crabdat <- read.csv("http://www.cknudson.com/data/crabs.csv")
attach(crabdat)
hist(satell)
boxplot(satell ~ color)
boxplot(satell ~ spine)
plot(log(satell) , width)
#looks pretty random
#We should use poission regression because the number of satellite crabs is a count
#The color will probably be big becasue there was a clear pattern there from dark to light
modmain <- glm( satell ~ color, family = "poisson" )
modmain2 <- glm (satell ~ width, family = "poisson")
modmain3 <- glm(satell ~ spine, family = "poisson")
AIC(modmain)
## [1] 972.4368
AIC(modmain2)
## [1] 927.1762
AIC(modmain3)
## [1] 982.4582
summary(modmain)
##
## Call:
## glm(formula = satell ~ color, family = "poisson")
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8577 -2.1106 -0.1649 0.8721 4.7491
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.80078 0.10102 7.927 2.24e-15 ***
## colordarker -0.08516 0.18007 -0.473 0.636279
## colorlight 0.60614 0.17496 3.464 0.000532 ***
## colormedium 0.39155 0.11575 3.383 0.000718 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 632.79 on 172 degrees of freedom
## Residual deviance: 609.14 on 169 degrees of freedom
## AIC: 972.44
##
## Number of Fisher Scoring iterations: 6
summary(modmain2)
##
## Call:
## glm(formula = satell ~ width, family = "poisson")
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.8526 -1.9884 -0.4933 1.0970 4.9221
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.30476 0.54224 -6.095 1.1e-09 ***
## width 0.16405 0.01997 8.216 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 632.79 on 172 degrees of freedom
## Residual deviance: 567.88 on 171 degrees of freedom
## AIC: 927.18
##
## Number of Fisher Scoring iterations: 6
summary(modmain3)
##
## Call:
## glm(formula = satell ~ spine, family = "poisson")
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.7014 -2.3706 -0.5097 1.1252 5.0859
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.03316 0.05423 19.051 <2e-16 ***
## spinegood 0.26120 0.10173 2.568 0.0102 *
## spinemiddle -0.34001 0.19045 -1.785 0.0742 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for poisson family taken to be 1)
##
## Null deviance: 632.79 on 172 degrees of freedom
## Residual deviance: 621.16 on 170 degrees of freedom
## AIC: 982.46
##
## Number of Fisher Scoring iterations: 5
#width has the best pvalue and lowest AIC so appears to be the best predictor
boxplot(width)
library(faraway)
plot(modmain2$residuals~modmain2$fitted, xlab="fitted", ylab="residuals")
halfnorm(residuals(modmain2))
#half norm plot showed the outliers as row 15 and 56 in the data, these are likely the same two outliers found in the
(dp <- sum(residuals(modmain2, type="pearson")^2 / modmain2$df.res) )
## [1] 3.182205
mean(width)
## [1] 26.29884
modcombo <- glm( satell ~ width, family = "poisson" )
modcombo2 <- glm (satell ~ width + spine, family = "poisson")
modcombo3 <- glm(satell ~ width + color, family = "poisson")
modcombo4 <- glm(satell ~ width + weight, family = "poisson")
modcombo5 <- glm(satell ~ width + color + spine, family = "poisson")
c(modcombo, modcombo2, modcombo3, modcombo4)
## $coefficients
## (Intercept) width
## -3.3047572 0.1640451
##
## $residuals
## 1 2 3 4 5
## 1.099549535 -1.000000000 2.444606074 -1.000000000 0.530936033
## 6 7 8 9 10
## -1.000000000 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 11 12 13 14 15
## -1.000000000 -1.000000000 1.934629043 -1.000000000 4.358276115
## 16 17 18 19 20
## 1.556340949 -0.563592551 -0.766027172 -1.000000000 0.520987453
## 21 22 23 24 25
## 1.423482556 0.352889059 -0.320404303 -0.317572788 0.186498200
## 26 27 28 29 30
## -1.000000000 -0.262673500 0.678309130 -1.000000000 -1.000000000
## 31 32 33 34 35
## -0.109052213 -1.000000000 -1.000000000 2.216327051 0.706068029
## 36 37 38 39 40
## -1.000000000 -1.000000000 0.795487929 -1.000000000 0.681453801
## 41 42 43 44 45
## -0.102256035 0.882533314 0.737535612 -0.007131909 1.285756022
## 46 47 48 49 50
## -0.048629230 0.600706751 0.803852078 -0.238081326 -0.432886950
## 51 52 53 54 55
## 1.368558398 0.447946343 0.825184943 1.545734223 1.010204407
## 56 57 58 59 60
## 2.936655378 -0.056969678 0.111141921 -1.000000000 -1.000000000
## 61 62 63 64 65
## -1.000000000 1.447557324 -0.025516630 0.913670041 -0.724317435
## 66 67 68 69 70
## 0.588588945 1.339728284 -1.000000000 -1.000000000 0.111141921
## 71 72 73 74 75
## 1.860833904 -0.373921656 -1.000000000 1.533930096 -1.000000000
## 76 77 78 79 80
## -1.000000000 0.148202025 0.689286730 -1.000000000 1.218127706
## 81 82 83 84 85
## -1.000000000 -1.000000000 -1.000000000 0.556257411 -1.000000000
## 86 87 88 89 90
## -0.025516630 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 91 92 93 94 95
## 0.042952948 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 96 97 98 99 100
## -0.766027172 -1.000000000 -0.647405839 -0.510488535 -0.746027109
## 101 102 103 104 105
## -0.733215542 -0.510488535 -0.700752012 0.894846718 -0.563592551
## 106 107 108 109 110
## -0.690771143 -0.635645707 -0.766027172 -0.141386401 0.410376642
## 111 112 113 114 115
## -0.145367643 0.948966740 -1.000000000 -0.098073961 -0.709204166
## 116 117 118 119 120
## -1.000000000 1.672843362 -1.000000000 1.254815098 0.948966740
## 121 122 123 124 125
## 2.294464495 -0.615671342 -1.000000000 0.028409373 -0.087407528
## 126 127 128 129 130
## -1.000000000 -0.225479342 1.718382786 -0.259238719 1.750531150
## 131 132 133 134 135
## 1.937068789 -1.000000000 0.102730261 2.954993999 0.961696101
## 136 137 138 139 140
## -1.000000000 0.290867763 0.571717203 1.705778117 0.795487929
## 141 142 143 144 145
## -0.217172592 -0.451191454 -0.298081515 -1.000000000 -1.000000000
## 146 147 148 149 150
## 2.607704156 -0.399010408 -0.137809013 4.313550419 0.787162563
## 151 152 153 154 155
## 0.177516071 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 156 157 158 159 160
## 0.894846718 -1.000000000 0.080287098 -1.000000000 -0.179213290
## 161 162 163 164 165
## 0.410376642 0.148202025 1.134275667 -1.000000000 1.468159124
## 166 167 168 169 170
## -1.000000000 -1.000000000 -0.259238719 0.129519988 -0.064108686
## 171 172 173
## -1.000000000 -1.000000000 -1.000000000
##
## $fitted.values
## 1 2 3 4 5 6 7 8
## 3.810341 1.471463 2.612781 2.145904 2.612781 1.821237 2.836122 2.110989
## 9 10 11 12 13 14 15 16
## 1.791604 2.446839 1.976917 2.528449 3.748344 1.150492 2.612781 3.129473
## 17 18 19 20 21 22 23 24
## 2.291437 4.274001 2.110989 3.287338 1.650517 2.217477 1.471463 2.930715
## 25 26 27 28 29 30 31 32
## 2.528449 2.699925 4.068754 2.979189 3.341710 2.181397 4.489601 2.528449
## 33 34 35 36 37 38 39 40
## 2.487309 2.487309 2.930715 1.791604 2.979189 3.341710 1.705567 3.568341
## 41 42 43 44 45 46 47 48
## 3.341710 2.655995 3.453167 5.035916 3.937428 4.204460 3.748344 2.217477
## 49 50 51 52 53 54 55 56
## 3.937428 5.289951 2.110989 3.453167 3.287338 1.571256 2.487309 3.810341
## 57 58 59 60 61 62 63 64
## 3.181234 2.699925 3.510281 2.407027 3.129473 2.042853 3.078554 2.612781
## 65 66 67 68 69 70 71 72
## 3.627360 5.035916 4.274001 2.699925 2.836122 2.699925 2.446839 1.597244
## 73 74 75 76 77 78 79 80
## 1.597244 2.367863 1.944751 1.571256 2.612781 2.367863 2.487309 2.254153
## 81 82 83 84 85 86 87 88
## 2.042853 3.341710 1.623662 2.570269 2.528449 3.078554 3.937428 2.407027
## 89 90 91 92 93 94 95 96
## 1.733777 1.881981 4.794080 2.979189 2.930715 4.068754 1.623662 4.274001
## 97 98 99 100 101 102 103 104
## 2.407027 2.836122 2.042853 3.937428 3.748344 2.042853 3.341710 2.110989
## 105 106 107 108 109 110 111 112
## 2.291437 3.233851 2.744581 4.274001 2.329337 2.836122 3.510281 3.078554
## 113 114 115 116 117 118 119 120
## 2.487309 2.217477 6.877678 1.791604 4.489601 1.355587 2.217477 3.078554
## 121 122 123 124 125 126 127 128
## 1.821237 5.203879 2.699925 1.944751 3.287338 2.367863 3.873363 1.471463
## 129 130 131 132 133 134 135 136
## 2.699925 2.181397 2.042853 2.254153 3.627360 2.528449 3.568341 2.181397
## 137 138 139 140 141 142 143 144
## 3.873363 3.181234 2.217477 3.341710 8.941945 5.466387 4.274001 1.976917
## 145 146 147 148 149 150 151 152
## 2.528449 2.217477 6.655689 4.639343 1.881981 5.035916 3.396981 2.699925
## 153 154 155 156 157 158 159 160
## 1.623662 1.571256 2.042853 2.110989 3.810341 1.851360 1.821237 4.873373
## 161 162 163 164 165 166 167 168
## 2.836122 2.612781 3.748344 2.487309 2.836122 2.528449 1.913108 2.699925
## 169 170 171 172 173
## 2.655995 4.274001 3.627360 3.078554 2.042853
##
## $effects
## (Intercept) width
## -25.518025251 8.216490521 3.854055956 -1.511574626 0.760780978
##
## -1.360593852 -1.805519628 -1.495787658 -1.346311048 -1.643710613
##
## -1.434195276 -1.678425463 3.528042513 -1.010307593 6.947330935
##
## 2.600523794 -0.915712377 -1.855084627 -1.495787658 0.775230894
##
## 1.836499652 0.470987545 -0.361276331 -0.675217691 0.208236816
##
## -1.749964854 -0.780346971 1.034060134 -2.003129112 -1.527520174
##
## -0.524302727 -1.678425463 -1.660981542 3.411553926 1.077187063
##
## -1.346311048 -1.862756321 1.279084699 -1.304312471 1.088448584
##
## -0.362022206 1.336194890 1.183751689 -0.363754490 2.314285765
##
## -0.364068474 0.945483428 1.142525342 -0.709457619 -1.368381940
##
## 1.945548100 0.645616700 1.326771906 1.954004777 1.509353126
##
## 5.508443569 -0.259783509 0.075802307 -2.066336435 -1.626611012
##
## -1.921724256 2.033571394 -0.192062859 1.379435974 -1.584475358
##
## 0.973093506 2.498281779 -1.749964854 -1.805519628 0.075802307
##
## 2.831313082 -0.458995679 -1.250246796 2.289495790 -1.419178597
##
## -1.237066621 0.142125982 0.989770162 -1.660981542 1.770367811
##
## -1.464683373 -2.003129112 -1.263560163 0.798956863 -1.678425463
##
## -0.192062859 -2.221329311 -1.626611012 -1.318171727 -1.389592695
##
## -0.229700956 -1.862756321 -1.843487527 -2.267619472 -1.263560163
##
## -1.855084627 -1.626611012 -1.211723234 -0.765032420 -1.717372081
##
## -1.637075657 -0.765032420 -1.456093477 1.257280948 -0.915712377
##
## -1.405939513 -1.164681552 -1.855084627 -0.282489077 0.569665946
##
## -0.465119068 1.517747097 -1.660981542 -0.200550252 -2.384259326
##
## -1.346311048 3.251298494 -1.124230159 1.814063139 1.517747097
##
## 3.085391595 -1.768771973 -1.749964854 0.014982730 -0.327851131
##
## -1.609681093 -0.674201631 2.111851472 -0.532786746 2.534891258
##
## 2.733222348 -1.559893195 -0.009310898 4.610448800 1.617827747
##
## -1.527520174 0.342013998 0.861543273 2.485600936 1.279084699
##
## -1.360552614 -1.444880349 -0.887669870 -1.434195276 -1.678425463
##
## 3.828676530 -1.532994534 -0.605111722 5.899817653 1.418709505
##
## 0.146278435 -1.749964854 -1.263560163 -1.237066621 -1.464683373
##
## 1.257280948 -2.175945178 0.094868564 -1.360593852 -0.727266449
##
## 0.569665946 0.142125982 1.978507062 -1.660981542 2.351055127
##
## -1.678425463 -1.404311551 -0.532786746 0.108992122 -0.403962492
##
## -2.109530177 -1.901872815 -1.464683373
##
## $R
## (Intercept) width
## (Intercept) -22.47221 -608.26697
## width 0.00000 50.08678
##
## $rank
## [1] 2
##
## $qr
## $qr
## (Intercept) width
## 1 -22.47220515 -6.082670e+02
## 2 0.05397952 5.008678e+01
## 3 0.07192929 3.763019e-02
## 4 0.06518677 6.919926e-02
## 5 0.07192929 3.763019e-02
## 6 0.06005339 9.069378e-02
## 7 0.07494053 2.239392e-02
## 8 0.06465428 7.153480e-02
## 9 0.05956283 9.262530e-02
## 10 0.06960766 4.890783e-02
## 11 0.06256746 8.045464e-02
## 12 0.07075895 4.336734e-02
## 13 0.08615372 -3.996745e-02
## 14 0.04773051 1.320456e-01
## 15 0.07192929 3.763019e-02
## 16 0.07872088 2.331976e-03
## 17 0.06736095 5.941826e-02
## 18 0.09199656 -7.569853e-02
## 19 0.06465428 7.153480e-02
## 20 0.08068197 -8.469692e-03
## 21 0.05716950 1.017284e-01
## 22 0.06626495 6.439764e-02
## 23 0.05397952 1.130053e-01
## 24 0.07618003 1.592844e-02
## 25 0.07075895 4.336734e-02
## 26 0.07311899 3.169139e-02
## 27 0.08976045 -6.177684e-02
## 28 0.07680745 1.261355e-02
## 29 0.08134647 -1.218918e-02
## 30 0.06572365 6.682039e-02
## 31 0.09428838 -9.027551e-02
## 32 0.07075895 4.336734e-02
## 33 0.07018094 4.616186e-02
## 34 0.07018094 4.616186e-02
## 35 0.07618003 1.592844e-02
## 36 0.05956283 9.262530e-02
## 37 0.07680745 1.261355e-02
## 38 0.08134647 -1.218918e-02
## 39 0.05811507 9.819617e-02
## 40 0.08405963 -2.768159e-02
## 41 0.08134647 -1.218918e-02
## 42 0.07252170 3.468631e-02
## 43 0.08269192 -1.981099e-02
## 44 0.09986045 -1.269732e-01
## 45 0.08829998 -5.284826e-02
## 46 0.09124507 -7.098632e-02
## 47 0.08615372 -3.996745e-02
## 48 0.06626495 6.439764e-02
## 49 0.08829998 -5.284826e-02
## 50 0.10234818 -1.439124e-01
## 51 0.06465428 7.153480e-02
## 52 0.08269192 -1.981099e-02
## 53 0.08068197 -8.469692e-03
## 54 0.05577991 1.067637e-01
## 55 0.07018094 4.616186e-02
## 56 0.08686328 -4.419388e-02
## 57 0.07936922 -1.209840e-03
## 58 0.07311899 3.169139e-02
## 59 0.08337297 -2.371481e-02
## 60 0.06903905 5.160586e-02
## 61 0.07872088 2.331976e-03
## 62 0.06360231 7.607811e-02
## 63 0.07807783 5.816009e-03
## 64 0.07192929 3.763019e-02
## 65 0.08475194 -3.171210e-02
## 66 0.09986045 -1.269732e-01
## 67 0.09199656 -7.569853e-02
## 68 0.07311899 3.169139e-02
## 69 0.07494053 2.239392e-02
## 70 0.07311899 3.169139e-02
## 71 0.06960766 4.890783e-02
## 72 0.05623931 1.051198e-01
## 73 0.05623931 1.051198e-01
## 74 0.06847509 5.425655e-02
## 75 0.06205636 8.258168e-02
## 76 0.05577991 1.067637e-01
## 77 0.07192929 3.763019e-02
## 78 0.06847509 5.425655e-02
## 79 0.07018094 4.616186e-02
## 80 0.06681070 6.193046e-02
## 81 0.06360231 7.607811e-02
## 82 0.08134647 -1.218918e-02
## 83 0.05670249 1.034415e-01
## 84 0.07134172 4.052365e-02
## 85 0.07075895 4.336734e-02
## 86 0.07807783 5.816009e-03
## 87 0.08829998 -5.284826e-02
## 88 0.06903905 5.160586e-02
## 89 0.05859370 9.637602e-02
## 90 0.06104666 8.671593e-02
## 91 0.09743320 -1.107724e-01
## 92 0.07680745 1.261355e-02
## 93 0.07618003 1.592844e-02
