBMns9130 -2.25365 3.32440 -0.678 0.49783
## sp_ptbBMns9131 -1.93381 2.97601 -0.650 0.51582
## sp_ptbBMns9132 -2.91995 3.00334 -0.972 0.33093
## sp_ptbBMns9133 -3.87005 2.82092 -1.372 0.17009
## sp_ptbBMns9134 -1.52709 2.78258 -0.549 0.58314
## sp_ptbBMns9135 -0.64545 3.33329 -0.194 0.84646
## sp_ptbBMns9136 1.42384 4.41467 0.323 0.74705
## sp_ptbBMns9137 -1.63010 4.76207 -0.342 0.73212
## sp_ptbBMns9138 3.16711 5.45648 0.580 0.56162
## sp_ptbBMns9139 -0.35729 4.69197 -0.076 0.93930
## sp_ptbBMns9140 0.14774 5.11863 0.029 0.97697
## sp_ptbBMns9141 -4.40838 4.86617 -0.906 0.36498
## sp_ptbBMns9142 -4.76877 5.08893 -0.937 0.34871
## sp_ptbBMns9143 -8.21111 5.34364 -1.537 0.12439
## sp_ptbBMns9144 -9.53755 4.76568 -2.001 0.04536 *
## sp_ptbBMns9145 -4.25725 3.87764 -1.098 0.27225
## sp_ptbBMns9146 -2.69858 2.83153 -0.953 0.34057
## sp_ptbBMns9147 3.50833 2.26014 1.552 0.12060
## sp_ptbBMns9148 -2.00347 2.23616 -0.896 0.37028
## sp_ptbBMns9149 2.45105 2.01888 1.214 0.22472
## sp_ptbBMns9150 1.19139 1.82961 0.651 0.51494
## sp_ptbBMns9151 -1.26496 1.76500 -0.717 0.47356
## sp_ptbBMns9152 0.32641 1.53419 0.213 0.83151
## sp_ptbBMns9153 -1.10440 1.64609 -0.671 0.50227
## sp_ptbBMns9154 -4.31727 1.80780 -2.388 0.01693 *
## sp_ptbBMns9155 -1.27737 1.51884 -0.841 0.40034
## sp_ptbBMns9156 -3.33878 1.52993 -2.182 0.02909 *
## sp_ptbBMns9157 -0.23522 1.09726 -0.214 0.83026
## sp_ptbBMns9158 -2.52750 1.24235 -2.034 0.04191 *
## sp_ptbBMns9159 0.60996 0.89605 0.681 0.49605
## sp_ptbBMns9160 1.74632 1.02537 1.703 0.08855 .
## sp_ptbBMns9161 NA NA NA NA
## sp_ptbBMns9162 NA NA NA NA
## lag(resid.fullbm, 1) -0.60653 0.03628 -16.716 < 2e-16 ***
## lag(resid.fullbm, 2) -0.69059 0.04390 -15.732 < 2e-16 ***
## lag(resid.fullbm, 3) -0.79537 0.04931 -16.131 < 2e-16 ***
## lag(resid.fullbm, 4) -0.75173 0.05184 -14.502 < 2e-16 ***
## lag(resid.fullbm, 5) -0.67727 0.05157 -13.134 < 2e-16 ***
## lag(resid.fullbm, 6) -0.53833 0.04780 -11.262 < 2e-16 ***
## lag(resid.fullbm, 7) -0.36910 0.04112 -8.975 < 2e-16 ***
## lag(resid.fullbm, 8) -0.21896 0.03357 -6.523 6.88e-11 ***
## lag(resid.fullbm, 13) 0.07035 0.02767 2.542 0.01101 *
## lag(resid.fullbm, 14) 0.06899 0.02775 2.486 0.01291 *
## lag(resid.fullbm, 18) -0.05227 0.02569 -2.034 0.04193 *
## lag(resid.fullbm, 25) -0.01877 0.02618 -0.717 0.47343
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for Negative Binomial(50488.69) family taken to be 1)
##
## Null deviance: 1055.03 on 861 degrees of freedom
## Residual deviance: 433.23 on 669 degrees of freedom
## (77 observations deleted due to missingness)
## AIC: 2803.1
##
## Number of Fisher Scoring iterations: 1
##
##
## Theta: 50489
## Std. Err.: 182860
## Warning while fitting theta: iteration limit reached
##
## 2 x log-likelihood: -2415.147resid.fullbm.ac<-residuals(mod_fullSA4bm.ac, type="deviance")
pred.fullbm.ac<-predict(mod_fullSA4bm.ac, type="response")
