require(mosaic)
## Loading required package: mosaic
## Loading required package: grid
## Loading required package: lattice
## Loading required package: car
##
## Attaching package: 'mosaic'
##
## The following object is masked from 'package:car':
##
## logit
##
## The following objects are masked from 'package:stats':
##
## binom.test, cor, cov, D, fivenum, IQR, median, prop.test, sd,
## t.test, var
##
## The following objects are masked from 'package:base':
##
## max, mean, min, print, prod, range, sample, sum
require(repmis)
## Loading required package: repmis
require(plm)
## Loading required package: plm
## Loading required package: Formula
##
## Attaching package: 'plm'
##
## The following object is masked from 'package:mosaic':
##
## r.squared
options(digits = 3)
trellis.par.set(theme = col.mosaic())
All <- repmis::source_DropboxData("CombinedStatesTEMP2.csv", "t7zov3jawubw9nq",
sep = ",", header = TRUE)
## Downloading data from: https://dl.dropboxusercontent.com/s/t7zov3jawubw9nq/CombinedStatesTEMP2.csv
##
## SHA-1 hash of the downloaded data file is:
## 3fc38a056c176aac4620cb396ca89a892c06535c
All$t = as.numeric(All$t)
All$t2 = (All$t)^2
SCtest = function(model, periods) {
res = model$res
n = length(res)
summary(lm(res[-n] ~ res[-1]))
}
fitted <- function(object, ...) object$model[[1]] - object$residuals
# TotalSTD SDs not 1 (means not 0) for some because standardized by county
# with complete data set TMEAN and PRECIP centered by county*month, not
# standardized with SD (used full data set)
favstats(All$TotalSTD ~ All$County.Area)
## .group min Q1 median Q3 max mean sd
## 1 Bar Harbor -0.981 -0.8296 -0.4821 0.812 2.07 -1.17e-02 0.979
## 2 Camden -2.744 -0.6976 -0.0207 0.485 3.80 4.17e-11 1.000
## 3 Carteret -1.677 -0.7362 -0.2120 0.654 2.62 -2.08e-11 1.000
## 4 Durham -1.607 -0.7178 -0.0614 0.526 2.77 2.34e-18 1.000
## 5 Duval (Jacksonville) -1.433 -0.3330 0.2604 0.933 3.20 3.74e-01 0.927
## 6 Leon (Tallahassee) -1.060 -0.2942 0.2886 0.860 3.29 3.89e-01 0.912
## 7 Lewiston -2.134 -0.5514 -0.0373 0.642 2.52 1.99e-02 1.019
## 8 LewistonSub -1.784 -0.8161 -0.1301 0.727 2.37 -1.16e-02 0.979
## 9 Miami-Dade -1.143 -0.1295 0.4393 0.930 2.77 4.86e-01 0.783
## 10 Orange (Orlando) -1.036 -0.0171 0.4191 0.945 2.50 4.98e-01 0.718
## 11 Pinellas (Tampa) -1.547 -0.6282 0.3220 0.741 3.31 2.38e-01 1.099
## 12 Portland -2.047 -0.7480 -0.0718 0.668 2.93 -7.60e-03 1.022
## 13 PortlandSub -1.823 -0.8575 0.0491 0.688 1.87 -2.62e-02 0.966
## 14 St. Lucie -1.046 -0.2563 0.3166 1.044 2.69 4.67e-01 0.855
## 15 Stanly -1.382 -0.6362 -0.2262 0.218 3.33 -3.79e-18 1.000
## n missing
## 1 96 0
## 2 96 0
## 3 96 0
## 4 96 0
## 5 96 0
## 6 96 0
## 7 96 0
## 8 96 0
## 9 96 0
## 10 96 0
## 11 96 0
## 12 96 0
## 13 96 0
## 14 96 0
## 15 96 0
favstats(All$PRECIPcm ~ All$County.Area)