## 94 0.08976045 -6.177684e-02
## 95 0.05670249 1.034415e-01
## 96 0.09199656 -7.569853e-02
## 97 0.06903905 5.160586e-02
## 98 0.07494053 2.239392e-02
## 99 0.06360231 7.607811e-02
## 100 0.08829998 -5.284826e-02
## 101 0.08615372 -3.996745e-02
## 102 0.06360231 7.607811e-02
## 103 0.08134647 -1.218918e-02
## 104 0.06465428 7.153480e-02
## 105 0.06736095 5.941826e-02
## 106 0.08002290 -4.810155e-03
## 107 0.07372119 2.864479e-02
## 108 0.09199656 -7.569853e-02
## 109 0.06791574 5.686049e-02
## 110 0.07494053 2.239392e-02
## 111 0.08337297 -2.371481e-02
## 112 0.07807783 5.816009e-03
## 113 0.07018094 4.616186e-02
## 114 0.06626495 6.439764e-02
## 115 0.11670117 -2.478698e-01
## 116 0.05956283 9.262530e-02
## 117 0.09428838 -9.027551e-02
## 118 0.05181053 1.200873e-01
## 119 0.06626495 6.439764e-02
## 120 0.07807783 5.816009e-03
## 121 0.06005339 9.069378e-02
## 122 0.10151213 -1.381823e-01
## 123 0.07311899 3.169139e-02
## 124 0.06205636 8.258168e-02
## 125 0.08068197 -8.469692e-03
## 126 0.06847509 5.425655e-02
## 127 0.08757869 -4.848721e-02
## 128 0.05397952 1.130053e-01
## 129 0.07311899 3.169139e-02
## 130 0.06572365 6.682039e-02
## 131 0.06360231 7.607811e-02
## 132 0.06681070 6.193046e-02
## 133 0.08475194 -3.171210e-02
## 134 0.07075895 4.336734e-02
## 135 0.08405963 -2.768159e-02
## 136 0.06572365 6.682039e-02
## 137 0.08757869 -4.848721e-02
## 138 0.07936922 -1.209840e-03
## 139 0.06626495 6.439764e-02
## 140 0.08134647 -1.218918e-02
## 141 0.13306698 -3.781544e-01
## 142 0.10404100 -1.556286e-01
## 143 0.09199656 -7.569853e-02
## 144 0.06256746 8.045464e-02
## 145 0.07075895 4.336734e-02
## 146 0.06626495 6.439764e-02
## 147 0.11480236 -2.335353e-01
## 148 0.09584789 -1.003694e-01
## 149 0.06104666 8.671593e-02
## 150 0.09986045 -1.269732e-01
## 151 0.08201643 -1.596937e-02
## 152 0.07311899 3.169139e-02
## 153 0.05670249 1.034415e-01
## 154 0.05577991 1.067637e-01
## 155 0.06360231 7.607811e-02
## 156 0.06465428 7.153480e-02
## 157 0.08686328 -4.419388e-02
## 158 0.06054799 8.872415e-02
## 159 0.06005339 9.069378e-02
## 160 0.09823566 -1.160923e-01
## 161 0.07494053 2.239392e-02
## 162 0.07192929 3.763019e-02
## 163 0.08615372 -3.996745e-02
## 164 0.07018094 4.616186e-02
## 165 0.07494053 2.239392e-02
## 166 0.07075895 4.336734e-02
## 167 0.06154944 8.466861e-02
## 168 0.07311899 3.169139e-02
## 169 0.07252170 3.468631e-02
## 170 0.09199656 -7.569853e-02
## 171 0.08475194 -3.171210e-02
## 172 0.07807783 5.816009e-03
## 173 0.06360231 7.607811e-02
##
## $rank
## [1] 2
##
## $qraux
## [1] 1.086863 1.113005
##
## $pivot
## [1] 1 2
##
## $tol
## [1] 1e-11
##
## attr(,"class")
## [1] "qr"
##
## $family
##
## Family: poisson
## Link function: log
##
##
## $linear.predictors
## 1 2 3 4 5 6 7
## 1.3377187 0.3862572 0.9604150 0.7635609 0.9604150 0.5995158 1.0424376
## 8 9 10 11 12 13 14
## 0.7471564 0.5831113 0.8947970 0.6815384 0.9276060 1.3213142 0.1401896
## 15 16 17 18 19 20 21
## 0.9604150 1.1408646 0.8291790 1.4525503 0.7471564 1.1900781 0.5010888
## 22 23 24 25 26 27 28
## 0.7963699 0.3862572 1.0752466 0.9276060 0.9932240 1.4033368 1.0916511
## 29 30 31 32 33 34 35
## 1.2064827 0.7799654 1.5017638 0.9276060 0.9112015 0.9112015 1.0752466
## 36 37 38 39 40 41 42
## 0.5831113 1.0916511 1.2064827 0.5338978 1.2721007 1.2064827 0.9768195
## 43 44 45 46 47 48 49
## 1.2392917 1.6165954 1.3705277 1.4361458 1.3213142 0.7963699 1.3705277
## 50 51 52 53 54 55 56
## 1.6658089 0.7471564 1.2392917 1.1900781 0.4518753 0.9112015 1.3377187
## 57 58 59 60 61 62 63
## 1.1572691 0.9932240 1.2556962 0.8783925 1.1408646 0.7143474 1.1244601
## 64 65 66 67 68 69 70
## 0.9604150 1.2885052 1.6165954 1.4525503 0.9932240 1.0424376 0.9932240
## 71 72 73 74 75 76 77
## 0.8947970 0.4682798 0.4682798 0.8619880 0.6651339 0.4518753 0.9604150
## 78 79 80 81 82 83 84
## 0.8619880 0.9112015 0.8127744 0.7143474 1.2064827 0.4846843 0.9440105
## 85 86 87 88 89 90 91
## 0.9276060 1.1244601 1.3705277 0.8783925 0.5503023 0.6323249 1.5673818
## 92 93 94 95 96 97 98
## 1.0916511 1.0752466 1.4033368 0.4846843 1.4525503 0.8783925 1.0424376
## 99 100 101 102 103 104 105
## 0.7143474 1.3705277 1.3213142 0.7143474 1.2064827 0.7471564 0.8291790
## 106 107 108 109 110 111 112
## 1.1736736 1.0096286 1.4525503 0.8455835 1.0424376 1.2556962 1.1244601
## 113 114 115 116 117 118 119
## 0.9112015 0.7963699 1.9282810 0.5831113 1.5017638 0.3042347 0.7963699
## 120 121 122 123 124 125 126
## 1.1244601 0.5995158 1.6494044 0.9932240 0.6651339 1.1900781 0.8619880
## 127 128 129 130 131 132 133
## 1.3541232 0.3862572 0.9932240 0.7799654 0.7143474 0.8127744 1.2885052
## 134 135 136 137 138 139 140
## 0.9276060 1.2721007 0.7799654 1.3541232 1.1572691 0.7963699 1.2064827
## 141 142 143 144 145 146 147
## 2.1907532 1.6986179 1.4525503 0.6815384 0.9276060 0.7963699 1.8954720
## 148 149 150 151 152 153 154
## 1.5345728 0.6323249 1.6165954 1.2228872 0.9932240 0.4846843 0.4518753
## 155 156 157 158 159 160 161
## 0.7143474 0.7471564 1.3377187 0.6159203 0.5995158 1.5837864 1.0424376
## 162 163 164 165 166 167 168
## 0.9604150 1.3213142 0.9112015 1.0424376 0.9276060 0.6487294 0.9932240
## 169 170 171 172 173
## 0.9768195 1.4525503 1.2885052 1.1244601 0.7143474
##
## $deviance
## [1] 567.8786
##
## $aic
## [1] 927.1762
##
## $null.deviance
## [1] 632.7917
##
## $iter
## [1] 6
##
## $weights
## 1 2 3 4 5 6 7 8
## 3.810341 1.471463 2.612781 2.145904 2.612781 1.821237 2.836122 2.110989
## 9 10 11 12 13 14 15 16
## 1.791604 2.446839 1.976917 2.528449 3.748344 1.150492 2.612781 3.129473
## 17 18 19 20 21 22 23 24
## 2.291437 4.274001 2.110989 3.287338 1.650517 2.217477 1.471463 2.930716
## 25 26 27 28 29 30 31 32
## 2.528449 2.699925 4.068754 2.979189 3.341710 2.181397 4.489601 2.528449
## 33 34 35 36 37 38 39 40
## 2.487309 2.487309 2.930716 1.791604 2.979189 3.341710 1.705567 3.568341
## 41 42 43 44 45 46 47 48
## 3.341710 2.655996 3.453167 5.035916 3.937428 4.204460 3.748344 2.217477
## 49 50 51 52 53 54 55 56
## 3.937428 5.289951 2.110989 3.453167 3.287338 1.571256 2.487309 3.810341
## 57 58 59 60 61 62 63 64
## 3.181234 2.699925 3.510281 2.407027 3.129473 2.042853 3.078554 2.612781
## 65 66 67 68 69 70 71 72
## 3.627360 5.035916 4.274001 2.699925 2.836122 2.699925 2.446839 1.597244
## 73 74 75 76 77 78 79 80
## 1.597244 2.367863 1.944751 1.571256 2.612781 2.367863 2.487309 2.254153
## 81 82 83 84 85 86 87 88
## 2.042853 3.341710 1.623662 2.570269 2.528449 3.078554 3.937428 2.407027
## 89 90 91 92 93 94 95 96
## 1.733777 1.881981 4.794080 2.979189 2.930716 4.068754 1.623662 4.274001
## 97 98 99 100 101 102 103 104
## 2.407027 2.836122 2.042853 3.937428 3.748344 2.042853 3.341710 2.110989
## 105 106 107 108 109 110 111 112
## 2.291437 3.233851 2.744581 4.274001 2.329337 2.836122 3.510281 3.078554
## 113 114 115 116 117 118 119 120
## 2.487309 2.217477 6.877678 1.791604 4.489601 1.355587 2.217477 3.078554
## 121 122 123 124 125 126 127 128
## 1.821237 5.203879 2.699925 1.944751 3.287338 2.367863 3.873363 1.471463
## 129 130 131 132 133 134 135 136
## 2.699925 2.181397 2.042853 2.254153 3.627360 2.528449 3.568341 2.181397
## 137 138 139 140 141 142 143 144
## 3.873363 3.181234 2.217477 3.341710 8.941945 5.466387 4.274001 1.976917
## 145 146 147 148 149 150 151 152
## 2.528449 2.217477 6.655689 4.639343 1.881981 5.035916 3.396981 2.699925
## 153 154 155 156 157 158 159 160
## 1.623662 1.571256 2.042853 2.110989 3.810341 1.851360 1.821237 4.873373
## 161 162 163 164 165 166 167 168
## 2.836122 2.612781 3.748344 2.487309 2.836122 2.528449 1.913108 2.699925
## 169 170 171 172 173
## 2.655996 4.274001 3.627360 3.078554 2.042853
##
## $prior.weights
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 163 164 165 166 167 168 169 170 171 172 173
## 1 1 1 1 1 1 1 1 1 1 1
##
## $df.residual
## [1] 171
##
## $df.null
## [1] 172
##
## $y
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 8 0 9 0 4 0 0 0 0 0 0 0 11 0 14 8 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 0 5 4 3 1 2 3 0 3 5 0 0 4 0 0 8 5 0
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 0 6 0 6 3 5 6 5 9 4 6 4 3 3 5 5 6 4
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 5 15 3 3 0 0 0 5 3 5 1 8 10 0 0 3 7 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 0 6 0 0 3 4 0 5 0 0 0 4 0 3 0 0 0 0
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 5 0 0 0 0 1 0 1 1 1 1 1 1 4 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 2 4 3 6 0 2 2 0 12 0 5 6 6 2 0 2 3 0
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 3 4 2 6 6 0 4 10 7 0 5 5 6 6 7 3 3 0
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 0 8 4 4 10 9 4 0 0 0 0 4 0 2 0 4 4 3
## 163 164 165 166 167 168 169 170 171 172 173
## 8 0 7 0 0 2 3 4 0 0 0
##
## $converged
## [1] TRUE
##
## $boundary
## [1] FALSE
##
## $model
## satell width
## 1 8 28.3
## 2 0 22.5
## 3 9 26.0
## 4 0 24.8
## 5 4 26.0
## 6 0 23.8
## 7 0 26.5
## 8 0 24.7
## 9 0 23.7
## 10 0 25.6
## 11 0 24.3
## 12 0 25.8
## 13 11 28.2
## 14 0 21.0
## 15 14 26.0
## 16 8 27.1
## 17 1 25.2
## 18 1 29.0
## 19 0 24.7
## 20 5 27.4
## 21 4 23.2
## 22 3 25.0
## 23 1 22.5
## 24 2 26.7
## 25 3 25.8
## 26 0 26.2
## 27 3 28.7
## 28 5 26.8
## 29 0 27.5
## 30 0 24.9
## 31 4 29.3
## 32 0 25.8
## 33 0 25.7
## 34 8 25.7
## 35 5 26.7
## 36 0 23.7
## 37 0 26.8
## 38 6 27.5
## 39 0 23.4
## 40 6 27.9
## 41 3 27.5
## 42 5 26.1
## 43 6 27.7
## 44 5 30.0
## 45 9 28.5
## 46 4 28.9
## 47 6 28.2
## 48 4 25.0
## 49 3 28.5
## 50 3 30.3
## 51 5 24.7
## 52 5 27.7
## 53 6 27.4
## 54 4 22.9
## 55 5 25.7
## 56 15 28.3
## 57 3 27.2
## 58 3 26.2
## 59 0 27.8
## 60 0 25.5
## 61 0 27.1
## 62 5 24.5
## 63 3 27.0
## 64 5 26.0
## 65 1 28.0
## 66 8 30.0
## 67 10 29.0
## 68 0 26.2
## 69 0 26.5
## 70 3 26.2
## 71 7 25.6
## 72 1 23.0
## 73 0 23.0
## 74 6 25.4
## 75 0 24.2
## 76 0 22.9
## 77 3 26.0
## 78 4 25.4
## 79 0 25.7
## 80 5 25.1
## 81 0 24.5
## 82 0 27.5
## 83 0 23.1
## 84 4 25.9
## 85 0 25.8
## 86 3 27.0
## 87 0 28.5
## 88 0 25.5
## 89 0 23.5
## 90 0 24.0
## 91 5 29.7
## 92 0 26.8
## 93 0 26.7
## 94 0 28.7
## 95 0 23.1
## 96 1 29.0
## 97 0 25.5
## 98 1 26.5
## 99 1 24.5
## 100 1 28.5
## 101 1 28.2
## 102 1 24.5
## 103 1 27.5
## 104 4 24.7
## 105 1 25.2
## 106 1 27.3
## 107 1 26.3
## 108 1 29.0
## 109 2 25.3
## 110 4 26.5
## 111 3 27.8
## 112 6 27.0
## 113 0 25.7
## 114 2 25.0
## 115 2 31.9
## 116 0 23.7
## 117 12 29.3
## 118 0 22.0
## 119 5 25.0
## 120 6 27.0
## 121 6 23.8
## 122 2 30.2
## 123 0 26.2
## 124 2 24.2
## 125 3 27.4
## 126 0 25.4
## 127 3 28.4
## 128 4 22.5
## 129 2 26.2
## 130 6 24.9
## 131 6 24.5
## 132 0 25.1
## 133 4 28.0
## 134 10 25.8
## 135 7 27.9
## 136 0 24.9
## 137 5 28.4
## 138 5 27.2
## 139 6 25.0
## 140 6 27.5
## 141 7 33.5
## 142 3 30.5
## 143 3 29.0
## 144 0 24.3
## 145 0 25.8
## 146 8 25.0
## 147 4 31.7
## 148 4 29.5
## 149 10 24.0
## 150 9 30.0
## 151 4 27.6
## 152 0 26.2
## 153 0 23.1
## 154 0 22.9
## 155 0 24.5
## 156 4 24.7
## 157 0 28.3
## 158 2 23.9
## 159 0 23.8
## 160 4 29.8
## 161 4 26.5
## 162 3 26.0
## 163 8 28.2
## 164 0 25.7
## 165 7 26.5
## 166 0 25.8
## 167 0 24.1
## 168 2 26.2
## 169 3 26.1
## 170 4 29.0
## 171 0 28.0
## 172 0 27.0
## 173 0 24.5
##
## $call
## glm(formula = satell ~ width, family = "poisson")
##
## $formula
## satell ~ width
##
## $terms
## satell ~ width
## attr(,"variables")
## list(satell, width)
## attr(,"factors")
## width
## satell 0
## width 1
## attr(,"term.labels")
## [1] "width"
## attr(,"order")
## [1] 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(satell, width)
## attr(,"dataClasses")
## satell width
## "numeric" "numeric"
##
## $data
## <environment: R_GlobalEnv>
##
## $offset
## NULL
##
## $control
## $control$epsilon
## [1] 1e-08
##
## $control$maxit
## [1] 25
##
## $control$trace
## [1] FALSE
##
##
## $method
## [1] "glm.fit"
##
## $contrasts
## NULL
##
## $xlevels
## named list()
##
## $coefficients
## (Intercept) width spinegood spinemiddle
## -3.12469245 0.15668180 0.09898946 -0.10033226
##
## $residuals
## 1 2 3 4 5
## 1.159850556 -1.000000000 2.155669715 -1.000000000 0.548458613
## 6 7 8 9 10
## -1.000000000 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 11 12 13 14 15
## -1.000000000 -1.000000000 2.016692232 -1.000000000 3.908819557
## 16 17 18 19 20
## 1.360958612 -0.561190073 -0.758063448 -1.000000000 0.554336900
## 21 22 23 24 25
## 1.654619404 0.501691261 -0.393249452 -0.306195076 0.198312456
## 26 27 28 29 30
## -1.000000000 -0.239259472 0.546614949 -1.000000000 -1.000000000
## 31 32 33 34 35
## -0.163709647 -1.000000000 -1.000000000 1.940037243 0.571038427
## 36 37 38 39 40
## -1.000000000 -1.000000000 0.836207678 -1.000000000 0.724659570
## 41 42 43 44 45
## -0.081896161 0.725895247 0.611840309 -0.063225908 1.132929193
## 46 47 48 49 50
## -0.016971561 0.645468490 0.811113399 -0.215043189 -0.463743821
## 51 52 53 54 55
## 1.372845541 0.482966515 0.689412973 1.516763057 0.837523277
## 56 57 58 59 60
## 3.049719793 -0.037710791 0.125515959 -1.000000000 -1.000000000
## 61 62 63 64 65
## -1.000000000 1.448379177 -0.100659305 0.935573267 -0.717025352
## 66 67 68 69 70
## 0.654799695 1.419365518 -1.000000000 -1.000000000 0.125515959
## 71 72 73 74 75
## 1.885068091 -0.380590681 -1.000000000 1.551634621 -1.000000000
## 76 77 78 79 80
## -1.000000000 0.283910091 0.701089747 -1.000000000 1.228697123
## 81 82 83 84 85
## -1.000000000 -1.000000000 -1.000000000 0.424667864 -1.000000000
## 86 87 88 89 90
## -0.007078707 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 91 92 93 94 95
## -0.018141999 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 96 97 98 99 100
## -0.780865424 -1.000000000 -0.642054761 -0.510324165 -0.738347730
## 101 102 103 104 105
## -0.725755252 -0.510324165 -0.693965387 1.098617077 -0.602546921
## 106 107 108 109 110
## -0.684223536 -0.630660465 -0.758063448 -0.136023688 0.431780956
## 111 112 113 114 115
## -0.124052674 0.985842586 -1.000000000 -0.094443301 -0.692816445
## 116 117 118 119 120
## -1.000000000 1.769930806 -1.000000000 1.263891749 0.985842586
## 121 122 123 124 125
## 2.278628066 -0.636850330 -1.000000000 0.026484701 -0.067397860
## 126 127 128 129 130
## -1.000000000 -0.202647489 1.679543100 -0.249656027 1.499487265
## 131 132 133 134 135
## 2.248131998 -1.000000000 0.025219696 2.994374855 1.012102831
## 136 137 138 139 140
## -1.000000000 0.203673052 0.603815349 2.003382523 0.836207678
## 141 142 143 144 145
## -0.242117704 -0.426209070 -0.274190344 -1.000000000 -1.000000000
## 146 147 148 149 150
## 2.622226798 -0.425821948 -0.105174283 4.295801443 0.861649657
## 151 152 153 154 155
## 0.205107907 -1.000000000 -1.000000000 -1.000000000 -1.000000000
## 156 157 158 159 160
## 0.898276433 -1.000000000 0.075886092 -1.000000000 -0.056159973
## 161 162 163 164 165
## 0.431780956 0.161343960 1.193957987 -1.000000000 1.505616672
## 166 167 168 169 170
## -1.000000000 -1.000000000 -0.249656027 0.143289622 -0.032253793
## 171 172 173
## -1.000000000 -1.000000000 -1.000000000
##
## $fitted.values
## 1 2 3 4 5 6 7 8
## 3.703960 1.492792 2.852009 2.140450 2.583214 1.830034 3.084423 1.906017
## 9 10 11 12 13 14 15 16
## 1.989047 2.426286 1.979166 2.503521 3.646378 1.067472 2.852009 3.388454
## 17 18 19 20 21 22 23 24
## 2.278891 4.133315 2.107175 3.216806 1.506807 1.997748 1.648124 2.882655
## 25 26 27 28 29 30 31 32
## 2.503521 2.665444 3.943526 3.232867 3.267604 2.174251 4.783028 2.503521
## 33 34 35 36 37 38 39 40
## 2.229322 2.721054 3.182608 1.801584 2.928176 3.267604 1.718861 3.478947
## 41 42 43 44 45 46 47 48
## 3.267604 2.897047 3.722453 5.337466 4.219549 4.069058 3.646378 2.208586
## 49 50 51 52 53 54 55 56
## 3.821866 5.594341 2.107175 3.371620 3.551529 1.589343 2.721054 3.703960
## 57 58 59 60 61 62 63 64
## 3.117566 2.665444 3.781237 2.388567 3.069100 2.042167 3.335777 2.583214
## 65 66 67 68 69 70 71 72
## 3.533885 4.834422 4.133315 2.665444 3.084423 2.665444 2.426286 1.614441
## 73 74 75 76 77 78 79 80
## 1.614441 2.351434 1.948397 1.437619 2.336612 2.351434 2.464601 2.243463
## 81 82 83 84 85 86 87 88
## 1.847216 3.267604 1.639936 2.807672 2.503521 3.021388 3.821866 2.637108
## 89 90 91 92 93 94 95 96
## 1.746004 1.708026 5.092386 3.232867 2.882655 4.353868 1.639936 4.563406
## 97 98 99 100 101 102 103 104
## 2.388567 2.793723 2.042167 3.821866 3.646378 2.042167 3.267604 1.906017
## 105 106 107 108 109 110 111 112
## 2.516020 3.166797 2.707536 4.133315 2.314878 2.793723 3.424863 3.021388
## 113 114 115 116 117 118 119 120
## 2.464601 2.208586 6.510765 1.801584 4.332238 1.380309 2.208586 3.021388
## 121 122 123 124 125 126 127 128
## 1.830034 5.507371 2.665444 1.948397 3.216806 2.126959 3.762451 1.492792
## 129 130 131 132 133 134 135 136
## 2.665444 2.400492 1.847216 2.243463 3.901603 2.503521 3.478947 2.174251
## 137 138 139 140 141 142 143 144
## 4.153952 3.117566 1.997748 3.267604 9.236263 5.228385 4.133315 2.185107
## 145 146 147 148 149 150 151 152
## 2.503521 2.208586 6.966480 4.470144 1.888288 4.834422 3.319205 2.665444
## 153 154 155 156 157 158 159 160
## 1.810579 1.754722 2.042167 2.107175 3.703960 1.858933 1.830034 4.238006
## 161 162 163 164 165 166 167 168
## 2.793723 2.583214 3.646378 2.464601 2.793723 2.503521 1.918107 2.665444
## 169 170 171 172 173
## 2.624007 4.133315 3.901603 3.021388 1.847216
##
## $effects
## (Intercept) width spinegood spinemiddle
## -25.546363793 8.230025412 -1.003844608 -0.519614796 0.912829098
##
## -1.294382528 -2.326958462 -1.711726518 -1.826122613 -1.520587425
##
## -1.353662552 -1.548006534 3.842966013 -1.239847222 6.061479706
##
## 1.895157332 -0.804748853 -1.567218897 -1.403003777 1.002154287
##
## 1.756474128 0.365518732 -0.866746594 -0.499572584 0.348025463
##
## -1.604313649 -0.494144633 0.392738852 -1.801609432 -1.428334135
##
## -1.134641632 -1.548006534 -1.867290702 2.678381561 0.435165772
##
## -1.282836899 -1.692576855 1.517614275 -1.248804733 1.349833265
##
## -0.141997579 0.689755528 0.528579775 -0.983863915 1.615473661
##
## -0.057893934 1.224546892 1.250384621 -0.434911675 -1.962218842
##
## 2.041445024 0.889005939 0.668577633 1.978813455 0.859716709
##
## 5.859249804 -0.054980445 0.233224880 -2.603644581 -1.507057789
##
## -1.738485199 2.120726259 -0.787180298 1.535014480 -1.351744559
##
## 1.387903950 2.859615266 -1.604313649 -2.326958462 0.233224880
##
## 2.973350315 -0.417781592 -1.204807200 2.419128203 -1.341596610
##
## -1.463362368 0.045949799 1.114869898 -1.534236659 1.884071322
##
## -1.682189651 -1.801609432 -1.215661425 0.177900301 -1.548006534
##
## 0.002861836 -1.969470384 -2.134138415 -1.260048877 -1.610536762
##
## -0.851984797 -2.388101785 -1.677541105 -2.814062002 -1.215661425
##
## -2.419763051 -1.507057789 -1.049578003 -0.678347382 -1.457950814
##
## -1.393872229 -0.678347382 -1.248405481 1.185594332 -1.449004627
##
## -1.207830135 -1.010969604 -1.567218897 -0.165837062 0.745277736
##
## -0.229367583 1.728771411 -1.534236659 -0.095391502 -1.880137586
##
## -1.282836899 3.650564319 -1.101376322 1.923272683 1.728771411
##
## 3.140904167 -2.350623047 -1.604313649 0.091222070 -0.112955355
##
## -1.819206806 -0.405385734 2.121911719 -0.379287963 1.846448241
##
## 2.732421488 -1.454114634 -0.623950343 4.772100122 1.885970618
##
## -1.428334135 -0.288878389 1.077738528 2.488034617 1.517614275
##
## -1.952424806 -1.040758249 -0.583477972 -1.923555919 -1.548006534
##
## 3.941936869 -2.129362928 -0.260820999 5.959444222 1.842711561
##
## 0.377808026 -1.604313649 -1.733581428 -1.703779005 -1.378115793
##
## 1.352555264 -1.934709189 0.160861587 -1.294382528 -0.722413791
##
## 0.745277736 0.290643716 2.271914541 -1.534236659 2.540133475
##
## -1.548006534 -1.329636550 -0.379287963 0.261939922 -0.091607510
##
## -2.649012996 -1.723047739 -1.682189651
##
## $R
## (Intercept) width spinegood spinemiddle
## (Intercept) -22.47221 -608.26697 -6.007421 -1.3349825
## width 0.00000 50.18306 2.648472 -1.0473453
## spinegood 0.00000 0.00000 -9.586266 0.5472347
## spinemiddle 0.00000 0.00000 0.000000 5.1789405
##
## $rank
## [1] 4
##
## $qr
## $qr
## (Intercept) width spinegood spinemiddle
## 1 -22.47220515 -6.082670e+02 -6.007421e+00 -1.3349825021
## 2 0.05436932 5.018306e+01 2.648472e+00 -1.0473453425
## 3 0.07515014 3.919684e-02 -9.586266e+00 0.5472347488
## 4 0.06510388 6.894149e-02 -1.853278e-02 5.1789404550
## 5 0.07152115 3.730403e-02 -3.097796e-02 0.0268500210
## 6 0.06019825 9.070374e-02 -9.688530e-03 0.0308169442
## 7 0.07815222 2.326420e-02 1.445205e-01 0.0060613335
## 8 0.06143526 6.780772e-02 -1.672837e-02 -0.2385575462
## 9 0.06275912 9.737272e-02 1.377962e-01 0.0157712887
## 10 0.06931469 4.856896e-02 -2.659200e-02 0.0277420830
## 11 0.06260303 8.031019e-02 -1.394822e-02 0.0301057441
## 12 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 13 0.08497382 -3.939299e-02 -5.993342e-02 0.0203005138
## 14 0.04597614 1.269219e-01 8.527433e-03 -0.1679732008
## 15 0.07515014 3.919684e-02 1.436178e-01 0.0081600408
## 16 0.08191343 2.375107e-03 1.453951e-01 0.0033033964
## 17 0.06717630 5.910333e-02 -2.244717e-02 0.0285535494
## 18 0.09046979 -7.435109e-02 -7.276422e-02 0.0171225788
## 19 0.06459584 7.129614e-02 -1.758897e-02 0.0294608102
## 20 0.07981173 -8.407851e-03 -4.839300e-02 0.0230291447
## 21 0.05462396 9.698120e-02 -4.736504e-03 -0.2070246261
## 22 0.06289623 6.097066e-02 -1.946066e-02 -0.2454014117
## 23 0.05712803 1.193345e-01 1.339139e-01 0.0186099721
## 24 0.07555281 1.572384e-02 -3.926742e-02 0.0250819107
## 25 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 26 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 27 0.08836833 -6.075252e-02 -6.779413e-02 0.0183698345
## 28 0.08001073 1.306869e-02 1.449876e-01 0.0047160700
## 29 0.08043944 -1.207609e-02 -4.976881e-02 0.0227111369
## 30 0.06561591 6.654539e-02 -1.949034e-02 0.0291117459
## 31 0.09732082 -9.305572e-02 1.462536e-01 -0.0093605750
## 32 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 33 0.06644170 4.358056e-02 -2.631182e-02 -0.2621206289
## 34 0.07340453 4.814763e-02 1.430063e-01 0.0093369283
## 35 0.07938636 1.652167e-02 1.448383e-01 0.0051718627
## 36 0.05972849 9.267061e-02 -8.873959e-03 0.0309470815
## 37 0.07614702 1.243760e-02 -4.051835e-02 0.0248066821
## 38 0.08043944 -1.207609e-02 -4.976881e-02 0.0227111369
## 39 0.05834111 9.835566e-02 -6.502426e-03 0.0313142593
## 40 0.08300003 -2.732762e-02 -5.546061e-02 0.0213740203
## 41 0.08043944 -1.207609e-02 -4.976881e-02 0.0227111369
## 42 0.07574119 3.611339e-02 1.438103e-01 0.0077542423
## 43 0.08585566 -2.057852e-02 1.460192e-01 0.0002659590
## 44 0.10280681 -1.305274e-01 1.455944e-01 -0.0143536637
## 45 0.09140866 -5.465608e-02 1.464162e-01 -0.0042543835
## 46 0.08976381 -6.975123e-02 -7.108584e-02 0.0175459497
## 47 0.08497382 -3.939299e-02 -5.993342e-02 0.0203005138
## 48 0.06613197 6.410734e-02 -2.046182e-02 0.0289303550
## 49 0.08699455 -5.201675e-02 -6.458759e-02 0.0191638625
## 50 0.10525162 -1.477711e-01 1.451502e-01 -0.0166542696
## 51 0.06459584 7.129614e-02 -1.758897e-02 0.0294608102
## 52 0.08170970 -1.958479e-02 -5.257658e-02 0.0220557598
## 53 0.08386138 -8.834465e-03 1.457401e-01 0.0018208673
## 54 0.05610004 1.071384e-01 -2.782270e-03 0.0318518888
## 55 0.07340453 4.814763e-02 1.430063e-01 0.0093369283
## 56 0.08564213 -4.353791e-02 -6.146436e-02 0.0199287629
## 57 0.07857097 -1.240253e-03 -4.569650e-02 0.0236462017
## 58 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 59 0.08653090 -2.461526e-02 1.460971e-01 -0.0002688757
## 60 0.06877380 5.126968e-02 -2.553361e-02 0.0279523412
## 61 0.07795785 2.260414e-03 -4.437541e-02 0.0239454093
## 62 0.06359163 7.588309e-02 -1.574201e-02 0.0297919857
## 63 0.08127422 5.996070e-03 1.452661e-01 0.0037819270
## 64 0.07152115 3.730403e-02 -3.097796e-02 0.0268500210
## 65 0.08365281 -3.128856e-02 -5.693176e-02 0.0210230561
## 66 0.09784228 -1.242243e-01 -9.079902e-02 0.0124467812
## 67 0.09046979 -7.435109e-02 -7.276422e-02 0.0171225788
## 68 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 69 0.07815222 2.326420e-02 1.445205e-01 0.0060613335
## 70 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 71 0.06931469 4.856896e-02 -2.659200e-02 0.0277420830
## 72 0.05654126 1.054491e-01 -3.503686e-03 0.0317515595
## 73 0.05654126 1.054491e-01 -3.503686e-03 0.0317515595
## 74 0.06823712 5.392528e-02 -2.449012e-02 0.0281576287
## 75 0.06211451 8.246501e-02 -1.307089e-02 0.0302562365
## 76 0.05335514 1.018963e-01 -2.646137e-03 -0.2012225980
## 77 0.06802172 3.547880e-02 -2.946225e-02 -0.2696202506
## 78 0.06823712 5.392528e-02 -2.449012e-02 0.0281576287
## 79 0.06985984 4.582259e-02 -2.766545e-02 0.0275267884
## 80 0.06665209 6.162683e-02 -2.144738e-02 0.0287443102
## 81 0.06048018 7.217024e-02 -1.497177e-02 -0.2340983512
## 82 0.08043944 -1.207609e-02 -4.976881e-02 0.0227111369
## 83 0.05698594 1.037266e-01 -4.236278e-03 0.0316476817
## 84 0.07456371 4.222997e-02 1.434196e-01 0.0085590337
## 85 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 86 0.07734950 5.706521e-03 -4.307215e-02 0.0242385073
## 87 0.08699455 -5.201675e-02 -6.458759e-02 0.0191638625
## 88 0.07226338 5.387110e-02 1.425712e-01 0.0100885680
## 89 0.05879995 9.649612e-02 -7.281045e-03 0.0311956863
## 90 0.05815695 8.241942e-02 -1.079904e-02 -0.2233015563
## 91 0.10041879 -1.140051e-01 1.459397e-01 -0.0121509500
## 92 0.08001073 1.306869e-02 1.449876e-01 0.0047160700
## 93 0.07555281 1.572384e-02 -3.926742e-02 0.0250819107
## 94 0.09285215 -6.383511e-02 1.464308e-01 -0.0054738667
## 95 0.05698594 1.037266e-01 -4.236278e-03 0.0316476817
## 96 0.09506023 -7.812367e-02 1.463847e-01 -0.0073735920
## 97 0.06877380 5.126968e-02 -2.553361e-02 0.0279523412
## 98 0.07437826 2.214078e-02 -3.681653e-02 0.0256150234
## 99 0.06359163 7.588309e-02 -1.574201e-02 0.0297919857
## 100 0.08699455 -5.201675e-02 -6.458759e-02 0.0191638625
## 101 0.08497382 -3.939299e-02 -5.993342e-02 0.0203005138
## 102 0.06359163 7.588309e-02 -1.574201e-02 0.0297919857
## 103 0.08043944 -1.207609e-02 -4.976881e-02 0.0227111369
## 104 0.06143526 6.780772e-02 -1.672837e-02 -0.2385575462
## 105 0.07058483 6.210223e-02 1.418794e-01 0.0111681702
## 106 0.07918892 -4.796124e-03 -4.703563e-02 0.0233408064
## 107 0.07322197 2.835440e-02 -3.443236e-02 0.0261254987
## 108 0.09046979 -7.435109e-02 -7.276422e-02 0.0171225788
## 109 0.06770463 5.653632e-02 -2.346137e-02 0.0283580100
## 110 0.07437826 2.214078e-02 -3.681653e-02 0.0256150234
## 111 0.08235234 -2.342660e-02 -5.400896e-02 0.0217182269
## 112 0.07734950 5.706521e-03 -4.307215e-02 0.0242385073
## 113 0.06985984 4.582259e-02 -2.766545e-02 0.0275267884
## 114 0.06613197 6.410734e-02 -2.046182e-02 0.0289303550
## 115 0.11354560 -2.407697e-01 -1.320631e-01 0.0010579295
## 116 0.05972849 9.267061e-02 -8.873959e-03 0.0309470815
## 117 0.09262121 -8.856207e-02 -7.793236e-02 0.0158056135
## 118 0.05228082 1.209150e-01 3.228557e-03 0.0326030911
## 119 0.06613197 6.410734e-02 -2.046182e-02 0.0289303550
## 120 0.07734950 5.706521e-03 -4.307215e-02 0.0242385073
## 121 0.06019825 9.070374e-02 -9.688530e-03 0.0308169442
## 122 0.10443029 -1.419415e-01 1.453095e-01 -0.0158763157
## 123 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 124 0.06211451 8.246501e-02 -1.307089e-02 0.0302562365
## 125 0.07981173 -8.407851e-03 -4.839300e-02 0.0230291447
## 126 0.06489838 5.128680e-02 -2.329186e-02 -0.2548239393
## 127 0.08631569 -4.774558e-02 -6.301567e-02 0.0195499087
## 128 0.05436932 1.135719e-01 -5.783725e-06 0.0322186954
## 129 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 130 0.06894527 6.992191e-02 1.411428e-01 0.0121921746
## 131 0.06048018 7.217024e-02 -1.497177e-02 -0.2340983512
## 132 0.06665209 6.162683e-02 -2.144738e-02 0.0287443102
## 133 0.08789736 -3.287615e-02 1.462293e-01 -0.0013639323
## 134 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 135 0.08300003 -2.732762e-02 -5.546061e-02 0.0213740203
## 136 0.06561591 6.654539e-02 -1.949034e-02 0.0291117459
## 137 0.09069535 -5.016819e-02 1.463957e-01 -0.0036584175
## 138 0.07857097 -1.240253e-03 -4.569650e-02 0.0236462017
## 139 0.06289623 6.097066e-02 -1.946066e-02 -0.2454014117
## 140 0.08043944 -1.207609e-02 -4.976881e-02 0.0227111369
## 141 0.13523915 -3.836673e-01 1.329632e-01 -0.0482524799
## 142 0.10175086 -1.519690e-01 -1.007206e-01 0.0097871685
## 143 0.09046979 -7.435109e-02 -7.276422e-02 0.0171225788
## 144 0.06577951 8.438513e-02 1.395449e-01 0.0140813286
## 145 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 146 0.06613197 6.410734e-02 -2.046182e-02 0.0289303550
## 147 0.11745216 -2.385343e-01 1.416319e-01 -0.0287879074
## 148 0.09408385 -9.838684e-02 -8.149106e-02 0.0148876236
## 149 0.06114887 8.665955e-02 -1.135461e-02 0.0305447315
## 150 0.09784228 -1.242243e-01 -9.079902e-02 0.0124467812
## 151 0.08107208 -1.580151e-02 -5.116326e-02 0.0223867024
## 152 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 153 0.05987741 1.089897e-01 1.359139e-01 0.0172763647
## 154 0.05894656 1.125746e-01 1.352596e-01 0.0177393148
## 155 0.06359163 7.588309e-02 -1.574201e-02 0.0297919857
## 156 0.06459584 7.129614e-02 -1.758897e-02 0.0294608102
## 157 0.08564213 -4.353791e-02 -6.146436e-02 0.0199287629
## 158 0.06067170 8.870021e-02 -1.051537e-02 0.0306828456
## 159 0.06019825 9.070374e-02 -9.688530e-03 0.0308169442
## 160 0.09160836 -1.081049e-01 -8.274707e-02 -0.3847119065
## 161 0.07437826 2.214078e-02 -3.681653e-02 0.0256150234
## 162 0.07152115 3.730403e-02 -3.097796e-02 0.0268500210
## 163 0.08497382 -3.939299e-02 -5.993342e-02 0.0203005138
## 164 0.06985984 4.582259e-02 -2.766545e-02 0.0275267884
## 165 0.07437826 2.214078e-02 -3.681653e-02 0.0256150234
## 166 0.07040928 4.303002e-02 -2.875415e-02 0.0273063913
## 167 0.06162980 8.458130e-02 -1.220640e-02 0.0304025470
## 168 0.07265058 3.138647e-02 -3.326483e-02 0.0263724275
## 169 0.07208366 3.436948e-02 -3.211342e-02 0.0266139116
## 170 0.09046979 -7.435109e-02 -7.276422e-02 0.0171225788
## 171 0.08789736 -3.287615e-02 1.462293e-01 -0.0013639323
## 172 0.07734950 5.706521e-03 -4.307215e-02 0.0242385073
## 173 0.06048018 7.217024e-02 -1.497177e-02 -0.2340983512
##
## $rank
## [1] 4
##
## $qraux
## [1] 1.085642 1.113572 1.143618 1.029289
##
## $pivot
## [1] 1 2 3 4
##
## $tol
## [1] 1e-11
##
## attr(,"class")
## [1] "qr"
##
## $family
##
## Family: poisson
## Link function: log
##
##
## $linear.predictors
## 1 2 3 4 5 6
## 1.30940251 0.40064807 1.04802383 0.76101621 0.94903437 0.60433441
## 7 8 9 10 11 12
## 1.12636473 0.64501577 0.68765569 0.88636165 0.68267531 0.91769801
## 13 14 15 16 17 18
## 1.29373433 0.06529311 1.04802383 1.22037381 0.82368893 1.41907977
## 19 20 21 22 23 24
## 0.74534803 1.16838889 0.40999307 0.69202031 0.49963753 1.05871163
## 25 26 27 28 29 30
## 0.91769801 0.98037073 1.37207523 1.17336927 1.18405707 0.77668439
## 31 32 33 34 35 36
## 1.56507377 0.91769801 0.80169757 1.00101929 1.15770109 0.58866623
## 37 38 39 40 41 42
## 1.07437981 1.18405707 0.54166169 1.24672979 1.18405707 1.06369201
## 43 44 45 46 47 48
## 1.31438289 1.67475104 1.43972833 1.40341159 1.29373433 0.79235257
## 49 50 51 52 53 54
## 1.34073887 1.72175558 0.74534803 1.21539343 1.26737835 0.46332079
## 55 56 57 58 59 60
## 1.00101929 1.30940251 1.13705253 0.98037073 1.33005107 0.87069347
## 61 62 63 64 65 66
## 1.12138435 0.71401167 1.20470563 0.94903437 1.26239797 1.57576157
## 67 68 69 70 71 72
## 1.41907977 0.98037073 1.12636473 0.98037073 0.88636165 0.47898897
## 73 74 75 76 77 78
## 0.47898897 0.85502529 0.66700713 0.36298853 0.84870211 0.85502529
## 79 80 81 82 83 84
## 0.90202983 0.80802075 0.61367941 1.18405707 0.49465715 1.03235565
## 85 86 87 88 89 90
## 0.91769801 1.10571617 1.34073887 0.96968293 0.55732987 0.53533851
## 91 92 93 94 95 96
## 1.62774649 1.17336927 1.05871163 1.47106469 0.49465715 1.51806923
## 97 98 99 100 101 102
## 0.87069347 1.02737527 0.71401167 1.34073887 1.29373433 0.71401167
## 103 104 105 106 107 108
## 1.18405707 0.64501577 0.92267839 1.15272071 0.99603891 1.41907977
## 109 110 111 112 113 114
## 0.83935711 1.02737527 1.23106161 1.10571617 0.90202983 0.79235257
## 115 116 117 118 119 120
## 1.87345699 0.58866623 1.46608431 0.32230717 0.79235257 1.10571617
## 121 122 123 124 125 126
## 0.60433441 1.70608740 0.98037073 0.66700713 1.16838889 0.75469303
## 127 128 129 130 131 132
## 1.32507069 0.40064807 0.98037073 0.87567385 0.61367941 0.80802075
## 133 134 135 136 137 138
## 1.36138743 0.91769801 1.24672979 0.77668439 1.42406015 1.13705253
## 139 140 141 142 143 144
## 0.69202031 1.18405707 2.22313734 1.65410247 1.41907977 0.78166477
## 145 146 147 148 149 150
## 0.91769801 0.79235257 1.94111010 1.49742067 0.63567077 1.57576157
## 151 152 153 154 155 156
## 1.19972525 0.98037073 0.59364661 0.56231025 0.71401167 0.74534803
## 157 158 159 160 161 162
## 1.30940251 0.62000259 0.60433441 1.44409295 1.02737527 0.94903437
## 163 164 165 166 167 168
## 1.29373433 0.90202983 1.02737527 0.91769801 0.65133895 0.98037073
## 169 170 171 172 173
## 0.96470255 1.41907977 1.36138743 1.10571617 0.61367941
##
## $deviance
## [1] 566.6049
##
## $aic
## [1] 929.9026
##
## $null.deviance
## [1] 632.7917
##
## $iter
## [1] 6
##
## $weights
## 1 2 3 4 5 6 7 8
## 3.703960 1.492792 2.852010 2.140450 2.583214 1.830034 3.084423 1.906017
## 9 10 11 12 13 14 15 16
## 1.989047 2.426286 1.979166 2.503521 3.646378 1.067472 2.852010 3.388454
## 17 18 19 20 21 22 23 24
## 2.278891 4.133315 2.107175 3.216806 1.506807 1.997748 1.648124 2.882655
## 25 26 27 28 29 30 31 32
## 2.503521 2.665444 3.943526 3.232867 3.267604 2.174251 4.783028 2.503521
## 33 34 35 36 37 38 39 40
## 2.229322 2.721054 3.182608 1.801584 2.928176 3.267604 1.718861 3.478947
## 41 42 43 44 45 46 47 48
## 3.267604 2.897047 3.722453 5.337466 4.219549 4.069058 3.646378 2.208586
## 49 50 51 52 53 54 55 56
## 3.821866 5.594341 2.107175 3.371620 3.551530 1.589343 2.721054 3.703960
## 57 58 59 60 61 62 63 64
## 3.117566 2.665444 3.781237 2.388567 3.069100 2.042167 3.335777 2.583214
## 65 66 67 68 69 70 71 72
## 3.533885 4.834422 4.133315 2.665444 3.084423 2.665444 2.426286 1.614441
## 73 74 75 76 77 78 79 80
## 1.614441 2.351434 1.948397 1.437619 2.336612 2.351434 2.464601 2.243463
## 81 82 83 84 85 86 87 88
## 1.847216 3.267604 1.639936 2.807672 2.503521 3.021388 3.821866 2.637108
## 89 90 91 92 93 94 95 96
## 1.746004 1.708026 5.092386 3.232867 2.882655 4.353868 1.639936 4.563406
## 97 98 99 100 101 102 103 104
## 2.388567 2.793723 2.042167 3.821866 3.646378 2.042167 3.267604 1.906017
## 105 106 107 108 109 110 111 112
## 2.516020 3.166797 2.707536 4.133315 2.314878 2.793723 3.424863 3.021388
## 113 114 115 116 117 118 119 120
## 2.464601 2.208586 6.510765 1.801584 4.332238 1.380309 2.208586 3.021388
## 121 122 123 124 125 126 127 128
## 1.830034 5.507371 2.665444 1.948397 3.216806 2.126959 3.762451 1.492792
## 129 130 131 132 133 134 135 136
## 2.665444 2.400492 1.847216 2.243463 3.901603 2.503521 3.478947 2.174251
## 137 138 139 140 141 142 143 144
## 4.153952 3.117566 1.997748 3.267604 9.236263 5.228385 4.133315 2.185107
## 145 146 147 148 149 150 151 152
## 2.503521 2.208586 6.966480 4.470144 1.888288 4.834422 3.319205 2.665444
## 153 154 155 156 157 158 159 160
## 1.810579 1.754722 2.042167 2.107175 3.703960 1.858933 1.830034 4.238006
## 161 162 163 164 165 166 167 168
## 2.793723 2.583214 3.646378 2.464601 2.793723 2.503521 1.918107 2.665444
## 169 170 171 172 173
## 2.624007 4.133315 3.901603 3.021388 1.847216
##
## $prior.weights
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 163 164 165 166 167 168 169 170 171 172 173
## 1 1 1 1 1 1 1 1 1 1 1
##
## $df.residual
## [1] 169
##
## $df.null
## [1] 172
##
## $y
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 8 0 9 0 4 0 0 0 0 0 0 0 11 0 14 8 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 0 5 4 3 1 2 3 0 3 5 0 0 4 0 0 8 5 0
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 0 6 0 6 3 5 6 5 9 4 6 4 3 3 5 5 6 4
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 5 15 3 3 0 0 0 5 3 5 1 8 10 0 0 3 7 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 0 6 0 0 3 4 0 5 0 0 0 4 0 3 0 0 0 0
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 5 0 0 0 0 1 0 1 1 1 1 1 1 4 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 2 4 3 6 0 2 2 0 12 0 5 6 6 2 0 2 3 0
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 3 4 2 6 6 0 4 10 7 0 5 5 6 6 7 3 3 0
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 0 8 4 4 10 9 4 0 0 0 0 4 0 2 0 4 4 3
## 163 164 165 166 167 168 169 170 171 172 173
## 8 0 7 0 0 2 3 4 0 0 0
##
## $converged
## [1] TRUE
##
## $boundary
## [1] FALSE
##
## $model
## satell width spine
## 1 8 28.3 bad
## 2 0 22.5 bad
## 3 9 26.0 good
## 4 0 24.8 bad
## 5 4 26.0 bad
## 6 0 23.8 bad
## 7 0 26.5 good
## 8 0 24.7 middle
## 9 0 23.7 good
## 10 0 25.6 bad
## 11 0 24.3 bad
## 12 0 25.8 bad
## 13 11 28.2 bad
## 14 0 21.0 middle
## 15 14 26.0 good
## 16 8 27.1 good
## 17 1 25.2 bad
## 18 1 29.0 bad
## 19 0 24.7 bad
## 20 5 27.4 bad
## 21 4 23.2 middle
## 22 3 25.0 middle
## 23 1 22.5 good
## 24 2 26.7 bad
## 25 3 25.8 bad
## 26 0 26.2 bad
## 27 3 28.7 bad
## 28 5 26.8 good
## 29 0 27.5 bad
## 30 0 24.9 bad
## 31 4 29.3 good
## 32 0 25.8 bad
## 33 0 25.7 middle
## 34 8 25.7 good
## 35 5 26.7 good
## 36 0 23.7 bad
## 37 0 26.8 bad
## 38 6 27.5 bad
## 39 0 23.4 bad
## 40 6 27.9 bad
## 41 3 27.5 bad
## 42 5 26.1 good
## 43 6 27.7 good
## 44 5 30.0 good
## 45 9 28.5 good
## 46 4 28.9 bad
## 47 6 28.2 bad
## 48 4 25.0 bad
## 49 3 28.5 bad
## 50 3 30.3 good
## 51 5 24.7 bad
## 52 5 27.7 bad
## 53 6 27.4 good
## 54 4 22.9 bad
## 55 5 25.7 good
## 56 15 28.3 bad
## 57 3 27.2 bad
## 58 3 26.2 bad
## 59 0 27.8 good
## 60 0 25.5 bad
## 61 0 27.1 bad
## 62 5 24.5 bad
## 63 3 27.0 good
## 64 5 26.0 bad
## 65 1 28.0 bad
## 66 8 30.0 bad
## 67 10 29.0 bad
## 68 0 26.2 bad
## 69 0 26.5 good
## 70 3 26.2 bad
## 71 7 25.6 bad
## 72 1 23.0 bad
## 73 0 23.0 bad
## 74 6 25.4 bad
## 75 0 24.2 bad
## 76 0 22.9 middle
## 77 3 26.0 middle
## 78 4 25.4 bad
## 79 0 25.7 bad
## 80 5 25.1 bad
## 81 0 24.5 middle
## 82 0 27.5 bad
## 83 0 23.1 bad
## 84 4 25.9 good
## 85 0 25.8 bad
## 86 3 27.0 bad
## 87 0 28.5 bad
## 88 0 25.5 good
## 89 0 23.5 bad
## 90 0 24.0 middle
## 91 5 29.7 good
## 92 0 26.8 good
## 93 0 26.7 bad
## 94 0 28.7 good
## 95 0 23.1 bad
## 96 1 29.0 good
## 97 0 25.5 bad
## 98 1 26.5 bad
## 99 1 24.5 bad
## 100 1 28.5 bad
## 101 1 28.2 bad
## 102 1 24.5 bad
## 103 1 27.5 bad
## 104 4 24.7 middle
## 105 1 25.2 good
## 106 1 27.3 bad
## 107 1 26.3 bad
## 108 1 29.0 bad
## 109 2 25.3 bad
## 110 4 26.5 bad
## 111 3 27.8 bad
## 112 6 27.0 bad
## 113 0 25.7 bad
## 114 2 25.0 bad
## 115 2 31.9 bad
## 116 0 23.7 bad
## 117 12 29.3 bad
## 118 0 22.0 bad
## 119 5 25.0 bad
## 120 6 27.0 bad
## 121 6 23.8 bad
## 122 2 30.2 good
## 123 0 26.2 bad
## 124 2 24.2 bad
## 125 3 27.4 bad
## 126 0 25.4 middle
## 127 3 28.4 bad
## 128 4 22.5 bad
## 129 2 26.2 bad
## 130 6 24.9 good
## 131 6 24.5 middle
## 132 0 25.1 bad
## 133 4 28.0 good
## 134 10 25.8 bad
## 135 7 27.9 bad
## 136 0 24.9 bad
## 137 5 28.4 good
## 138 5 27.2 bad
## 139 6 25.0 middle
## 140 6 27.5 bad
## 141 7 33.5 good
## 142 3 30.5 bad
## 143 3 29.0 bad
## 144 0 24.3 good
## 145 0 25.8 bad
## 146 8 25.0 bad
## 147 4 31.7 good
## 148 4 29.5 bad
## 149 10 24.0 bad
## 150 9 30.0 bad
## 151 4 27.6 bad
## 152 0 26.2 bad
## 153 0 23.1 good
## 154 0 22.9 good
## 155 0 24.5 bad
## 156 4 24.7 bad
## 157 0 28.3 bad
## 158 2 23.9 bad
## 159 0 23.8 bad
## 160 4 29.8 middle
## 161 4 26.5 bad
## 162 3 26.0 bad
## 163 8 28.2 bad
## 164 0 25.7 bad
## 165 7 26.5 bad
## 166 0 25.8 bad
## 167 0 24.1 bad
## 168 2 26.2 bad
## 169 3 26.1 bad
## 170 4 29.0 bad
## 171 0 28.0 good
## 172 0 27.0 bad
## 173 0 24.5 middle
##
## $call
## glm(formula = satell ~ width + spine, family = "poisson")
##
## $formula
## satell ~ width + spine
##
## $terms
## satell ~ width + spine
## attr(,"variables")
## list(satell, width, spine)
## attr(,"factors")
## width spine
## satell 0 0
## width 1 0
## spine 0 1
## attr(,"term.labels")
## [1] "width" "spine"
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(satell, width, spine)