pacf(resid.fullbm.ac,na.action = na.omit) 
length(pred.fullbm.ac)## [1] 939length(resid.fullbm.ac)## [1] 939##plot b4 ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,pred.fullbm,lwd=1, col="dark blue")
plot(week$time,resid.fullbm)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm, week$ptbBM)
abline(coef = c(0,1), col="red")
##plot after ac
plot(week$time, week$ptbBM,type="l")
lines(week$time,pred.fullbm.ac,lwd=1, col="dark blue")
plot(week$time,resid.fullbm.ac)
abline(h=0,lty=2,lwd=2, col="blue")
plot(pred.fullbm.ac, week$ptbBM)
abline(coef = c(0,1), col="red")
##checking general model fit plot
plot(mod_fullSA4bm)## Warning: not plotting observations with leverage one:
## 53
plot(mod_fullSA4bm.ac)
#1. plotting the dose response and slices now for min temperature
SA4predbm.temp <- crosspred(cb9.minT, mod_fullSA4bm.ac,cen = 24.0, by=0.1,cumul=TRUE)
#cumulative effect
plot(SA4predbm.temp, "overall", xlab="Min temperature (?C)",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of min temperature on ptb")
##getting est from SA4predbm.temp
#for 5th % - mat
SA4predbm.temp$matRRfit["22.9","lag0"]; SA4predbm.temp$matRRlow["22.9","lag0"]; SA4predbm.temp$matRRhigh["22.9","lag0"]## [1] 0.8821777## [1] 0.7071471## [1] 1.100531SA4predbm.temp$matRRfit["22.9","lag13"]; SA4predbm.temp$matRRlow["22.9","lag13"]; SA4predbm.temp$matRRhigh["22.9","lag13"]## [1] 0.965487## [1] 0.812605## [1] 1.147132SA4predbm.temp$matRRfit["22.9","lag26"]; SA4predbm.temp$matRRlow["22.9","lag26"]; SA4predbm.temp$matRRhigh["22.9","lag26"]## [1] 1.056664## [1] 0.9030783## [1] 1.236369SA4predbm.temp$matRRfit["22.9","lag39"]; SA4predbm.temp$matRRlow["22.9","lag39"]; SA4predbm.temp$matRRhigh["22.9","lag39"]## [1] 1.156451## [1] 0.9623363## [1] 1.38972SA4predbm.temp$matRRfit["22.9","lag52"]; SA4predbm.temp$matRRlow["22.9","lag52"]; SA4predbm.temp$matRRhigh["22.9","lag52"]## [1] 1.265661## [1] 0.9968521## [1] 1.606957#for 95th % - mat
SA4predbm.temp$matRRfit["25.1","lag0"]; SA4predbm.temp$matRRlow["25.1","lag0"]; SA4predbm.temp$matRRhigh["25.1","lag0"]## [1] 0.7973803## [1] 0.6293517## [1] 1.01027SA4predbm.temp$matRRfit["25.1","lag13"]; SA4predbm.temp$matRRlow["25.1","lag13"]; SA4predbm.temp$matRRhigh["25.1","lag13"]## [1] 0.8796897## [1] 0.7298826## [1] 1.060244SA4predbm.temp$matRRfit["25.1","lag26"]; SA4predbm.temp$matRRlow["25.1","lag26"]; SA4predbm.temp$matRRhigh["25.1","lag26"]## [1] 0.9704954## [1] 0.821256## [1] 1.146855SA4predbm.temp$matRRfit["25.1","lag27"]; SA4predbm.temp$matRRlow["25.1","lag27"]; SA4predbm.temp$matRRhigh["25.1","lag27"]## [1] 0.977857## [1] 0.8273596## [1] 1.15573SA4predbm.temp$matRRfit["25.1","lag28"]; SA4predbm.temp$matRRlow["25.1","lag28"]; SA4predbm.temp$matRRhigh["25.1","lag28"]## [1] 0.9852744## [1] 0.8332998## [1] 1.164966SA4predbm.temp$matRRfit["25.1","lag29"]; SA4predbm.temp$matRRlow["25.1","lag29"]; SA4predbm.temp$matRRhigh["25.1","lag29"]## [1] 