## .group min Q1 median Q3 max mean sd n
## 1 Bar Harbor -4.59 -1.69 -0.212 1.206 8.07 1.42e-01 2.51 96
## 2 Camden -4.37 -1.55 -0.439 1.280 10.26 1.13e-17 2.58 96
## 3 Carteret -4.18 -1.62 -0.338 1.268 7.86 1.66e-17 2.47 96
## 4 Durham -3.23 -1.23 -0.182 1.176 5.58 -1.14e-17 1.84 96
## 5 Duval (Jacksonville) -4.07 -1.23 -0.587 1.472 5.40 2.76e-02 2.15 96
## 6 Leon (Tallahassee) -6.89 -1.93 -0.310 1.150 7.61 -8.42e-02 2.65 96
## 7 Lewiston -4.04 -1.37 -0.283 0.922 7.32 9.14e-02 2.11 96
## 8 LewistonSub -4.04 -1.37 -0.283 0.922 7.32 9.14e-02 2.11 96
## 9 Miami-Dade -8.17 -1.66 -0.295 1.273 9.90 -7.41e-02 2.79 96
## 10 Orange (Orlando) -6.67 -1.47 -0.409 1.790 11.94 1.30e-02 3.00 96
## 11 Pinellas (Tampa) -8.65 -1.63 -0.491 1.117 13.00 6.78e-02 3.27 96
## 12 Portland -4.14 -1.45 -0.348 0.777 8.06 3.81e-02 2.36 96
## 13 PortlandSub -4.14 -1.45 -0.348 0.777 8.06 3.81e-02 2.36 96
## 14 St. Lucie -4.27 -1.78 -0.492 1.162 11.33 -1.71e-02 3.05 96
## 15 Stanly -3.57 -1.37 -0.207 1.023 7.21 -9.74e-19 1.91 96
## missing
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
favstats(All$TMEANcm ~ All$County.Area)
## .group min Q1 median Q3 max mean sd
## 1 Bar Harbor -7.17 -1.425 0.02778 1.497 7.56 6.91e-02 2.67
## 2 Camden -10.51 -1.819 0.15625 1.625 7.26 1.64e-17 2.91
## 3 Carteret -10.54 -1.503 -0.00625 1.519 6.79 -1.19e-17 2.59
## 4 Durham -8.74 -2.203 -0.32500 2.341 7.42 -3.32e-18 2.86
## 5 Duval (Jacksonville) -9.01 -1.083 0.19231 1.825 5.46 1.93e-01 2.51
## 6 Leon (Tallahassee) -8.58 -1.225 0.06538 2.254 7.46 2.15e-01 2.76
## 7 Lewiston -6.96 -1.483 0.01667 1.703 7.10 6.23e-02 2.67
## 8 LewistonSub -6.96 -1.483 0.01667 1.703 7.10 6.23e-02 2.67
## 9 Miami-Dade -8.96 -0.760 0.12692 0.969 5.56 -8.01e-04 2.10
## 10 Orange (Orlando) -11.08 -0.887 0.01538 1.694 5.28 9.07e-02 2.59
## 11 Pinellas (Tampa) -12.10 -1.179 0.16154 1.538 5.03 -1.32e-01 3.00
## 12 Portland -6.78 -1.553 -0.22222 1.683 7.30 4.46e-02 2.60
## 13 PortlandSub -6.78 -1.553 -0.22222 1.683 7.30 4.46e-02 2.60
## 14 St. Lucie -10.81 -0.960 0.12692 1.415 5.68 8.00e-02 2.54
## 15 Stanly -9.96 -2.047 -0.20625 2.013 9.36 1.14e-17 2.88
## n missing
## 1 96 0
## 2 96 0
## 3 96 0
## 4 96 0
## 5 96 0
## 6 96 0
## 7 96 0
## 8 96 0
## 9 96 0
## 10 96 0
## 11 96 0
## 12 96 0
## 13 96 0
## 14 96 0
## 15 96 0
densityplot(~TotalSTD, data = All)
densityplot(~PRECIPcm, data = All)
densityplot(~TMEANcm, data = All)
## assuming same intercepts, no time effects
m1 = lm(TotalSTD ~ TMEANcm, data = All)
summary(m1) # nonsig
##
## Call:
## lm(formula = TotalSTD ~ TMEANcm, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.910 -0.719 -0.086 0.612 3.642
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16058 0.02567 6.26 5.2e-10 ***
## TMEANcm 0.00863 0.00965 0.89 0.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.974 on 1438 degrees of freedom
## Multiple R-squared: 0.000555, Adjusted R-squared: -0.00014
## F-statistic: 0.798 on 1 and 1438 DF, p-value: 0.372
m2 = lm(TotalSTD ~ PRECIPcm, data = All)
summary(m2) # nonsig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPcm, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.919 -0.723 -0.082 0.607 3.622
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1613 0.0257 6.28 4.3e-10 ***
## PRECIPcm -0.0114 0.0103 -1.11 0.27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.974 on 1438 degrees of freedom
## Multiple R-squared: 0.000852, Adjusted R-squared: 0.000157
## F-statistic: 1.23 on 1 and 1438 DF, p-value: 0.268
m3 = lm(TotalSTD ~ PRECIPcm + TMEANcm, data = All)