## attr(,"dataClasses")
## satell width spine
## "numeric" "numeric" "factor"
##
## $data
## <environment: R_GlobalEnv>
##
## $offset
## NULL
##
## $control
## $control$epsilon
## [1] 1e-08
##
## $control$maxit
## [1] 25
##
## $control$trace
## [1] FALSE
##
##
## $method
## [1] "glm.fit"
##
## $contrasts
## $contrasts$spine
## [1] "contr.treatment"
##
##
## $xlevels
## $xlevels$spine
## [1] "bad" "good" "middle"
##
##
## $coefficients
## (Intercept) width colordarker colorlight colormedium
## -3.08640395 0.14934271 -0.01099663 0.43636303 0.23667654
##
## $residuals
## 1 2 3 4 5 6
## 1.01933885 -1.00000000 1.62309508 -1.00000000 0.80360557 -1.00000000
## 7 8 9 10 11 12
## -1.00000000 -1.00000000 -1.00000000 -1.00000000 -1.00000000 -1.00000000
## 13 14 15 16 17 18
## 1.81836847 -1.00000000 3.98221308 0.97841284 -0.59896596 -0.77263788
## 19 20 21 22 23 24
## -1.00000000 0.44365049 1.16251266 0.01519973 -0.39979478 -0.18771119
## 25 26 27 28 29 30
## 0.40912770 -1.00000000 -0.28665934 0.57898241 -1.00000000 -1.00000000
## 31 32 33 34 35 36
## -0.28780692 -1.00000000 -1.00000000 1.97743208 0.60274033 -1.00000000
## 37 38 39 40 41 42
## -1.00000000 0.70670098 -1.00000000 0.60773309 0.08122171 0.43567340
## 43 44 45 46 47 48
## 0.35663481 -0.02089470 1.79368410 0.16963752 0.53729189 0.65277209
## 49 50 51 52 53 54
## -0.26503150 -0.43827593 1.76785726 0.38039840 0.41879801 1.26160249
## 55 56 57 58 59 60
## 0.86089505 2.78626035 -0.10754761 0.31289831 -1.00000000 -1.00000000
## 61 62 63 64 65 66
## -1.00000000 1.82058802 0.16504880 0.77936181 -0.73601647 0.56656848
## 67 68 69 70 71 72
## 1.27362121 -1.00000000 -1.00000000 0.03620044 2.35060401 -0.29423888
## 73 74 75 76 77 78
## -1.00000000 1.33539713 -1.00000000 -1.00000000 0.35270418 0.55693142
## 79 80 81 82 83 84
## -1.00000000 1.03534067 -1.00000000 -1.00000000 -1.00000000 0.83074324
## 85 86 87 88 89 90
## -1.00000000 0.17793111 -1.00000000 -1.00000000 -1.00000000 -1.00000000
## 91 92 93 94 95 96
## 0.02396949 -1.00000000 -1.00000000 -1.00000000 -1.00000000 -0.77263788
## 97 98 99 100 101 102
## -1.00000000 -0.58154167 -0.43588240 -0.68959066 -0.74378468 -0.55477214
## 103 104 105 106 107 108
## -0.71554984 0.72850478 -0.59896596 -0.62866555 -0.65971983 -0.77263788
## 109 110 111 112 113 114
## -0.20982123 0.32106715 -0.18403820 0.83902145 -1.00000000 -0.17361396
## 115 116 117 118 119 120
## -0.70511150 -1.00000000 2.34198761 -1.00000000 1.06596511 1.33009759
## 121 122 123 124 125 126
## 2.75769913 -0.68868868 -1.00000000 -0.06874215 -0.13380970 -1.00000000
## 127 128 129 130 131 132
## -0.05476039 2.07555043 -0.30919971 1.51646040 1.18781627 -1.00000000
## 133 134 135 136 137 138
## 0.05593411 3.69709233 0.87568861 -1.00000000 0.24337848 0.88460840
## 139 140 141 142 143 144
## 1.47915813 0.70670098 -0.18725945 -0.45480572 -0.13577528 -1.00000000
## 145 146 147 148 149 150
## -1.00000000 3.23453822 -0.39234151 -0.15598776 5.07853624 0.76238954
## 151 152 153 154 155 156
## 0.12093468 -1.00000000 -1.00000000 -1.00000000 -1.00000000 0.72850478
## 157 158 159 160 161 162
## -1.00000000 -0.02607041 -1.00000000 0.02253562 0.32106715 0.06761709
## 163 164 165 166 167 168
## 1.04972252 -1.00000000 1.31186752 -1.00000000 -1.00000000 -0.12473446
## 169 170 171 172 173
## 0.33265263 0.15229962 -1.00000000 -1.00000000 -1.00000000
##
## $fitted.values
## 1 2 3 4 5 6 7 8
## 3.961693 1.314961 3.431061 1.853907 2.217780 2.023096 3.697071 1.826426
## 9 10 11 12 13 14 15 16
## 1.993107 2.089176 1.720516 2.727307 3.902967 1.039559 2.809996 4.043645
## 17 18 19 20 21 22 23 24
## 2.493554 4.398270 1.806452 3.463442 1.849700 2.955084 1.666097 2.462178
## 25 26 27 28 29 30 31 32
## 2.128977 2.260031 4.205564 3.166596 2.744295 2.384301 5.616454 3.330096
## 33 34 35 36 37 38 39 40
## 2.686879 2.686879 3.119657 1.555850 3.166596 3.515554 1.487682 3.731963
## 41 42 43 44 45 46 47 48
## 2.774639 3.482686 4.422708 5.106703 3.221553 3.419863 3.902967 2.420176
## 49 50 51 52 53 54 55 56
## 4.081808 5.340700 1.806452 3.622143 4.228932 1.768657 2.686879 3.961693
## 57 58 59 60 61 62 63 64
## 3.361524 2.285021 3.676643 2.035698 2.613744 1.772680 2.574999 2.809996
## 65 66 67 68 69 70 71 72
## 3.788115 5.106703 4.398270 2.895193 3.027855 2.895193 2.089176 1.416910
## 73 74 75 76 77 78 79 80
## 1.416910 2.569156 1.695012 1.768657 2.217780 2.569156 2.120610 2.456591
## 81 82 83 84 85 86 87 88
## 1.772680 2.744295 1.438229 2.184905 2.727307 2.546838 4.081808 2.035698
## 89 90 91 92 93 94 95 96
## 1.510066 2.084435 4.882958 3.166596 2.435251 4.205564 1.438229 4.398270
## 97 98 99 100 101 102 103 104
## 2.058207 2.389724 1.772680 3.221553 3.902967 2.246041 3.515554 2.314139
## 105 106 107 108 109 110 111 112
## 2.493554 2.692990 2.938755 4.398270 2.531073 3.027855 3.676643 3.262605
## 113 114 115 116 117 118 119 120
## 2.120610 2.420176 6.782225 1.555850 3.590678 1.220348 2.420176 2.574999
## 121 122 123 124 125 126 127 128
## 1.596722 6.424437 2.285021 2.147633 3.463442 2.569156 3.173798 1.300580
## 129 130 131 132 133 134 135 136
## 2.895193 2.384301 2.742461 2.456591 3.788115 2.128977 3.731963 2.384301
## 137 138 139 140 141 142 143 144
## 4.021302 2.653071 2.420176 3.515554 8.612835 5.502626 3.471319 2.179947
## 145 146 147 148 149 150 151 152
## 2.727307 1.889226 6.582645 4.739268 1.645133 5.106703 3.568451 2.895193
## 153 154 155 156 157 158 159 160
## 1.822281 1.768657 1.753293 2.314139 3.961693 2.053537 1.596722 3.911844
## 161 162 163 164 165 166 167 168
## 3.027855 2.809996 3.902967 2.097418 3.027855 2.727307 1.669886 2.285021
## 169 170 171 172 173
## 2.251149 3.471319 4.625364 2.546838 2.246041
##
## $effects
## (Intercept) width colordarker colorlight colormedium
## -25.70790076 8.14203844 -1.29311302 1.68316045 2.00321341
##
## -1.42515897 -2.52603320 -1.53103412 -1.41184465 -1.66245247
##
## -1.47590127 -1.71816137 3.42147244 -0.62857318 6.60125136
##
## 1.31339601 -0.99140815 -1.83401361 -0.92413274 0.69327497
##
## 1.59405598 -0.46366959 -0.48635289 -0.56333778 1.02197883
##
## -1.07698176 -0.78494791 0.92423313 -1.22814126 -1.57979109
##
## -1.55309111 -2.37284941 -1.70223047 3.17828756 0.96271307
##
## -0.83367783 -1.88555547 1.18808388 -0.80818051 1.01807186
##
## -0.17564683 0.24188782 0.04172388 -0.32082632 2.84989943
##
## -0.08123305 0.89058665 0.97658332 -0.72185421 -1.30597921
##
## 2.79597946 0.57763122 0.18048589 1.69818881 1.34809330
##
## 5.36978512 -0.32054924 0.22856225 -2.06857800 -1.00294740
##
## -1.90607439 2.25216444 -0.01928074 1.23229793 -1.59338721
##
## 1.00672389 2.45741179 -1.78332938 -1.83375763 -0.02020678
##
## 3.18050404 -0.46954245 -1.30963833 2.08802721 -1.46242048
##
## -1.30953618 0.29017571 0.84025664 -1.67771020 1.58051141
##
## -1.50322366 -1.22814126 -1.32175803 0.99746640 -1.71816137
##
## 0.71203090 -2.20674663 -1.00294740 -0.81659858 -1.45214949
##
## -0.20184587 -1.88555547 -1.13294103 -2.24782931 -1.32175803
##
## -1.83401361 -1.64732891 -1.15785984 -0.75214604 -1.60725193
##
## -1.64029914 -0.85453259 -1.47860958 1.07892359 -0.99140815
##
## -1.33168671 -1.21665343 -1.83401361 -0.38279130 0.46499607
##
## -0.50400605 1.40089981 -1.67771020 -0.30901851 -2.24667266
##
## -0.83367783 4.86240268 -1.19418600 1.61938423 1.85025032
##
## 3.33860720 -2.72096923 -1.75605229 -0.11488943 -0.38139725
##
## -1.65528450 -0.46091253 2.77150824 -0.60791431 2.30592146
##
## 1.51112646 -1.60958312 -0.05200739 5.81944347 1.53571606
##
## -1.57979109 0.30689210 1.14620773 2.26218514 1.18808388
##
## -1.10221716 -1.37341718 -0.65445028 -1.49355590 -1.71816137
##
## 4.86733504 -1.40104843 -0.58225401 6.36068214 1.44924063
##
## 0.08682711 -1.78332938 -1.33442646 -1.30953618 -0.90534661
##
## 1.07892359 -2.16638800 -0.04293633 -1.40967609 -0.41201951
##
## 0.46499607 0.03919717 1.90294096 -1.02360244 2.18906134
##
## -1.71816137 -1.44905859 -0.43297593 0.25938327 -0.11772418
##
## -2.88739381 -1.16780427 -1.52178656
##
## $R
## (Intercept) width colordarker colorlight colormedium
## (Intercept) -22.47221 -608.26706 -2.002481 -2.1804710 -13.928315
## width 0.00000 49.82327 -1.163887 0.2972126 2.361050
## colordarker 0.00000 0.00000 6.295681 -0.6386012 -3.993720
## colorlight 0.00000 0.00000 0.000000 6.6143329 -5.083266
## colormedium 0.00000 0.00000 0.000000 0.0000000 8.463929
##
## $rank
## [1] 5
##
## $qr
## $qr
## (Intercept) width colordarker colorlight colormedium
## 1 -22.47220882 -6.082671e+02 -2.002480816 -2.18047102 -1.392831e+01
## 2 0.05102825 4.982327e+01 -1.163887048 0.29721258 2.361050e+00
## 3 0.08242681 4.341624e-02 6.295681422 -0.63860117 -3.993720e+00
## 4 0.06058962 6.470795e-02 0.027634715 6.61433288 -5.083266e+00
## 5 0.06626946 3.490577e-02 0.024720501 0.02665536 8.463929e+00
## 6 0.06329400 9.614418e-02 0.033249513 0.02534417 -6.690187e-02
## 7 0.08556244 2.577188e-02 0.028955956 -0.25624790 2.182863e-02
## 8 0.06013887 6.693906e-02 0.027845425 0.02412525 1.029703e-01
## 9 0.06282313 9.826249e-02 0.033437034 0.02515047 -6.720100e-02
## 10 0.06431936 4.548280e-02 0.025773981 0.02584984 1.174704e-01
## 11 0.05836917 7.549995e-02 0.028642195 0.02339615 9.697883e-02
## 12 0.07348881 4.533764e-02 0.028430945 0.02954710 -5.903552e-02
## 13 0.08791272 -4.092862e-02 0.019405985 0.03551971 -4.386110e-02
## 14 0.04537113 1.262187e-01 -0.129322405 -0.03755701 -2.522201e-02
## 15 0.07459455 3.929080e-02 0.027826009 0.03000393 -5.803151e-02
## 16 0.08948305 2.736617e-03 0.026566244 -0.26794550 2.963874e-02
## 17 0.07026898 6.236762e-02 0.030103770 0.02821790 -6.179659e-02
## 18 0.09332439 -7.712240e-02 0.015432481 0.03776754 -3.709144e-02
## 19 0.05980923 6.657215e-02 -0.185794102 -0.04932672 -5.179802e-03
## 20 0.08281485 -8.673118e-03 0.022866766 0.03340558 -4.972092e-02
## 21 0.06052084 1.083101e-01 0.034306346 0.02420392 -6.857643e-02
## 22 0.07649607 7.479505e-02 0.033830548 -0.22918960 4.961754e-03
## 23 0.05743867 1.209290e-01 0.035342430 0.02293825 -7.018378e-02
## 24 0.06982549 1.473303e-02 0.022663581 0.02812583 1.372688e-01
## 25 0.06492927 4.005697e-02 -0.206643210 -0.05349073 3.435794e-03
## 26 0.06689786 2.920206e-02 -0.214760766 -0.05509054 6.934030e-03
## 27 0.09125704 -6.306582e-02 0.016985716 0.03690841 -3.974223e-02
## 28 0.07918640 1.313655e-02 0.025153749 0.03190293 -5.356873e-02
## 29 0.07371747 -1.104530e-02 -0.243287334 -0.06062778 1.979608e-02
## 30 0.06871235 7.028361e-02 0.030863824 0.02757587 -6.304224e-02
## 31 0.10545941 -1.014205e-01 0.015249139 -0.31559411 6.435814e-02
## 32 0.08120497 5.009798e-02 0.031416129 -0.24324456 1.350706e-02
## 33 0.07294211 4.829033e-02 0.028724358 0.02932130 -5.952152e-02
## 34 0.07294211 4.829033e-02 0.028724358 0.02932130 -5.952152e-02
## 35 0.07859731 1.658387e-02 0.025510689 0.03165914 -5.416713e-02
## 36 0.05550586 8.681745e-02 -0.168583696 -0.04582318 -1.184734e-02
## 37 0.07918640 1.313655e-02 0.025153749 0.03190293 -5.356873e-02
## 38 0.08343555 -1.250139e-02 0.022460596 0.03366282 -4.903530e-02
## 39 0.05427628 9.223845e-02 -0.163722035 -0.04482146 -1.365017e-02
## 40 0.08596524 -2.838987e-02 0.020761304 0.03471168 -4.616056e-02
## 41 0.07412377 -1.110618e-02 0.019953892 0.02990589 1.532400e-01
## 42 0.08304460 3.999602e-02 0.030403280 -0.24873461 1.697301e-02
## 43 0.09358331 -2.246379e-02 0.023896735 -0.28017708 3.811876e-02
## 44 0.10055983 -1.284580e-01 0.009668002 0.04077825 -2.721237e-02
## 45 0.07987058 -4.799194e-02 0.015972110 0.03229010 1.752513e-01
## 46 0.08229218 -6.429379e-02 0.014177796 0.03329614 1.847399e-01
## 47 0.08791272 -4.092862e-02 0.019405985 0.03551971 -4.386110e-02
## 48 0.06922736 6.768797e-02 0.030615945 0.02778824 -6.263669e-02
## 49 0.08990431 -5.402092e-02 0.017978603 0.03634654 -4.143376e-02
## 50 0.10283793 -1.452833e-01 0.007751426 0.04172739 -2.391573e-02
## 51 0.05980923 6.657215e-02 -0.185794102 -0.04932672 -5.179802e-03
## 52 0.08469095 -2.032926e-02 0.021626049 0.03418323 -4.762471e-02
## 53 0.09151022 -9.583775e-03 0.025267723 -0.27399304 3.379226e-02
## 54 0.05918016 1.139185e-01 0.034775351 0.02365316 -6.930918e-02
## 55 0.07294211 4.829033e-02 0.028724358 0.02932130 -5.952152e-02
## 56 0.08857163 -4.523031e-02 0.018938323 0.03579321 -4.306642e-02
## 57 0.08158726 -1.184755e-03 0.023657332 0.03289700 -5.105356e-02
## 58 0.06726658 2.936301e-02 0.024161188 0.02706747 1.279723e-01
## 59 0.08532572 -2.433015e-02 0.021197496 0.03444644 -4.689941e-02
## 60 0.06349093 4.776067e-02 -0.200747084 -0.05232143 9.437720e-04
## 61 0.07194254 2.200184e-03 0.021358716 0.02900222 1.450807e-01
## 62 0.05924742 7.129138e-02 0.028252909 0.02375791 9.994096e-02
## 63 0.07140733 5.404561e-03 0.021694117 0.02878060 1.430956e-01
## 64 0.07459455 3.929080e-02 0.027826009 0.03000393 -5.803151e-02
## 65 0.08660956 -3.250908e-02 0.020317383 0.03497897 -4.540800e-02
## 66 0.10055983 -1.284580e-01 0.009668002 0.04077825 -2.721237e-02
## 67 0.09332439 -7.712240e-02 0.015432481 0.03776754 -3.709144e-02
## 68 0.07571693 3.305174e-02 0.027196432 0.03046782 -5.698393e-02
## 69 0.07743223 2.332302e-02 0.026204540 0.03117712 -5.532846e-02
## 70 0.07571693 3.305174e-02 0.027196432 0.03046782 -5.698393e-02
## 71 0.06431936 4.548280e-02 0.025773981 0.02584984 1.174704e-01
## 72 0.05296944 9.957408e-02 0.030759155 0.02117521 7.927324e-02
## 73 0.05296944 9.957408e-02 0.030759155 0.02117521 7.927324e-02
## 74 0.07132627 5.687185e-02 0.029569252 0.02865419 -6.091703e-02
## 75 0.05793495 7.755137e-02 0.028830154 0.02321734 9.552254e-02
## 76 0.05918016 1.139185e-01 0.034775351 0.02365316 -6.930918e-02
## 77 0.06626946 3.490577e-02 0.024720501 0.02665536 1.243942e-01
## 78 0.07132627 5.687185e-02 0.029569252 0.02865419 -6.091703e-02
## 79 0.06480144 4.290091e-02 0.025518590 0.02604891 1.191728e-01
## 80 0.06974622 6.504947e-02 0.030362616 0.02800225 -6.222151e-02
## 81 0.05924742 7.129138e-02 0.028252909 0.02375791 9.994096e-02
## 82 0.07371747 -1.104530e-02 -0.243287334 -0.06062778 1.979608e-02
## 83 0.05336645 9.791336e-02 0.030620284 0.02133830 8.054429e-02
## 84 0.06577646 3.761287e-02 0.024991915 0.02645166 1.226345e-01
## 85 0.07348881 4.533764e-02 0.028430945 0.02954710 -5.903552e-02
## 86 0.07101592 5.374937e-03 -0.231913577 -0.05843511 1.456686e-02
## 87 0.08990431 -5.402092e-02 0.017978603 0.03634654 -4.143376e-02
## 88 0.06349093 4.776067e-02 -0.200747084 -0.05232143 9.437720e-04
## 89 0.05468308 9.046337e-02 -0.165327673 -0.04515291 -1.305889e-02
## 90 0.06424634 9.179528e-02 0.032860344 0.02573606 -6.627873e-02
## 91 0.09833219 -1.123069e-01 0.011495860 0.03985068 -3.035122e-02
## 92 0.07918640 1.313655e-02 0.025153749 0.03190293 -5.356873e-02
## 93 0.06944275 1.465227e-02 -0.225334053 -0.05715774 1.160179e-02
## 94 0.09125704 -6.306582e-02 0.016985716 0.03690841 -3.974223e-02
## 95 0.05336645 9.791336e-02 0.030620284 0.02133830 8.054429e-02
## 96 0.09332439 -7.712240e-02 0.015432481 0.03776754 -3.709144e-02
## 97 0.06384087 4.802391e-02 0.026024160 0.02565229 1.157868e-01
## 98 0.06879045 2.072007e-02 0.023279995 0.02769762 1.334890e-01
## 99 0.05924742 7.129138e-02 0.028252909 0.02375791 9.994096e-02
## 100 0.07987058 -4.799194e-02 0.015972110 0.03229010 1.752513e-01
## 101 0.08791272 -4.092862e-02 0.019405985 0.03551971 -4.386110e-02
## 102 0.06669036 8.024734e-02 0.031802176 0.02674249 -6.457064e-02
## 103 0.08343555 -1.250139e-02 0.022460596 0.03366282 -4.903530e-02
## 104 0.06769381 7.534827e-02 0.031343501 0.02715599 -6.382497e-02
## 105 0.07026898 6.236762e-02 0.030103770 