0.992748## [1] 0.8390741## [1] 1.174567SA4predbm.temp$matRRfit["25.1","lag30"]; SA4predbm.temp$matRRlow["25.1","lag30"]; SA4predbm.temp$matRRhigh["25.1","lag30"]## [1] 1.000278## [1] 0.8446808## [1] 1.184538SA4predbm.temp$matRRfit["25.1","lag31"]; SA4predbm.temp$matRRlow["25.1","lag31"]; SA4predbm.temp$matRRhigh["25.1","lag31"]## [1] 1.007866## [1] 0.8501193## [1] 1.194884SA4predbm.temp$matRRfit["25.1","lag32"]; SA4predbm.temp$matRRlow["25.1","lag32"]; SA4predbm.temp$matRRhigh["25.1","lag32"]## [1] 1.015511## [1] 0.8553901## [1] 1.205605SA4predbm.temp$matRRfit["25.1","lag33"]; SA4predbm.temp$matRRlow["25.1","lag33"]; SA4predbm.temp$matRRhigh["25.1","lag33"]## [1] 1.023214## [1] 0.8604944## [1] 1.216704SA4predbm.temp$matRRfit["25.1","lag34"]; SA4predbm.temp$matRRlow["25.1","lag34"]; SA4predbm.temp$matRRhigh["25.1","lag34"]## [1] 1.030975## [1] 0.8654344## [1] 1.228181SA4predbm.temp$matRRfit["25.1","lag35"]; SA4predbm.temp$matRRlow["25.1","lag35"]; SA4predbm.temp$matRRhigh["25.1","lag35"]## [1] 1.038796## [1] 0.8702131## [1] 1.240037SA4predbm.temp$matRRfit["25.1","lag36"]; SA4predbm.temp$matRRlow["25.1","lag36"]; SA4predbm.temp$matRRhigh["25.1","lag36"]## [1] 1.046675## [1] 0.8748339## [1] 1.252271SA4predbm.temp$matRRfit["25.1","lag37"]; SA4predbm.temp$matRRlow["25.1","lag37"]; SA4predbm.temp$matRRhigh["25.1","lag37"]## [1] 1.054615## [1] 0.879301## [1] 1.264882SA4predbm.temp$matRRfit["25.1","lag38"]; SA4predbm.temp$matRRlow["25.1","lag38"]; SA4predbm.temp$matRRhigh["25.1","lag38"]## [1] 1.062614## [1] 0.883619## [1] 1.277869SA4predbm.temp$matRRfit["25.1","lag39"]; SA4predbm.temp$matRRlow["25.1","lag39"]; SA4predbm.temp$matRRhigh["25.1","lag39"]## [1] 1.070675## [1] 0.8877928## [1] 1.291229SA4predbm.temp$matRRfit["25.1","lag52"]; SA4predbm.temp$matRRlow["25.1","lag52"]; SA4predbm.temp$matRRhigh["25.1","lag52"]## [1] 1.181195## [1] 0.9313749## [1] 1.498023#2. plotting the dose reponse and slices now for RF
SA4predbm.rf <- crosspred(cb3.RF, mod_fullSA4bm.ac,cen = 44.9, by=0.1,cumul=TRUE)
#cumulative effect of RF
plot(SA4predbm.rf, "overall", xlab="Total precipitation",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of rainfall on ptb")
#10mm lower than median rf
plot(SA4predbm.rf,var=c(34.9),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
#90th %
plot(SA4predbm.rf,"slices", var=c(135),type="p",ci="bars",col=1,pch=19,ylim=c(0.85,1.04),
xlab="Lag (weeks)",ylab="RR")
#3. plotting the dose reponse and slices now for wind
SA4predbm.wind <- crosspred(cb1.avgWindSp, mod_fullSA4bm.ac,cen = 4.5, by=0.1,cumul=TRUE)
#cumulative effect of wind
plot(SA4predbm.wind, "overall", xlab="Av wind speed",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of wind speed on ptb")
#0.5 unit lower than median
plot(SA4predbm.wind,var=c(4),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