summary(m3) # nonsig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPcm + TMEANcm, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.922 -0.722 -0.082 0.602 3.629
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.16086 0.02567 6.27 4.8e-10 ***
## PRECIPcm -0.01063 0.01032 -1.03 0.30
## TMEANcm 0.00772 0.00969 0.80 0.43
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.974 on 1437 degrees of freedom
## Multiple R-squared: 0.00129, Adjusted R-squared: -9.74e-05
## F-statistic: 0.93 on 2 and 1437 DF, p-value: 0.395
## with quadratic time trends & county effects
m1a = lm(TotalSTD ~ TMEANcm + t + t2 + County.Area, data = All)
summary(m1a) # sig at 0.1 level
##
## Call:
## lm(formula = TotalSTD ~ TMEANcm + t + t2 + County.Area, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.539 -0.663 -0.079 0.537 3.285
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.72e-01 1.26e-01 6.11 1.3e-09 ***
## TMEANcm 1.56e-02 9.31e-03 1.68 0.09350 .
## t -2.82e-02 3.83e-03 -7.36 3.0e-13 ***
## t2 1.97e-04 3.56e-05 5.54 3.6e-08 ***
## County.AreaCamden 1.27e-02 1.33e-01 0.10 0.92391
## County.AreaCarteret 1.27e-02 1.33e-01 0.10 0.92391
## County.AreaDurham 1.27e-02 1.33e-01 0.10 0.92391
## County.AreaDuval (Jacksonville) 3.84e-01 1.33e-01 2.88 0.00404 **
## County.AreaLeon (Tallahassee) 3.99e-01 1.33e-01 2.99 0.00282 **
## County.AreaLewiston 3.17e-02 1.33e-01 0.24 0.81204
## County.AreaLewistonSub 1.20e-04 1.33e-01 0.00 0.99928
## County.AreaMiami-Dade 4.99e-01 1.33e-01 3.74 0.00019 ***
## County.AreaOrange (Orlando) 5.09e-01 1.33e-01 3.82 0.00014 ***
## County.AreaPinellas (Tampa) 2.53e-01 1.33e-01 1.90 0.05793 .
## County.AreaPortland 4.44e-03 1.33e-01 0.03 0.97344
## County.AreaPortlandSub -1.41e-02 1.33e-01 -0.11 0.91561
## County.AreaSt. Lucie 4.78e-01 1.33e-01 3.59 0.00035 ***
## County.AreaStanly 1.27e-02 1.33e-01 0.10 0.92391
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.923 on 1422 degrees of freedom
## Multiple R-squared: 0.111, Adjusted R-squared: 0.101
## F-statistic: 10.5 on 17 and 1422 DF, p-value: <2e-16
plot(m1a, which = 1)
plot(m1a, which = 2)
plot(m1a$residuals[which(All$County.Area == "Miami-Dade")] ~ All$t[which(All$County.Area ==
"Miami-Dade")])
SCtest(m1a) # highly serially correlated
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.841 -0.552 -0.116 0.474 3.338
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.05e-05 2.24e-02 0.0 1
## res[-1] 3.83e-01 2.44e-02 15.7 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.849 on 1437 degrees of freedom
## Multiple R-squared: 0.146, Adjusted R-squared: 0.146
## F-statistic: 246 on 1 and 1437 DF, p-value: <2e-16
m2a = lm(TotalSTD ~ PRECIPcm + t + t2 + County.Area, data = All)
summary(m2a) # not sig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPcm + t + t2 + County.Area, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.548 -0.666 -0.075 0.526 3.264
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.74e-01 1.26e-01 6.12 1.2e-09 ***