0.02821790 -6.179659e-02
## 106 0.07302501 -4.354129e-03 0.020669098 0.02945059 1.491161e-01
## 107 0.07628443 2.985874e-02 0.026872215 0.03070244 -5.644345e-02
## 108 0.09332439 -7.712240e-02 0.015432481 0.03776754 -3.709144e-02
## 109 0.07079565 5.964192e-02 0.029839338 0.02843521 -6.136181e-02
## 110 0.07743223 2.332302e-02 0.026204540 0.03117712 -5.532846e-02
## 111 0.08532572 -2.433015e-02 0.021197496 0.03444644 -4.689941e-02
## 112 0.08037787 6.083508e-03 0.024419437 0.03239615 -5.233576e-02
## 113 0.06480144 4.290091e-02 0.025518590 0.02604891 1.191728e-01
## 114 0.06922736 6.768797e-02 0.030615945 0.02778824 -6.263669e-02
## 115 0.11588849 -2.473526e-01 -0.004100151 0.04717506 -3.432310e-03
## 116 0.05550586 8.681745e-02 -0.168583696 -0.04582318 -1.184734e-02
## 117 0.08432246 -8.109308e-02 -0.288793183 -0.06922503 4.189540e-02
## 118 0.04915821 1.145818e-01 0.031948818 0.01961124 6.733430e-02
## 119 0.06922736 6.768797e-02 0.030615945 0.02778824 -6.263669e-02
## 120 0.07140733 5.404561e-03 0.021694117 0.02878060 1.430956e-01
## 121 0.05623010 8.541405e-02 0.029538717 0.02251565 8.985877e-02
## 122 0.11279031 -1.542562e-01 0.009282346 -0.33744890 8.170737e-02
## 123 0.06726658 2.936301e-02 0.024161188 0.02706747 1.279723e-01
## 124 0.06521301 8.729375e-02 0.032451936 0.02613401 -6.562169e-02
## 125 0.08281485 -8.673118e-03 0.022866766 0.03340558 -4.972092e-02
## 126 0.07132627 5.687185e-02 0.029569252 0.02865419 -6.091703e-02
## 127 0.07927640 -4.405924e-02 0.016402056 0.03204337 1.729421e-01
## 128 0.05074855 1.068439e-01 -0.149919169 -0.04194577 -1.855611e-02
## 129 0.07571693 3.305174e-02 0.027196432 0.03046782 -5.698393e-02
## 130 0.06871235 7.028361e-02 0.030863824 0.02757587 -6.304224e-02
## 131 0.07369270 8.867313e-02 0.035141331 -0.22082069 1.064172e-04
## 132 0.06974622 6.504947e-02 0.030362616 0.02800225 -6.222151e-02
## 133 0.08660956 -3.250908e-02 0.020317383 0.03497897 -4.540800e-02
## 134 0.06492927 4.005697e-02 -0.206643210 -0.05349073 3.435794e-03
## 135 0.08596524 -2.838987e-02 0.020761304 0.03471168 -4.616056e-02
## 136 0.06871235 7.028361e-02 0.030863824 0.02757587 -6.304224e-02
## 137 0.08923548 -4.959418e-02 0.018462561 0.03606881 -4.225737e-02
## 138 0.07248175 -1.052531e-03 0.021017067 0.02922555 1.470874e-01
## 139 0.06922736 6.768797e-02 0.030615945 0.02778824 -6.263669e-02
## 140 0.08343555 -1.250139e-02 0.022460596 0.03366282 -4.903530e-02
## 141 0.13059522 -3.729883e-01 -0.019084590 0.05333340 2.263513e-02
## 142 0.10438527 -1.568857e-01 0.006422904 0.04237238 -2.162758e-02
## 143 0.08290897 -6.851519e-02 0.013710147 0.03355251 1.871761e-01
## 144 0.06570179 8.498462e-02 0.032240365 0.02633528 -6.528019e-02
## 145 0.07348881 4.533764e-02 0.028430945 0.02954710 -5.903552e-02
## 146 0.06116416 5.980407e-02 -0.191273292 -0.05042911 -2.969772e-03
## 147 0.11417064 -2.333870e-01 -0.002458748 0.04645702 -6.277658e-03
## 148 0.09687459 -1.019033e-01 0.012666625 0.03924405 -3.235878e-02
## 149 0.05707616 8.155051e-02 0.029192981 0.02286380 9.265870e-02
## 150 0.10055983 -1.284580e-01 0.009668002 0.04077825 -2.721237e-02
## 151 0.08406091 -1.638656e-02 0.022047053 0.03392203 -4.833662e-02
## 152 0.07571693 3.305174e-02 0.027196432 0.03046782 -5.698393e-02
## 153 0.06007060 1.102137e-01 0.034466951 0.02401892 -6.882818e-02
## 154 0.05918016 1.139185e-01 0.034775351 0.02365316 -6.930918e-02
## 155 0.05892266 7.090060e-02 -0.182224270 -0.04860522 -6.597742e-03
## 156 0.06769381 7.534827e-02 0.031343501 0.02715599 -6.382497e-02
## 157 0.08857163 -4.523031e-02 0.018938323 0.03579321 -4.306642e-02
## 158 0.06376839 9.398859e-02 0.033057303 0.02553937 -6.659449e-02
## 159 0.05623010 8.541405e-02 0.029538717 0.02251565 8.985877e-02
## 160 0.08801263 -1.044905e-01 0.009680175 0.03567575 2.076288e-01
## 161 0.07743223 2.332302e-02 0.026204540 0.03117712 -5.532846e-02
## 162 0.07459455 3.929080e-02 0.027826009 0.03000393 -5.803151e-02
## 163 0.08791272 -4.092862e-02 0.019405985 0.03551971 -4.386110e-02
## 164 0.06444624 4.266575e-02 -0.204659814 -0.05309809 2.592813e-03
## 165 0.07743223 2.332302e-02 0.026204540 0.03117712 -5.532846e-02
## 166 0.07348881 4.533764e-02 0.028430945 0.02954710 -5.903552e-02
## 167 0.05750395 7.956809e-02 0.029013731 0.02303989 9.408255e-02
## 168 0.06726658 2.936301e-02 0.024161188 0.02706747 1.279723e-01
## 169 0.06676616 3.215598e-02 0.024443614 0.02686063 1.261734e-01
## 170 0.08290897 -6.851519e-02 0.013710147 0.03355251 1.871761e-01
## 171 0.09570336 -3.592246e-02 0.022450661 -0.28650068 4.262395e-02
## 172 0.07101592 5.374937e-03 -0.231913577 -0.05843511 1.456686e-02
## 173 0.06669036 8.024734e-02 0.031802176 0.02674249 -6.457064e-02
##
## $rank
## [1] 5
##
## $qraux
## [1] 1.088572 1.107433 1.030748 1.024311 1.124394
##
## $pivot
## [1] 1 2 3 4 5
##
## $tol
## [1] 1e-11
##
## attr(,"class")
## [1] "qr"
##
## $family
##
## Family: poisson
## Link function: log
##
##
## $linear.predictors
## 1 2 3 4 5 6 7
## 1.3766714 0.2738071 1.2328696 0.6172953 0.7965066 0.7046292 1.3075410
## 8 9 10 11 12 13 14
## 0.6023611 0.6896949 0.7367695 0.5426240 1.0033146 1.3617371 0.0387964
## 15 16 17 18 19 20 21
## 1.0331831 1.3971466 0.9137090 1.4812113 0.5913644 1.2422629 0.6150235
## 22 23 24 25 26 27 28
## 1.0835269 0.5104836 0.9010465 0.7556414 0.8153785 1.4364085 1.1526573
## 29 30 31 32 33 34 35
## 1.0095240 0.8689062 1.7257006 1.2030011 0.9883803 0.9883803 1.1377230
## 36 37 38 39 40 41 42
## 0.4420217 1.1526573 1.2571972 0.3972189 1.3169343 1.0205207 1.2478039
## 43 44 45 46 47 48 49
## 1.4867522 1.6305540 1.1698634 1.2296005 1.3617371 0.8838404 1.4065399
## 50 51 52 53 54 55 56
## 1.6753568 0.5913644 1.2870658 1.4419494 0.5702207 0.9883803 1.3766714
## 57 58 59 60 61 62 63
## 1.2123944 0.8263751 1.3020000 0.7108386 0.9607836 0.5724925 0.9458493
## 64 65 66 67 68 69 70
## 1.0331831 1.3318686 1.6305540 1.4812113 1.0630517 1.1078545 1.0630517
## 71 72 73 74 75 76 77
## 0.7367695 0.3484785 0.3484785 0.9435775 0.5276897 0.5702207 0.7965066
## 78 79 80 81 82 83 84
## 0.9435775 0.7517038 0.8987747 0.5724925 1.0095240 0.3634127 0.7815723
## 85 86 87 88 89 90 91
## 1.0033146 0.9348527 1.4065399 0.7108386 0.4121532 0.7344977 1.5857512
## 92 93 94 95 96 97 98
## 1.1526573 0.8900499 1.4364085 0.3634127 1.4812113 0.7218352 0.8711780
## 99 100 101 102 103 104 105
## 0.5724925 1.1698634 1.3617371 0.8091691 1.2571972 0.8390376 0.9137090
## 106 107 108 109 110 111 112
## 0.9906521 1.0779860 1.4812113 0.9286432 1.1078545 1.3020000 1.1825259
## 113 114 115 116 117 118 119
## 0.7517038 0.8838404 1.9143052 0.4420217 1.2783409 0.1991357 0.8838404
## 120 121 122 123 124 125 126
## 0.9458493 0.4679526 1.8601090 0.8263751 0.7643663 1.2422629 0.9435775
## 127 128 129 130 131 132 133
## 1.1549291 0.2628105 1.0630517 0.8689062 1.0088556 0.8987747 1.3318686
## 134 135 136 137 138 139 140
## 0.7556414 1.3169343 0.8689062 1.3916057 0.9757179 0.8838404 1.2571972
## 141 142 143 144 145 146 147
## 2.1532535 1.7052254 1.2445347 0.7793005 1.0033146 0.6361673 1.8844366
## 148 149 150 151 152 153 154
## 1.5558826 0.4978212 1.6305540 1.2721315 1.0630517 0.6000893 0.5702207
## 155 156 157 158 159 160 161
## 0.5614959 0.8390376 1.3766714 0.7195634 0.4679526 1.3640089 1.1078545
## 162 163 164 165 166 167 168
## 1.0331831 1.3617371 0.7407072 1.1078545 1.0033146 0.5127554 0.8263751
## 169 170 171 172 173
## 0.8114409 1.2445347 1.5315551 0.9348527 0.8091691
##
## $deviance
## [1] 559.3448
##
## $aic
## [1] 924.6425
##
## $null.deviance
## [1] 632.7917
##
## $iter
## [1] 6
##
## $weights
## 1 2 3 4 5 6 7 8
## 3.961693 1.314961 3.431061 1.853907 2.217780 2.023096 3.697071 1.826426
## 9 10 11 12 13 14 15 16
## 1.993107 2.089176 1.720516 2.727307 3.902967 1.039563 2.809996 4.043645
## 17 18 19 20 21 22 23 24
## 2.493554 4.398270 1.806458 3.463442 1.849700 2.955084 1.666097 2.462178
## 25 26 27 28 29 30 31 32
## 2.128985 2.260039 4.205564 3.166596 2.744305 2.384301 5.616454 3.330096
## 33 34 35 36 37 38 39 40
## 2.686879 2.686879 3.119657 1.555855 3.166596 3.515554 1.487687 3.731963
## 41 42 43 44 45 46 47 48
## 2.774639 3.482686 4.422708 5.106703 3.221552 3.419863 3.902967 2.420176
## 49 50 51 52 53 54 55 56
## 4.081808 5.340700 1.806458 3.622143 4.228932 1.768657 2.686879 3.961693
## 57 58 59 60 61 62 63 64
## 3.361524 2.285021 3.676643 2.035705 2.613744 1.772680 2.574999 2.809996
## 65 66 67 68 69 70 71 72
## 3.788115 5.106703 4.398270 2.895193 3.027855 2.895193 2.089176 1.416910
## 73 74 75 76 77 78 79 80
## 1.416910 2.569156 1.695012 1.768657 2.217780 2.569156 2.120610 2.456591
## 81 82 83 84 85 86 87 88
## 1.772680 2.744305 1.438229 2.184905 2.727307 2.546848 4.081808 2.035705
## 89 90 91 92 93 94 95 96
## 1.510071 2.084435 4.882958 3.166596 2.435260 4.205564 1.438229 4.398270
## 97 98 99 100 101 102 103 104
## 2.058207 2.389724 1.772680 3.221552 3.902967 2.246041 3.515554 2.314139
## 105 106 107 108 109 110 111 112
## 2.493554 2.692990 2.938755 4.398270 2.531073 3.027855 3.676643 3.262605
## 113 114 115 116 117 118 119 120
## 2.120610 2.420176 6.782224 1.555855 3.590691 1.220348 2.420176 2.574999
## 121 122 123 124 125 126 127 128
## 1.596722 6.424437 2.285021 2.147633 3.463442 2.569156 3.173798 1.300585
## 129 130 131 132 133 134 135 136
## 2.895193 2.384301 2.742461 2.456591 3.788115 2.128985 3.731963 2.384301
## 137 138 139 140 141 142 143 144
## 4.021302 2.653071 2.420176 3.515554 8.612834 5.502625 3.471319 2.179947
## 145 146 147 148 149 150 151 152
## 2.727307 1.889233 6.582645 4.739268 1.645133 5.106703 3.568451 2.895193
## 153 154 155 156 157 158 159 160
## 1.822282 1.768657 1.753300 2.314139 3.961693 2.053537 1.596722 3.911844
## 161 162 163 164 165 166 167 168
## 3.027855 2.809996 3.902967 2.097426 3.027855 2.727307 1.669886 2.285021
## 169 170 171 172 173
## 2.251149 3.471319 4.625364 2.546848 2.246041
##
## $prior.weights
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 163 164 165 166 167 168 169 170 171 172 173
## 1 1 1 1 1 1 1 1 1 1 1
##
## $df.residual
## [1] 168
##
## $df.null
## [1] 172
##
## $y
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 8 0 9 0 4 0 0 0 0 0 0 0 11 0 14 8 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 0 5 4 3 1 2 3 0 3 5 0 0 4 0 0 8 5 0
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 0 6 0 6 3 5 6 5 9 4 6 4 3 3 5 5 6 4
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 5 15 3 3 0 0 0 5 3 5 1 8 10 0 0 3 7 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 0 6 0 0 3 4 0 5 0 0 0 4 0 3 0 0 0 0
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 5 0 0 0 0 1 0 1 1 1 1 1 1 4 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 2 4 3 6 0 2 2 0 12 0 5 6 6 2 0 2 3 0
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 3 4 2 6 6 0 4 10 7 0 5 5 6 6 7 3 3 0
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 0 8 4 4 10 9 4 0 0 0 0 4 0 2 0 4 4 3
## 163 164 165 166 167 168 169 170 171 172 173
## 8 0 7 0 0 2 3 4 0 0 0
##
## $converged
## [1] TRUE
##
## $boundary
## [1] FALSE
##
## $model
## satell width color
## 1 8 28.3 medium
## 2 0 22.5 dark
## 3 9 26.0 light
## 4 0 24.8 dark
## 5 4 26.0 dark
## 6 0 23.8 medium
## 7 0 26.5 light
## 8 0 24.7 dark
## 9 0 23.7 medium
## 10 0 25.6 dark
## 11 0 24.3 dark
## 12 0 25.8 medium
## 13 11 28.2 medium
## 14 0 21.0 darker
## 15 14 26.0 medium
## 16 8 27.1 light
## 17 1 25.2 medium
## 18 1 29.0 medium
## 19 0 24.7 darker
## 20 5 27.4 medium
## 21 4 23.2 medium
## 22 3 25.0 light
## 23 1 22.5 medium
## 24 2 26.7 dark
## 25 3 25.8 darker
## 26 0 26.2 darker
## 27 3 28.7 medium
## 28 5 26.8 medium
## 29 0 27.5 darker
## 30 0 24.9 medium
## 31 4 29.3 light
## 32 0 25.8 light
## 33 0 25.7 medium
## 34 8 25.7 medium
## 35 5 26.7 medium
## 36 0 23.7 darker
## 37 0 26.8 medium
## 38 6 27.5 medium
## 39 0 23.4 darker
## 40 6 27.9 medium
## 41 3 27.5 dark
## 42 5 26.1 light
## 43 6 27.7 light
## 44 5 30.0 medium
## 45 9 28.5 dark
## 46 4 28.9 dark
## 47 6 28.2 medium
## 48 4 25.0 medium
## 49 3 28.5 medium
## 50 3 30.3 medium
## 51 5 24.7 darker
## 52 5 27.7 medium
## 53 6 27.4 light
## 54 4 22.9 medium
## 55 5 25.7 medium
## 56 15 28.3 medium
## 57 3 27.2 medium
## 58 3 26.2 dark
## 59 0 27.8 medium
## 60 0 25.5 darker
## 61 0 27.1 dark
## 62 5 24.5 dark
## 63 3 27.0 dark
## 64 5 26.0 medium
## 65 1 28.0 medium
## 66 8 30.0 medium
## 67 10 29.0 medium
## 68 0 26.2 medium
## 69 0 26.5 medium
## 70 3 26.2 medium
## 71 7 25.6 dark
## 72 1 23.0 dark
## 73 0 23.0 dark
## 74 6 25.4 medium
## 75 0 24.2 dark
## 76 0 22.9 medium
## 77 3 26.0 dark
## 78 4 25.4 medium
## 79 0 25.7 dark
## 80 5 25.1 medium
## 81 0 24.5 dark
## 82 0 27.5 darker
## 83 0 23.1 dark
## 84 4 25.9 dark
## 85 0 25.8 medium
## 86 3 27.0 darker
## 87 0 28.5 medium
## 88 0 25.5 darker
## 89 0 23.5 darker
## 90 0 24.0 medium
## 91 5 29.7 medium
## 92 0 26.8 medium
## 93 0 26.7 darker
## 94 0 28.7 medium
## 95 0 23.1 dark
## 96 1 29.0 medium
## 97 0 25.5 dark
## 98 1 26.5 dark
## 99 1 24.5 dark
## 100 1 28.5 dark
## 101 1 28.2 medium
## 102 1 24.5 medium
## 103 1 27.5 medium
## 104 4 24.7 medium
## 105 1 25.2 medium
## 106 1 27.3 dark
## 107 1 26.3 medium
## 108 1 29.0 medium
## 109 2 25.3 medium
## 110 4 26.5 medium
## 111 3 27.8 medium
## 112 6 27.0 medium
## 113 0 25.7 dark
## 114 2 25.0 medium
## 115 2 31.9 medium
## 116 0 23.7 darker
## 117 12 29.3 darker
## 118 0 22.0 dark
## 119 5 25.0 medium
## 120 6 27.0 dark
## 121 6 23.8 dark
## 122 2 30.2 light
## 123 0 26.2 dark
## 124 2 24.2 medium
## 125 3 27.4 medium
## 126 0 25.4 medium
## 127 3 28.4 dark
## 128 4 22.5 darker
## 129 2 26.2 medium
## 130 6 24.9 medium
## 131 6 24.5 light
## 132 0 25.1 medium
## 133 4 28.0 medium
## 134 10 25.8 darker
## 135 7 27.9 medium
## 136 0 24.9 medium
## 137 5 28.4 medium
## 138 5 27.2 dark
## 139 6 25.0 medium
## 140 6 27.5 medium
## 141 7 33.5 medium
## 142 3 30.5 medium
## 143 3 29.0 dark
## 144 0 24.3 medium
## 145 0 25.8 medium
## 146 8 25.0 darker
## 147 4 31.7 medium
## 148 4 29.5 medium
## 149 10 24.0 dark
## 150 9 30.0 medium
## 151 4 27.6 medium
## 152 0 26.2 medium
## 153 0 23.1 medium
## 154 0 22.9 medium
## 155 0 24.5 darker
## 156 4 24.7 medium
## 157 0 28.3 medium
## 158 2 23.9 medium
## 159 0 23.8 dark
## 160 4 29.8 dark
## 161 4 26.5 medium
## 162 3 26.0 medium
## 163 8 28.2 medium
## 164 0 25.7 darker
## 165 7 26.5 medium
## 166 0 25.8 medium
## 167 0 24.1 dark
## 168 2 26.2 dark
## 169 3 26.1 dark
## 170 4 29.0 dark
## 171 0 28.0 light
## 172 0 27.0 darker
## 173 0 24.5 medium
##
## $call
## glm(formula = satell ~ width + color, family = "poisson")
##
## $formula
## satell ~ width + color
##
## $terms
## satell ~ width + color
## attr(,"variables")
## list(satell, width, color)
## attr(,"factors")
## width color
## satell 0 0
## width 1 0
## color 0 1
## attr(,"term.labels")
## [1] "width" "color"
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(satell, width, color)