#90th %
plot(SA4predbm.wind,var=c(5.5),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
#4. plotting the dose reponse and slices now for sun
SA4predbm.sun <- crosspred(cb2.sun, mod_fullSA4bm.ac,cen = 50.7, by=0.1,cumul=TRUE)
#cumulative effect of sun
plot(SA4predbm.sun, "overall", xlab="Total sunshine hours",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of sunshine on ptb")
#10 units lower than median
plot(SA4predbm.sun,var=c(40),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
#90th %
plot(SA4predbm.sun,var=c(70),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
#5. plotting the dose reponse and slices now for minRH
SA4predbm.minRH <- crosspred(cb5.minRH, mod_fullSA4bm.ac,cen = 63, by=0.1, cumul=TRUE)
#cumulative effect
plot(SA4predbm.minRH, "overall", xlab="Min RH",col=4, lwd=2, ylab="RR", ci.arg=list(density=40,lwd=1, col=8),
main="Cumulative effect of minRH on ptb")
#1 units lower than median
plot(SA4predbm.minRH,var=c(62),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
#1 units higher than median
plot(SA4predbm.minRH,var=c(64),type="p",ci="bars",col=1,pch=19,ylim=c(0.9,1.1),
xlab="Lag (weeks)",ylab="RR")
###to make lag plots for full model, ptbBM version, SA4 -----
##order: minT, rf, sun, minRH & avgWindSp
#tiff("fig_lagFullptbBM_SA4_Apr10.tiff", units="in", width=10, height=6, res=400)
par(mfrow=c(5,5),mar=c(2.5,4,2.5,2))
#axis(1, at= c(3,3.7,4.5,5.4,10.2)
for (i in c(0,13,26,39,52)){
x = crosspred(cb1.avgWindSp, mod_fullSA4bm.ac, cen=4.5)
title = paste(c(i,"week lag"),collapse=" ")
plot(x,lag=i,main=title,ylab="RR (Av wind speed)", xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.3),xaxt = "n")
axis(1, at= c(3.0,3.6,4.5,6.1,10.2), labels = c("3.0","5th","50th","95th","10.2"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb9.minT, mod_fullSA4bm.ac, cen=24.0)
plot(x,lag=i,ylab="RR (Min Temp)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(21.8,22.9,24.0,25.1,26.3), labels = c("21.8","5th","50th","95th","26.3"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb3.RF, mod_fullSA4bm.ac, cen=44.9)
plot(x,lag=i,ylab="RR (Rainfall)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.8,1.5),xaxt = "n")
axis(1, at= c(0.0,0.8,44.9,160.7,414.5), labels = c("0.0","5th","50th","95th","414.5"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb2.sun, mod_fullSA4bm.ac, cen=50.7)
plot(x,lag=i,ylab="RR (Sunshine hours)", xlab="",col=4,cex.lab=0.9,
ylim=c(0.6,1.5),xaxt = "n")
axis(1, at= c(11.2,30.8,50.7,66.1,76.0), labels = c("11.2","5th","50th","95th","76.0"),
cex.axis=0.8)
}
for (i in c(0,13,26,39,52)){
x = crosspred(cb5.minRH, mod_fullSA4bm.ac, cen=63.0)
plot(x,lag=i,ylab="RR (Min RH)",xlab="", col=4,cex.lab=0.9,
ylim=c(0.6,1.5),xaxt = "n")
axis(1, at= c(45.0,53.7,63.0,70.3,80.6), labels = c("45.0","5th","50th","95th","80.6"),
cex.axis=0.8)
}
dev.off()## null device
## 1