## PRECIPcm -1.17e-02 9.76e-03 -1.20 0.22979
## t -2.85e-02 3.82e-03 -7.45 1.6e-13 ***
## t2 2.02e-04 3.54e-05 5.70 1.4e-08 ***
## County.AreaCamden 9.99e-03 1.33e-01 0.07 0.94031
## County.AreaCarteret 9.99e-03 1.33e-01 0.07 0.94031
## County.AreaDurham 9.99e-03 1.33e-01 0.07 0.94031
## County.AreaDuval (Jacksonville) 3.84e-01 1.33e-01 2.88 0.00400 **
## County.AreaLeon (Tallahassee) 3.98e-01 1.33e-01 2.99 0.00286 **
## County.AreaLewiston 3.10e-02 1.33e-01 0.23 0.81620
## County.AreaLewistonSub -5.80e-04 1.33e-01 0.00 0.99653
## County.AreaMiami-Dade 4.95e-01 1.33e-01 3.71 0.00021 ***
## County.AreaOrange (Orlando) 5.08e-01 1.33e-01 3.81 0.00014 ***
## County.AreaPinellas (Tampa) 2.49e-01 1.33e-01 1.87 0.06211 .
## County.AreaPortland 2.84e-03 1.33e-01 0.02 0.98303
## County.AreaPortlandSub -1.57e-02 1.33e-01 -0.12 0.90613
## County.AreaSt. Lucie 4.76e-01 1.33e-01 3.57 0.00037 ***
## County.AreaStanly 9.99e-03 1.33e-01 0.07 0.94031
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.924 on 1422 degrees of freedom
## Multiple R-squared: 0.111, Adjusted R-squared: 0.0999
## F-statistic: 10.4 on 17 and 1422 DF, p-value: <2e-16
m3a = lm(TotalSTD ~ PRECIPcm + TMEANcm + t + t2 + County.Area, data = All)
summary(m3a) # not sig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPcm + TMEANcm + t + t2 + County.Area,
## data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.551 -0.665 -0.085 0.531 3.272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.74e-01 1.26e-01 6.13 1.2e-09 ***
## PRECIPcm -1.04e-02 9.79e-03 -1.06 0.28996
## TMEANcm 1.48e-02 9.35e-03 1.58 0.11459
## t -2.82e-02 3.83e-03 -7.37 2.9e-13 ***
## t2 1.97e-04 3.56e-05 5.54 3.5e-08 ***
## County.AreaCamden 1.12e-02 1.33e-01 0.08 0.93305
## County.AreaCarteret 1.12e-02 1.33e-01 0.08 0.93305
## County.AreaDurham 1.12e-02 1.33e-01 0.08 0.93305
## County.AreaDuval (Jacksonville) 3.83e-01 1.33e-01 2.87 0.00414 **
## County.AreaLeon (Tallahassee) 3.97e-01 1.33e-01 2.98 0.00298 **
## County.AreaLewiston 3.12e-02 1.33e-01 0.23 0.81512
## County.AreaLewistonSub -4.10e-04 1.33e-01 0.00 0.99755
## County.AreaMiami-Dade 4.96e-01 1.33e-01 3.72 0.00020 ***
## County.AreaOrange (Orlando) 5.08e-01 1.33e-01 3.81 0.00014 ***
## County.AreaPinellas (Tampa) 2.52e-01 1.33e-01 1.89 0.05886 .
## County.AreaPortland 3.34e-03 1.33e-01 0.03 0.98001
## County.AreaPortlandSub -1.52e-02 1.33e-01 -0.11 0.90907
## County.AreaSt. Lucie 4.76e-01 1.33e-01 3.57 0.00036 ***
## County.AreaStanly 1.12e-02 1.33e-01 0.08 0.93305
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.923 on 1421 degrees of freedom
## Multiple R-squared: 0.112, Adjusted R-squared: 0.101
## F-statistic: 9.97 on 18 and 1421 DF, p-value: <2e-16
SCtest(m3a) # highly serially correlated
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.893 -0.551 -0.109 0.475 3.333
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.42e-05 2.24e-02 0.0 1
## res[-1] 3.82e-01 2.44e-02 15.7 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.849 on 1437 degrees of freedom
## Multiple R-squared: 0.146, Adjusted R-squared: 0.145
## F-statistic: 245 on 1 and 1437 DF, p-value: <2e-16
## with month*year & county FE
m1b = lm(TotalSTD ~ TMEANcm + factor(t) + County.Area, data = All)
summary(m1b) # nonsig
##
## Call:
## lm(formula = TotalSTD ~ TMEANcm + factor(t) + County.Area, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.499 -0.521 -0.030 0.488 3.525
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.55e-01 2.19e-01 2.53 0.01143 *