## attr(,"dataClasses")
## satell width color
## "numeric" "numeric" "factor"
##
## $data
## <environment: R_GlobalEnv>
##
## $offset
## NULL
##
## $control
## $control$epsilon
## [1] 1e-08
##
## $control$maxit
## [1] 25
##
## $control$trace
## [1] FALSE
##
##
## $method
## [1] "glm.fit"
##
## $contrasts
## $contrasts$color
## [1] "contr.treatment"
##
##
## $xlevels
## $xlevels$color
## [1] "dark" "darker" "light" "medium"
##
##
## $coefficients
## (Intercept) width weight
## -1.2952111086 0.0460764879 0.0004469701
##
## $residuals
## 1 2 3 4 5 6
## 1.02876931 -1.00000000 2.54811282 -1.00000000 0.37904968 -1.00000000
## 7 8 9 10 11 12
## -1.00000000 -1.00000000 -1.00000000 -1.00000000 -1.00000000 -1.00000000
## 13 14 15 16 17 18
## 1.80244076 -1.00000000 4.51928661 1.24211171 -0.53226646 -0.74890332
## 19 20 21 22 23 24
## -1.00000000 0.54547930 1.09790522 0.23847410 -0.36660559 -0.33235995
## 25 26 27 28 29 30
## 0.36493920 -1.00000000 -0.28575376 0.58880154 -1.00000000 -1.00000000
## 31 32 33 34 35 36
## -0.09411220 -1.00000000 -1.00000000 2.65664767 0.59613907 -1.00000000
## 37 38 39 40 41 42
## -1.00000000 0.50970078 -1.00000000 0.73311505 -0.22808992 0.56915384
## 43 44 45 46 47 48
## 1.00015907 0.04849226 1.06804369 0.10338067 0.86916436 0.80571412
## 49 50 51 52 53 54
## -0.22915402 -0.45740191 1.28855964 0.39391611 0.85457516 1.48730999
## 55 56 57 58 59 60
## 1.28540479 2.88991194 -0.06412769 0.17185537 -1.00000000 -1.00000000
## 61 62 63 64 65 66
## -1.00000000 1.36194739 0.05620424 1.10786314 -0.71247536 0.87592023
## 67 68 69 70 71 72
## 1.29624197 -1.00000000 -1.00000000 0.12063027 1.24801364 -0.39471068
## 73 74 75 76 77 78
## -1.00000000 1.48667170 -1.00000000 -1.00000000 0.23676683 0.65778113
## 79 80 81 82 83 84
## -1.00000000 1.24676645 -1.00000000 -1.00000000 -1.00000000 0.41672912
## 85 86 87 88 89 90
## -1.00000000 0.15497141 -1.00000000 -1.00000000 -1.00000000 -1.00000000
## 91 92 93 94 95 96
## -0.16861093 -1.00000000 -1.00000000 -1.00000000 -1.00000000 -0.72542291
## 97 98 99 100 101 102
## -1.00000000 -0.55291472 -0.55824373 -0.74305134 -0.72351785 -0.42236607
## 103 104 105 106 107 108
## -0.67098961 0.49726867 -0.53226646 -0.71603100 -0.62817377 -0.75987946
## 109 110 111 112 113 114
## -0.02626878 0.54102442 -0.28805574 1.06572293 -1.00000000 -0.09714294
## 115 116 117 118 119 120
## -0.62002778 -1.00000000 1.68746459 -1.00000000 0.97389486 1.06572293
## 121 122 123 124 125 126
## 2.27331770 -0.57978071 -1.00000000 0.14546043 -0.15200925 -1.00000000
## 127 128 129 130 131 132
## -0.29181722 1.67916091 -0.11658488 1.48838745 1.96389715 -1.00000000
## 133 134 135 136 137 138
## 0.09982451 3.06876770 0.80819401 -1.00000000 0.23425750 0.78361179
## 139 140 141 142 143 144
## 1.53292749 0.90898321 -0.46563994 -0.39206356 -0.22102956 -1.00000000
## 145 146 147 148 149 150
## -1.00000000 2.61142823 -0.35858761 -0.02939468 4.16919710 1.15810575
## 151 152 153 154 155 156
## 0.14560139 -1.00000000 -1.00000000 -1.00000000 -1.00000000 0.95780628
## 157 158 159 160 161 162
## -1.00000000 0.06208676 -1.00000000 -0.22583410 0.50696681 0.19599422
## 163 164 165 166 167 168
## 1.03813874 -1.00000000 1.20543918 -1.00000000 -1.00000000 -0.17387202
## 169 170 171 172 173
## -0.03722988 -0.11178338 -1.00000000 -1.00000000 -1.00000000
##
## $fitted.values
## 1 2 3 4 5 6 7
## 3.943277 1.543903 2.536560 2.194870 2.900548 2.096033 2.654338
## 8 9 10 11 12 13 14
## 1.997949 1.951100 2.328752 2.193357 2.938893 3.925150 1.647551
## 15 16 17 18 19 20 21
## 2.536560 3.568065 2.137969 3.982530 2.284649 3.235242 1.906664
## 22 23 24 25 26 27 28
## 2.422336 1.578795 2.995626 2.197900 1.637309 4.200232 3.147026
## 29 30 31 32 33 34 35
## 3.108109 2.205007 4.415558 2.873942 2.187796 2.187796 3.132559
## 36 37 38 39 40 41 42
## 1.865812 3.077475 3.974297 1.881787 3.461974 3.886463 3.186431
## 43 44 45 46 47 48 49
## 2.999761 4.768752 4.351939 3.625222 3.209991 2.215190 3.891828
## 50 51 52 53 54 55 56
## 5.528954 2.184780 3.587016 3.235242 1.608163 2.187796 3.856128
## 57 58 59 60 61 62 63
## 3.205566 2.560043 3.369899 2.424007 2.983913 2.116897 2.840360
## 64 65 66 67 68 69 70
## 2.372071 3.477963 4.264574 4.354942 2.677065 1.660098 2.677065
## 71 72 73 74 75 76 77
## 3.113860 1.652102 1.766666 2.412864 1.952446 1.608163 2.425680
## 78 79 80 81 82 83 84
## 2.412864 1.530078 2.225420 2.314851 3.554113 1.659732 2.823405
## 85 86 87 88 89 90 91
## 2.513292 2.597467 3.979784 3.031044 1.890478 1.769105 6.014031
## 92 93 94 95 96 97 98
## 2.942950 2.801368 4.295158 1.587181 3.641964 2.424007 2.236710
## 99 100 101 102 103 104 105
## 2.263692 3.891828 3.616870 1.731200 3.039418 2.671531 2.137969
## 106 107 108 109 110 111 112
## 3.521511 2.689428 4.164575 2.053955 2.595676 4.213813 2.904552
## 113 114 115 116 117 118 119
## 2.287803 2.215190 5.263543 1.824576 4.465175 1.410903 2.533063
## 120 121 122 123 124 125 126
## 2.904552 1.833003 4.759420 2.475646 1.746023 3.537774 2.467395
## 127 128 129 130 131 132 133
## 4.236194 1.493005 2.263941 2.411200 2.024362 1.946153 3.636944
## 134 135 136 137 138 139 140
## 2.457747 3.871266 2.305800 4.051019 2.803301 2.368801 3.143034
## 141 142 143 144 145 146 147
## 13.099781 4.934726 3.851237 2.051123 2.628177 2.215190 6.236237
## 148 149 150 151 152 153 154
## 4.121140 1.934536 4.170324 3.491616 2.560043 1.940792 1.608163
## 155 156 157 158 159 160 161
## 1.979622 2.043103 4.216720 1.883085 1.833003 5.166851 2.654338
## 162 163 164 165 166 167 168
## 2.508373 3.925150 2.339507 3.173971 2.403429 1.858516 2.420932
## 169 170 171 172 173
## 3.116009 4.503406 3.216285 3.071452 2.070113
##
## $effects
## (Intercept) width weight
## -25.69553277 8.57166143 2.81785468 -1.49304754 0.77413220
##
## -1.18786383 -1.90974352 -1.57219198 -1.24898421 -1.71957058
##
## -1.30820822 -1.46786860 3.45343157 -0.57765851 7.02354049
##
## 2.50699446 -0.98929391 -1.97346537 -1.40184086 0.75607580
##
## 1.79146973 0.49251054 -0.31618031 -0.67151562 0.16045725
##
## -2.25576520 -0.77292173 1.02191944 -2.12522259 -1.52430520
##
## -0.56121138 -1.50378072 -1.83111576 3.57750524 1.06570640
##
## -1.30553745 -1.83066908 1.28203771 -1.19177243 1.07162083
##
## -0.24742709 1.33725312 1.20479635 -0.42359579 2.24411873
##
## -0.45905583 0.95419136 1.13183812 -0.74075779 -1.34317970
##
## 1.92079485 0.63601576 1.31203970 1.93689226 1.54927237
##
## 5.46112961 -0.27177408 0.04008963 -2.14641971 -1.62930144
##
## -2.00611905 2.00717994 -0.24947487 1.39554738 -1.65676376
##
## 0.99005390 2.46137982 -1.77555811 -2.34381115 0.05798785
##
## 2.68512633 -0.44034484 -1.13704779 2.26618875 -1.42332237
##
## -1.21734929 0.10250982 0.97864026 -2.14100978 1.76446119
##
## -1.30721078 -1.92414391 -1.24528945 0.80679263 -1.69928424
##
## -0.27798075 -2.22541822 -1.28561008 -1.22027519 -1.46891405
##
## -0.03931081 -1.89590299 -1.92150468 -2.19802230 -1.29538665
##
## -2.07754358 -1.62930144 -1.43734047 -0.67438570 -1.75455986
##
## -1.70326587 -0.89735992 -1.58171831 1.28381746 -0.98929391
##
## -1.31211154 -1.20059551 -1.91473506 -0.35962646 0.54460715
##
## -0.30637493 1.52071947 -1.77931043 -0.21193050 -2.75992784
##
## -1.33264096 3.23283442 -1.08637058 1.77216419 1.52071947
##
## 3.07061923 -1.90915674 -1.87709983 -0.03592708 -0.28950181
##
## -1.56600733 -0.60964427 2.09812193 -0.65122783 2.46133292
##
## 2.73161519 -1.74215018 -0.02759232 4.64999361 1.58397409
##
## -1.46520011 0.33866316 0.85576101 2.43190703 1.27453793
##
## -0.69886863 -1.58339613 -0.99541436 -1.39743569 -1.63780757
##
## 3.81937535 -1.64228162 -0.70572511 5.82503818 1.49240542
##
## 0.14094152 -1.83489465 -1.04619846 -1.21734929 -1.51225620
##
## 1.25260611 -2.02400706 0.09448056 -1.36107411 -0.69924280
##
## 0.54542800 0.11285070 1.93919709 -1.75218645 2.27985673
##
## -1.75724359 -1.44711969 -0.61877120 0.19761362 -0.38696066
##
## -2.30222670 -1.92152461 -1.45778899
##
## $R
## (Intercept) width weight
## (Intercept) -22.47221 -608.26697 -59903.289
## width 0.00000 51.64307 13853.568
## weight 0.00000 0.00000 6304.348
##
## $rank
## [1] 3
##
## $qr
## $qr
## (Intercept) width weight
## 1 -22.47220513 -6.082670e+02 -5.990329e+04
## 2 0.05529225 5.164307e+01 1.385357e+04
## 3 0.07087235 3.600817e-02 6.304348e+03
## 4 0.06592630 6.792020e-02 -7.723097e-03
## 5 0.07578694 3.850513e-02 -5.772742e-02
## 6 0.06442484 9.440745e-02 -6.862835e-02
## 7 0.07249907 2.106087e-02 4.375190e-02
## 8 0.06289940 6.753879e-02 3.150971e-02
## 9 0.06215758 9.378978e-02 -3.887168e-02
## 10 0.06790722 4.632146e-02 3.144812e-02
## 11 0.06590357 8.223558e-02 -5.070785e-02
## 12 0.07628624 4.539792e-02 -8.616945e-02
## 13 0.08816220 -3.960664e-02 -2.468002e-02
## 14 0.05711809 1.532932e-01 -1.615748e-01
## 15 0.07087235 3.600817e-02 2.180461e-02
## 16 0.08405638 2.472244e-03 -8.123144e-02
## 17 0.06506614 5.570875e-02 4.024653e-02
## 18 0.08880426 -7.080924e-02 5.832384e-02
## 19 0.06726111 7.222221e-02 -3.823209e-02
## 20 0.08004012 -8.094590e-03 1.674252e-02
## 21 0.06144568 1.060845e-01 -6.755478e-02
## 22 0.06925824 6.532545e-02 -4.435567e-02
## 23 0.05591357 1.135648e-01 -2.882330e-02
## 24 0.07701904 1.567103e-02 -7.550731e-03
## 25 0.06597179 3.925980e-02 7.833540e-02
## 26 0.05694028 2.397424e-02 2.312821e-01
## 27 0.09119918 -6.081341e-02 -1.480572e-02
## 28 0.07894134 1.262707e-02 -2.839385e-02
## 29 0.07845171 -1.134774e-02 5.181282e-02
## 30 0.06607836 6.520150e-02 -1.476025e-03
## 31 0.09350764 -8.676634e-02 2.134657e-02
## 32 0.07543854 4.489346e-02 -7.176668e-02
## 33 0.06581998 4.203357e-02 7.191475e-02
## 34 0.06581998 4.203357e-02 7.191475e-02
## 35 0.07875968 1.602520e-02 -3.579571e-02
## 36 0.06078385 9.171697e-02 -1.634584e-02
## 37 0.07806414 1.248676e-02 -1.416514e-02
## 38 0.08871243 -1.283191e-02 -1.153318e-01
## 39 0.06104352 1.000776e-01 -4.465791e-02
## 40 0.08279731 -2.638783e-02 2.705573e-02
## 41 0.08772666 -1.268932e-02 -9.841494e-02
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## 43 0.07707218 -1.785570e-02 9.298896e-02
## 44 0.09717548 -1.197695e-01 5.203753e-02
## 45 0.09283157 -5.382289e-02 -6.576378e-02
## 46 0.08472696 -6.387130e-02 1.080158e-01
## 47 0.07972714 -3.581721e-02 1.055676e-01
## 48 0.06623077 6.246989e-02 4.799905e-03
## 49 0.08778719 -5.089820e-02 1.604037e-02
## 50 0.10463472 -1.426225e-01 -2.609973e-02
## 51 0.06577460 7.062605e-02 -1.394141e-02
## 52 0.08427931 -1.952541e-02 -1.848287e-02
## 53 0.08004012 -8.094590e-03 1.674252e-02
## 54 0.05643121 1.047938e-01 -7.689184e-03
## 55 0.06581998 4.203357e-02 7.191475e-02
## 56 0.08738362 -4.305931e-02 -6.030400e-04
## 57 0.07967217 -1.123595e-03 1.558130e-03
## 58 0.07119966 2.997804e-02 3.540618e-02
## 59 0.08168885 -2.247991e-02 3.350783e-02
## 60 0.06928213 5.027410e-02 8.201999e-04
## 61 0.07686831 2.260830e-03 3.531564e-02
## 62 0.06474470 7.515485e-02 -1.446069e-02
## 63 0.07499650 5.469209e-03 5.407818e-02
## 64 0.06853590 3.482109e-02 5.773079e-02
## 65 0.08298828 -3.005989e-02 3.498624e-02
## 66 0.09189505 -1.132614e-01 1.311011e-01
## 67 0.09286359 -7.404600e-02 -5.213634e-03
## 68 0.07280878 3.065555e-02 1.025326e-02
## 69 0.05733519 1.665579e-02 2.491940e-01
## 70 0.07280878 3.065555e-02 1.025326e-02
## 71 0.07852426 5.356365e-02 -1.455728e-01
## 72 0.05719694 1.037269e-01 -1.256477e-02
## 73 0.05914684 1.072631e-01 -4.461796e-02
## 74 0.06912270 5.316625e-02 -5.735206e-03
## 75 0.06217902 8.029370e-02 1.672887e-03
## 76 0.05643121 1.047938e-01 -7.689184e-03
## 77 0.06930603 3.521237e-02 4.602724e-02
## 78 0.06912270 5.316625e-02 -5.735206e-03
## 79 0.05504414 3.515197e-02 2.171075e-01
## 80 0.06638353 5.972533e-02 1.110479e-02
## 81 0.06770424 7.859025e-02 -6.338882e-02
## 82 0.08389188 -1.213463e-02 -3.430547e-02
## 83 0.05732887 1.014715e-01 -7.158406e-03
## 84 0.07477233 4.124332e-02 -5.071723e-02
## 85 0.07054655 4.198223e-02 8.327349e-03
## 86 0.07171819 5.230135e-03 1.028430e-01
## 87 0.08877364 -5.147014e-02 3.986782e-04
## 88 0.07747301 5.621776e-02 -1.371613e-01
## 89 0.06118431 9.764602e-02 -3.896004e-02
## 90 0.05918765 8.158189e-02 3.256480e-02
## 91 0.10912824 -1.202554e-01 -1.865477e-01
## 92 0.07633887 1.221079e-02 1.335935e-02
## 93 0.07447995 1.515440e-02 3.252140e-02
## 94 0.09222398 -6.149677e-02 -3.140897e-02
## 95 0.05606186 9.922895e-02 1.298337e-02
## 96 0.08492238 -6.771397e-02 1.163165e-01
## 97 0.06928213 5.027410e-02 8.201999e-04
## 98 0.06655170 1.933317e-02 1.310208e-01
## 99 0.06695191 7.771695e-02 -5.075175e-02
## 100 0.08778719 -5.089820e-02 1.604037e-02
## 101 0.08462930 -3.801949e-02 3.151384e-02
## 102 0.05855014 6.796429e-02 8.084025e-02
## 103 0.07757995 -1.122164e-02 6.506398e-02
## 104 0.07273349 7.809823e-02 -1.320846e-01
## 105 0.06506614 5.570875e-02 4.024653e-02
## 106 0.08350622 -4.811394e-03 -4.998220e-02
## 107 0.07297671 2.755071e-02 1.719583e-02
## 108 0.09081125 -7.240954e-02 2.727178e-02
## 109 0.06377490 5.182808e-02 