## TMEANcm -5.58e-03 1.14e-02 -0.49 0.62401
## factor(t)6 -2.31e-01 2.89e-01 -0.80 0.42493
## factor(t)7 -3.61e-01 2.89e-01 -1.25 0.21189
## factor(t)8 -5.75e-01 2.89e-01 -1.99 0.04695 *
## factor(t)9 -3.62e-01 2.89e-01 -1.25 0.21152
## factor(t)10 -2.98e-01 2.89e-01 -1.03 0.30346
## factor(t)11 7.75e-01 2.89e-01 2.68 0.00754 **
## factor(t)12 -9.40e-01 2.89e-01 -3.25 0.00118 **
## factor(t)13 -7.79e-01 2.90e-01 -2.69 0.00725 **
## factor(t)14 4.39e-01 2.90e-01 1.51 0.13053
## factor(t)15 -3.72e-02 2.89e-01 -0.13 0.89772
## factor(t)16 1.23e-01 2.90e-01 0.43 0.67044
## factor(t)17 1.54e-01 2.90e-01 0.53 0.59620
## factor(t)18 -3.24e-01 2.89e-01 -1.12 0.26237
## factor(t)19 -2.58e-01 2.90e-01 -0.89 0.37332
## factor(t)20 -3.80e-01 2.90e-01 -1.31 0.19021
## factor(t)21 -8.11e-01 2.90e-01 -2.80 0.00526 **
## factor(t)22 -7.67e-01 2.89e-01 -2.65 0.00812 **
## factor(t)23 1.20e+00 2.91e-01 4.14 3.7e-05 ***
## factor(t)24 -9.52e-01 2.90e-01 -3.28 0.00106 **
## factor(t)25 -1.03e+00 2.89e-01 -3.55 0.00039 ***
## factor(t)26 -4.03e-02 2.90e-01 -0.14 0.88938
## factor(t)27 -4.56e-01 2.89e-01 -1.58 0.11459
## factor(t)28 -4.63e-01 2.89e-01 -1.60 0.10925
## factor(t)29 3.67e-01 2.89e-01 1.27 0.20439
## factor(t)30 -8.69e-02 2.90e-01 -0.30 0.76440
## factor(t)31 -7.03e-02 2.89e-01 -0.24 0.80796
## factor(t)32 -3.60e-01 2.89e-01 -1.24 0.21336
## factor(t)33 -4.41e-01 2.89e-01 -1.53 0.12727
## factor(t)34 -7.35e-01 2.94e-01 -2.50 0.01256 *
## factor(t)35 7.00e-01 2.95e-01 2.38 0.01766 *
## factor(t)36 -7.93e-01 2.89e-01 -2.74 0.00614 **
## factor(t)37 -1.05e+00 2.89e-01 -3.62 0.00030 ***
## factor(t)38 -1.55e-01 2.89e-01 -0.53 0.59339
## factor(t)39 -6.06e-01 2.89e-01 -2.09 0.03650 *
## factor(t)40 -3.78e-01 2.89e-01 -1.31 0.19082
## factor(t)41 1.51e-01 2.89e-01 0.52 0.60112
## factor(t)42 -3.43e-01 2.89e-01 -1.18 0.23627
## factor(t)43 -3.71e-02 2.89e-01 -0.13 0.89803
## factor(t)44 -5.42e-01 2.92e-01 -1.86 0.06347 .
## factor(t)45 -6.17e-01 2.89e-01 -2.13 0.03314 *
## factor(t)46 -6.92e-01 2.91e-01 -2.38 0.01737 *
## factor(t)47 4.02e-01 2.89e-01 1.39 0.16497
## factor(t)48 -9.96e-01 2.93e-01 -3.40 0.00068 ***
## factor(t)49 -1.28e+00 2.89e-01 -4.43 1.0e-05 ***
## factor(t)50 -4.86e-01 2.89e-01 -1.68 0.09337 .
## factor(t)51 -6.81e-01 2.89e-01 -2.35 0.01868 *
## factor(t)52 -5.62e-01 2.89e-01 -1.94 0.05248 .
## factor(t)53 -1.43e-02 2.89e-01 -0.05 0.96064
## factor(t)54 -4.40e-01 2.89e-01 -1.52 0.12846
## factor(t)55 -4.68e-01 2.89e-01 -1.62 0.10556
## factor(t)56 -7.39e-01 2.89e-01 -2.56 0.01069 *
## factor(t)57 -1.12e+00 2.90e-01 -3.85 0.00012 ***
## factor(t)58 -1.48e+00 2.90e-01 -5.12 3.5e-07 ***
## factor(t)59 -3.43e-02 2.89e-01 -0.12 0.90567
## factor(t)60 -1.72e+00 2.90e-01 -5.93 3.9e-09 ***
## factor(t)61 -1.73e+00 2.89e-01 -5.99 2.7e-09 ***
## factor(t)62 -1.20e+00 2.89e-01 -4.15 3.6e-05 ***
## factor(t)63 -1.36e+00 2.89e-01 -4.69 3.1e-06 ***
## factor(t)64 -1.24e+00 2.89e-01 -4.30 1.9e-05 ***
## factor(t)65 -7.55e-01 2.89e-01 -2.61 0.00918 **
## factor(t)66 -8.38e-01 2.89e-01 -2.90 0.00381 **
## factor(t)67 -6.43e-01 2.89e-01 -2.22 0.02637 *
## factor(t)68 -8.78e-01 2.89e-01 -3.04 0.00243 **
## factor(t)69 -1.22e+00 2.89e-01 -4.21 2.7e-05 ***
## factor(t)70 -1.38e+00 2.90e-01 -4.76 2.1e-06 ***
## factor(t)71 -2.03e-01 2.91e-01 -0.70 0.48496
## factor(t)72 -1.82e+00 2.91e-01 -6.24 5.7e-10 ***
## factor(t)73 -1.63e+00 2.89e-01 -5.63 2.2e-08 ***
## factor(t)74 -7.86e-01 2.91e-01 -2.70 0.00693 **
## factor(t)75 -1.03e+00 2.92e-01 -3.54 0.00042 ***
## factor(t)76 -1.03e+00 2.93e-01 -3.51 0.00047 ***
## factor(t)77 -4.59e-01 2.90e-01 -1.58 0.11383
## factor(t)78 -8.07e-01 2.90e-01 -2.78 0.00548 **
## factor(t)79 -6.57e-01 2.90e-01 -2.27 0.02362 *
## factor(t)80 -8.37e-01 2.89e-01 -2.89 0.00391 **
## factor(t)81 -1.05e+00 2.89e-01 -3.62 0.00031 ***
## factor(t)82 -1.20e+00 2.97e-01 -4.05 5.5e-05 ***
## factor(t)83 4.26e-01 2.90e-01 1.47 0.14207
## factor(t)84 -1.50e+00 2.90e-01 -5.18 2.6e-07 ***
## factor(t)85 -1.34e+00 2.89e-01 -4.65 3.7e-06 ***
## factor(t)86 -4.02e-01 2.90e-01 -1.39 0.16580
## factor(t)87 -7.96e-01 2.89e-01 -2.75 0.00596 **
## factor(t)88 -8.12e-01 2.90e-01 -2.81 0.00510 **
## factor(t)89 -1.37e-01 2.89e-01 -0.47 0.63548
## factor(t)90 -4.16e-01 2.90e-01 -1.43 0.15200
## factor(t)91 -3.97e-01 2.89e-01 -1.37 0.17011
## factor(t)92 -4.12e-01 2.89e-01 -1.43 0.15397
## factor(t)93 -7.71e-01 2.90e-01 -2.66 0.00794 **
## factor(t)94 -6.81e-01 2.93e-01 -2.32 0.02024 *
## factor(t)95 5.47e-01 2.92e-01 1.88 0.06082 .
## factor(t)96 -1.25e+00 2.93e-01 -4.27 2.1e-05 ***
## factor(t)97 -8.31e-01 2.97e-01 -2.80 0.00517 **
## factor(t)98 -8.43e-02 2.91e-01 -0.29 0.77201
## factor(t)99 -5.64e-01 2.90e-01 -1.94 0.05207 .
## factor(t)100 -4.67e-01 2.89e-01 -1.61 0.10672
## County.AreaCamden 1.13e-02 1.14e-01 0.10 0.92148
## County.AreaCarteret 1.13e-02 1.14e-01 0.10 0.92148
## County.AreaDurham 1.13e-02 1.14e-01 0.10 0.92148
## County.AreaDuval (Jacksonville) 3.86e-01 1.14e-01 3.38 0.00074 ***
## County.AreaLeon (Tallahassee) 4.02e-01 1.14e-01 3.52 0.00045 ***
## County.AreaLewiston 3.16e-02 1.14e-01 0.28 0.78247
## County.AreaLewistonSub -2.45e-05 1.14e-01 0.00 0.99983
## County.AreaMiami-Dade 4.97e-01 1.14e-01 4.35 1.5e-05 ***
## County.AreaOrange (Orlando) 5.10e-01 1.14e-01 4.46 8.8e-06 ***
## County.AreaPinellas (Tampa) 2.49e-01 1.14e-01 2.18 0.02972 *
## County.AreaPortland 3.92e-03 1.14e-01 0.03 0.97265
## County.AreaPortlandSub -1.46e-02 1.14e-01 -0.13 0.89803
## County.AreaSt. Lucie 4.78e-01 1.14e-01 4.19 3.0e-05 ***
## County.AreaStanly 1.13e-02 1.14e-01 0.10 0.92148
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.792 on 1329 degrees of freedom