6.822725e-02
## 110 0.07169346 2.082684e-02 5.604349e-02
## 111 0.09134651 -2.513760e-02 -1.253356e-01
## 112 0.07583923 5.530666e-03 4.116919e-02
## 113 0.06730752 4.298354e-02 4.954789e-02
## 114 0.06623077 6.246989e-02 4.799905e-03
## 115 0.10209239 -2.102371e-01 2.294808e-01
## 116 0.06010842 9.069780e-02 -5.451208e-03
## 117 0.09403154 -8.725247e-02 1.308665e-02
## 118 0.05285704 1.188569e-01 -1.462214e-02
## 119 0.07082349 6.680181e-02 -7.060355e-02
## 120 0.07583923 5.530666e-03 4.116919e-02
## 121 0.06024706 8.828538e-02 2.482378e-04
## 122 0.09708035 -1.281011e-01 7.904616e-02
## 123 0.07001620 2.947976e-02 5.353593e-02
## 124 0.05880026 7.593061e-02 5.398120e-02
## 125 0.08369883 -8.464601e-03 -4.216201e-02
## 126 0.06989943 5.376368e-02 -1.825768e-02
## 127 0.09158878 -4.911689e-02 -5.724321e-02
## 128 0.05437320 1.104362e-01 -3.802197e-03
## 129 0.06695560 2.819111e-02 9.892913e-02
## 130 0.06909887 6.818192e-02 -5.080486e-02
## 131 0.06331380 7.349388e-02 8.427466e-03
## 132 0.06207873 5.585230e-02 7.676963e-02
## 133 0.08486383 -3.073924e-02 5.526742e-03
## 134 0.06976263 4.151573e-02 2.066846e-02
## 135 0.08755497 -2.790412e-02 -4.941328e-02
## 136 0.06757174 6.667506e-02 -2.559571e-02
## 137 0.08956461 -4.803137e-02 -2.405229e-02
## 138 0.07450564 -1.050733e-03 8.113096e-02
## 139 0.06848864 6.459955e-02 -3.165621e-02
## 140 0.07889126 -1.141131e-02 4.507281e-02
## 141 0.16105950 -4.438017e-01 -4.701020e-01
## 142 0.09885209 -1.433434e-01 9.098706e-02
## 143 0.08732819 -6.963227e-02 8.070087e-02
## 144 0.06373092 7.952451e-02 -1.496029e-02
## 145 0.07214091 4.293104e-02 -1.719949e-02
## 146 0.06623077 6.246989e-02 4.799905e-03
## 147 0.11112599 -2.191686e-01 7.026861e-02
## 148 0.09033644 -9.168565e-02 9.410384e-02
## 149 0.06189318 8.531108e-02 -1.007098e-02
## 150 0.09087391 -1.120028e-01 1.458406e-01
## 151 0.08315101 -1.564574e-02 -1.129914e-02
## 152 0.07119966 2.997804e-02 3.540618e-02
## 153 0.06199316 1.097273e-01 -8.508316e-02
## 154 0.05643121 1.047938e-01 -7.689184e-03
## 155 0.06261025 7.267722e-02 1.949271e-02
## 156 0.06360620 6.829772e-02 2.052738e-02
## 157 0.09137801 -4.502760e-02 -6.577503e-02
## 158 0.06106457 8.682616e-02 -4.842273e-03
## 159 0.06024706 8.828538e-02 2.482378e-04
## 160 0.10115033 -1.158655e-01 -3.712526e-02
## 161 0.07249907 2.106087e-02 4.375190e-02
## 162 0.07047749 3.580755e-02 2.796365e-02
## 163 0.08816220 -3.960664e-02 -2.468002e-02
## 164 0.06806385 4.346654e-02 3.797378e-02
## 165 0.07927857 2.303031e-02 -6.519398e-02
## 166 0.06898743 4.105440e-02 3.273427e-02
## 167 0.06066490 8.097827e-02 1.750486e-02
## 168 0.06923818 2.915217e-02 6.528120e-02
## 169 0.07855135 3.649153e-02 -9.438575e-02
## 170 0.09443323 -7.529757e-02 -3.054770e-02
## 171 0.07980527 -2.890694e-02 8.342667e-02
## 172 0.07798771 5.687347e-03 7.586541e-03
## 173 0.06402525 7.431973e-02 -2.888917e-03
##
## $rank
## [1] 3
##
## $qraux
## [1] 1.088366 1.112303 1.021805
##
## $pivot
## [1] 1 2 3
##
## $tol
## [1] 1e-11
##
## attr(,"class")
## [1] "qr"
##
## $family
##
## Family: poisson
## Link function: log
##
##
## $linear.predictors
## 1 2 3 4 5 6 7
## 1.3720122 0.4343135 0.9308087 0.7861229 1.0648997 0.7400464 0.9761955
## 8 9 10 11 12 13 14
## 0.6921213 0.6683933 0.8453326 0.7854332 1.0780329 1.3674045 0.4992897
## 15 16 17 18 19 20 21
## 0.9308087 1.2720234 0.7598565 1.3819172 0.8262123 1.1741038 0.6453550
## 22 23 24 25 26 27 28
## 0.8847322 0.4566620 1.0971533 0.7875024 0.4930540 1.4351398 1.1464579
## 29 30 31 32 33 34 35
## 1.1340145 0.7907306 1.4851342 1.0556844 0.7828948 0.7828948 1.1418503
## 36 37 38 39 40 41 42
## 0.6236963 1.1241094 1.3798480 0.6322218 1.2418391 1.3574995 1.1589014
## 43 44 45 46 47 48 49
## 1.0985328 1.5620847 1.4706215 1.2879156 1.1662680 0.7953382 1.3588790
## 50 51 52 53 54 55 56
## 1.7099987 0.7815153 1.2773208 1.1741038 0.4750926 0.7828948 1.3496637
## 57 58 59 60 61 62 63
## 1.1648885 0.9400240 1.2148829 0.8854220 1.0932354 0.7499515 1.0439307
## 64 65 66 67 68 69 70
## 0.8637632 1.2464467 1.4503422 1.4713112 0.9847210 0.5068769 0.9847210
## 71 72 73 74 75 76 77
## 1.1358632 0.5020487 0.5690942 0.8808143 0.6690830 0.4750926 0.8861117
## 78 79 80 81 82 83 84
## 0.8808143 0.4253187 0.7999459 0.8393455 1.2681055 0.5066564 1.0379436
## 85 86 87 88 89 90 91
## 0.9215934 0.9545367 1.3812275 1.1089070 0.6368295 0.5704737 1.7940953
## 92 93 94 95 96 97 98
## 1.0794124 1.0301078 1.4574883 0.4619594 1.2925232 0.8854220 0.8050059
## 99 100 101 102 103 104 105
## 0.8169970 1.3588790 1.2856090 0.5488149 1.1116660 0.9826518 0.7598565
## 106 107 108 109 110 111 112
## 1.2588902 0.9893287 1.4266142 0.7197672 0.9538470 1.4383680 1.0662792
## 113 114 115 116 117 118 119
## 0.8275918 0.7953382 1.6608043 0.6013478 1.4963084 0.3442297 0.9294292
## 120 121 122 123 124 125 126
## 1.0662792 0.6059554 1.5601258 0.9065013 0.5573405 1.2634978 0.9031628
## 127 128 129 130 131 132 133
## 1.4436653 0.4007907 0.8171072 0.8801246 0.7052545 0.6658548 1.2911437
## 134 135 136 137 138 139 140
## 0.8992449 1.3535816 0.8354276 1.3989683 1.0307975 0.8623837 1.1451887
## 141 142 143 144 145 146 147
## 2.5725955 1.5962972 1.3483945 0.7183877 0.9662904 0.7953382 1.8303770
## 148 149 150 151 152 153 154
## 1.4161297 0.6598677 1.4279937 1.2503646 0.9400240 0.6630959 0.4750926
## 155 156 157 158 159 160 161
## 0.6829060 0.7144698 1.4390577 0.6329116 0.6059554 1.6422634 0.9761955
## 162 163 164 165 166 167 168
## 0.9196345 1.3674045 0.8499403 1.1549835 0.8768964 0.6197784 0.8841528
## 169 170 171 172 173
## 1.1365529 1.5048340 1.1682270 1.1221505 0.7276030
##
## $deviance
## [1] 559.9006
##
## $aic
## [1] 921.1983
##
## $null.deviance
## [1] 632.7917
##
## $iter
## [1] 6
##
## $weights
## 1 2 3 4 5 6 7
## 3.943277 1.543903 2.536560 2.194870 2.900548 2.096033 2.654338
## 8 9 10 11 12 13 14
## 1.997949 1.951100 2.328752 2.193357 2.938893 3.925150 1.647551
## 15 16 17 18 19 20 21
## 2.536560 3.568065 2.137969 3.982530 2.284649 3.235242 1.906664
## 22 23 24 25 26 27 28
## 2.422336 1.578795 2.995626 2.197900 1.637309 4.200232 3.147026
## 29 30 31 32 33 34 35
## 3.108109 2.205007 4.415558 2.873942 2.187796 2.187796 3.132559
## 36 37 38 39 40 41 42
## 1.865812 3.077475 3.974298 1.881787 3.461974 3.886463 3.186431
## 43 44 45 46 47 48 49
## 2.999761 4.768752 4.351939 3.625222 3.209991 2.215190 3.891828
## 50 51 52 53 54 55 56
## 5.528954 2.184780 3.587016 3.235242 1.608163 2.187796 3.856128
## 57 58 59 60 61 62 63
## 3.205566 2.560043 3.369900 2.424007 2.983913 2.116897 2.840360
## 64 65 66 67 68 69 70
## 2.372071 3.477963 4.264574 4.354942 2.677065 1.660098 2.677065
## 71 72 73 74 75 76 77
## 3.113860 1.652103 1.766666 2.412864 1.952446 1.608163 2.425680
## 78 79 80 81 82 83 84
## 2.412864 1.530078 2.225420 2.314851 3.554113 1.659732 2.823405
## 85 86 87 88 89 90 91
## 2.513292 2.597467 3.979784 3.031044 1.890478 1.769105 6.014031
## 92 93 94 95 96 97 98
## 2.942950 2.801368 4.295158 1.587181 3.641964 2.424007 2.236710
## 99 100 101 102 103 104 105
## 2.263692 3.891828 3.616870 1.731200 3.039418 2.671531 2.137969
## 106 107 108 109 110 111 112
## 3.521511 2.689428 4.164575 2.053955 2.595676 4.213813 2.904552
## 113 114 115 116 117 118 119
## 2.287803 2.215190 5.263543 1.824576 4.465175 1.410903 2.533063
## 120 121 122 123 124 125 126
## 2.904552 1.833003 4.759420 2.475646 1.746023 3.537774 2.467395
## 127 128 129 130 131 132 133
## 4.236195 1.493005 2.263941 2.411200 2.024362 1.946153 3.636944
## 134 135 136 137 138 139 140
## 2.457747 3.871266 2.305800 4.051019 2.803301 2.368801 3.143034
## 141 142 143 144 145 146 147
## 13.099781 4.934726 3.851237 2.051123 2.628177 2.215190 6.236237
## 148 149 150 151 152 153 154
## 4.121140 1.934536 4.170324 3.491616 2.560043 1.940792 1.608163
## 155 156 157 158 159 160 161
## 1.979622 2.043103 4.216721 1.883085 1.833003 5.166851 2.654338
## 162 163 164 165 166 167 168
## 2.508373 3.925150 2.339507 3.173971 2.403429 1.858516 2.420932
## 169 170 171 172 173
## 3.116009 4.503406 3.216285 3.071452 2.070113
##
## $prior.weights
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 163 164 165 166 167 168 169 170 171 172 173
## 1 1 1 1 1 1 1 1 1 1 1
##
## $df.residual
## [1] 170
##
## $df.null
## [1] 172
##
## $y
## 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
## 8 0 9 0 4 0 0 0 0 0 0 0 11 0 14 8 1 1
## 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
## 0 5 4 3 1 2 3 0 3 5 0 0 4 0 0 8 5 0
## 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54
## 0 6 0 6 3 5 6 5 9 4 6 4 3 3 5 5 6 4
## 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72
## 5 15 3 3 0 0 0 5 3 5 1 8 10 0 0 3 7 1
## 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90
## 0 6 0 0 3 4 0 5 0 0 0 4 0 3 0 0 0 0
## 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108
## 5 0 0 0 0 1 0 1 1 1 1 1 1 4 1 1 1 1
## 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126
## 2 4 3 6 0 2 2 0 12 0 5 6 6 2 0 2 3 0
## 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144
## 3 4 2 6 6 0 4 10 7 0 5 5 6 6 7 3 3 0
## 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162
## 0 8 4 4 10 9 4 0 0 0 0 4 0 2 0 4 4 3
## 163 164 165 166 167 168 169 170 171 172 173
## 8 0 7 0 0 2 3 4 0 0 0
##
## $converged
## [1] TRUE
##
## $boundary
## [1] FALSE
##
## $model
## satell width weight
## 1 8 28.3 3050
## 2 0 22.5 1550
## 3 9 26.0 2300
## 4 0 24.8 2100
## 5 4 26.0 2600
## 6 0 23.8 2100
## 7 0 26.5 2350
## 8 0 24.7 1900
## 9 0 23.7 1950
## 10 0 25.6 2150
## 11 0 24.3 2150
## 12 0 25.8 2650
## 13 11 28.2 3050
## 14 0 21.0 1850
## 15 14 26.0 2300
## 16 8 27.1 2950
## 17 1 25.2 2000
## 18 1 29.0 3000
## 19 0 24.7 2200
## 20 5 27.4 2700
## 21 4 23.2 1950
## 22 3 25.0 2300
## 23 1 22.5 1600
## 24 2 26.7 2600
## 25 3 25.8 2000
## 26 0 26.2 1300
## 27 3 28.7 3150
## 28 5 26.8 2700
## 29 0 27.5 2600
## 30 0 24.9 2100
## 31 4 29.3 3200
## 32 0 25.8 2600
## 33 0 25.7 2000
## 34 8 25.7 2000
## 35 5 26.7 2700
## 36 0 23.7 1850
## 37 0 26.8 2650
## 38 6 27.5 3150
## 39 0 23.4 1900
## 40 6 27.9 2800
## 41 3 27.5 3100
## 42 5 26.1 2800
## 43 6 27.7 2500
## 44 5 30.0 3300
## 45 9 28.5 3250
## 46 4 28.9 2800
## 47 6 28.2 2600
## 48 4 25.0 2100
## 49 3 28.5 3000
## 50 3 30.3 3600
## 51 5 24.7 2100
## 52 5 27.7 2900
## 53 6 27.4 2700
## 54 4 22.9 1600
## 55 5 25.7 2000
## 56 15 28.3 3000
## 57 3 27.2 2700
## 58 3 26.2 2300
## 59 0 27.8 2750
## 60 0 25.5 2250
## 61 0 27.1 2550
## 62 5 24.5 2050
## 63 3 27.0 2450
## 64 5 26.0 2150
## 65 1 28.0 2800
## 66 8 30.0 3050
## 67 10 29.0 3200
## 68 0 26.2 2400
## 69 0 26.5 1300
## 70 3 26.2 2400
## 71 7 25.6 2800
## 72 1 23.0 1650
## 73 0 23.0 1800
## 74 6 25.4 2250
## 75 0 24.2 1900
## 76 0 22.9 1600
## 77 3 26.0 2200
## 78 4 25.4 2250
## 79 0 25.7 1200
## 80 5 25.1 2100
## 81 0 24.5 2250
## 82 0 27.5 2900
## 83 0 23.1 1650
## 84 4 25.9 2550
## 85 0 25.8 2300
## 86 3 27.0 2250
## 87 0 28.5 3050
## 88 0 25.5 2750
## 89 0 23.5 1900
## 90 0 24.0 1700
## 91 5 29.7 3850
## 92 0 26.8 2550
## 93 0 26.7 2450
## 94 0 28.7 3200
## 95 0 23.1 1550
## 96 1 29.0 2800
## 97 0 25.5 2250
## 98 1 26.5 1967
## 99 1 24.5 2200
## 100 1 28.5 3000
## 101 1 28.2 2867
## 102 1 24.5 1600
## 103 1 27.5 2550
## 104 4 24.7 2550
## 105 1 25.2 2000
## 106 1 27.3 2900
## 107 1 26.3 2400
## 108 1 29.0 3100
## 109 2 25.3 1900
## 110 4 26.5 2300
## 111 3 27.8 3250
## 112 6 27.0 2500
## 113 0 25.7 2100
## 114 2 25.0 2100
## 115 2 31.9 3325
## 116 0 23.7 1800
## 117 12 29.3 3225
## 118 0 22.0 1400
## 119 5 25.0 2400
## 120 6 27.0 2500
## 121 6 23.8 1800
## 122 2 30.2 3275
## 123 0 26.2 2225
## 124 2 24.2 1650
## 125 3 27.4 2900
## 126 0 25.4 2300
## 127 3 28.4 3200
## 128 4 22.5 1475
## 129 2 26.2 2025
## 130 6 24.9 2300
## 131 6 24.5 1950
## 132 0 25.1 1800
## 133 4 28.0 2900
## 134 10 25.8 2250
## 135 7 27.9 3050
## 136 0 24.9 2200
## 137 5 28.4 3100
## 138 5 27.2 2400
## 139 6 25.0 2250
## 140 6 27.5 2625
## 141 7 33.5 5200
## 142 3 30.5 3325
## 143 3 29.0 2925
## 144 0 24.3 2000
## 145 0 25.8 2400
## 146 8 25.0 2100
## 147 4 31.7 3725
## 148 4 29.5 3025
## 149 10 24.0 1900
## 150 9 30.0 3000
## 151 4 27.6 2850
## 152 0 26.2 2300
## 153 0 23.1 2000
## 154 0 22.9 1600
## 155 0 24.5 1900
## 156 4 24.7 1950
## 157 0 28.3 3200
## 158 2 23.9 1850
## 159 0 23.8 1800
## 160 4 29.8 3500
## 161 4 26.5 2350
## 162 3 26.0 2275
## 163 8 28.2 3050
## 164 0 25.7 2150
## 165 7 26.5 2750
## 166 0 25.8 2200
## 167 0 24.1 1800
## 168 2 26.2 2175
## 169 3 26.1 2750
## 170 4 29.0 3275
## 171 0 28.0 2625
## 172 0 27.0 2625
## 173 0 24.5 2000
##
## $call
## glm(formula = satell ~ width + weight, family = "poisson")
##
## $formula
## satell ~ width + weight
##
## $terms
## satell ~ width + weight
## attr(,"variables")
## list(satell, width, weight)
## attr(,"factors")
## width weight
## satell 0 0
## width 1 0
## weight 0 1
## attr(,"term.labels")
## [1] "width" "weight"
## attr(,"order")
## [1] 1 1
## attr(,"intercept")
## [1] 1
## attr(,"response")
## [1] 1
## attr(,".Environment")
## <environment: R_GlobalEnv>
## attr(,"predvars")
## list(satell, width, weight)
## attr(,"dataClasses")
## satell width weight
## "numeric" "numeric" "numeric"
##
## $data
## <environment: R_GlobalEnv>
##
## $offset
## NULL
##
## $control
## $control$epsilon
## [1] 1e-08
##
## $control$maxit
## [1] 25
##
## $control$trace
## [1] FALSE
##
##
## $method
## [1] "glm.fit"
##
## $contrasts
## NULL
##
## $xlevels
## named list()
pvalue <- c(anova(modcombo, modcombo2, test = "Chisq")[5] ,anova(modcombo, modcombo3, test = "Chisq")[5] , anova(modcombo, modcombo4, test = "Chisq")[5])
modcombo5 <- glm(satell ~ width + weight + spine, family = "poisson")
modcombo6 <- glm(satell ~ width + weight + color, family = "poisson")
#it appears we should keep modcombo6 and add the predictor of color since it makes the model better
pvalue2 <- c(anova(modcombo4, modcombo5, test = "Chisq")[5] ,anova(modcombo4, modcombo6, test = "Chisq")[5])