## Multiple R-squared: 0.39, Adjusted R-squared: 0.339
## F-statistic: 7.71 on 110 and 1329 DF, p-value: <2e-16
m2b = lm(TotalSTD ~ PRECIPcm + factor(t) + County.Area, data = All)
summary(m2b) # nonsig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPcm + factor(t) + County.Area, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.502 -0.514 -0.036 0.482 3.517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.551267 0.218981 2.52 0.01194 *
## PRECIPcm -0.006976 0.009694 -0.72 0.47185
## factor(t)6 -0.209355 0.289693 -0.72 0.47000
## factor(t)7 -0.340436 0.290416 -1.17 0.24131
## factor(t)8 -0.574177 0.289024 -1.99 0.04717 *
## factor(t)9 -0.370057 0.289051 -1.28 0.20068
## factor(t)10 -0.290424 0.289023 -1.00 0.31515
## factor(t)11 0.770314 0.289057 2.66 0.00779 **
## factor(t)12 -0.930014 0.289184 -3.22 0.00133 **
## factor(t)13 -0.756502 0.289646 -2.61 0.00911 **
## factor(t)14 0.464527 0.289594 1.60 0.10894
## factor(t)15 -0.015292 0.289889 -0.05 0.95794
## factor(t)16 0.160383 0.291323 0.55 0.58205
## factor(t)17 0.148467 0.289174 0.51 0.60774
## factor(t)18 -0.325155 0.289127 -1.12 0.26096
## factor(t)19 -0.278386 0.289209 -0.96 0.33593
## factor(t)20 -0.376274 0.289812 -1.30 0.19440
## factor(t)21 -0.796713 0.291292 -2.74 0.00632 **
## factor(t)22 -0.745503 0.289857 -2.57 0.01022 *
## factor(t)23 1.194913 0.289148 4.13 3.8e-05 ***
## factor(t)24 -0.951624 0.289569 -3.29 0.00104 **
## factor(t)25 -1.031496 0.289116 -3.57 0.00037 ***
## factor(t)26 -0.051081 0.289022 -0.18 0.85974
## factor(t)27 -0.456434 0.289024 -1.58 0.11452
## factor(t)28 -0.441410 0.290570 -1.52 0.12897
## factor(t)29 0.380640 0.290276 1.31 0.18998
## factor(t)30 -0.092696 0.289076 -0.32 0.74852
## factor(t)31 -0.060954 0.289070 -0.21 0.83303
## factor(t)32 -0.353764 0.289048 -1.22 0.22121
## factor(t)33 -0.421005 0.290220 -1.45 0.14712
## factor(t)34 -0.748639 0.289560 -2.59 0.00983 **
## factor(t)35 0.678380 0.289161 2.35 0.01912 *
## factor(t)36 -0.786456 0.289129 -2.72 0.00661 **
## factor(t)37 -1.047506 0.289060 -3.62 0.00030 ***
## factor(t)38 -0.140976 0.289218 -0.49 0.62603
## factor(t)39 -0.586755 0.289462 -2.03 0.04286 *
## factor(t)40 -0.373360 0.289150 -1.29 0.19685
## factor(t)41 0.146930 0.289030 0.51 0.61129
## factor(t)42 -0.359594 0.289305 -1.24 0.21410
## factor(t)43 -0.042956 0.289021 -0.15 0.88187
## factor(t)44 -0.546042 0.289933 -1.88 0.05987 .
## factor(t)45 -0.626147 0.289028 -2.17 0.03046 *
## factor(t)46 -0.697697 0.289313 -2.41 0.01602 *
## factor(t)47 0.409373 0.289121 1.42 0.15703
## factor(t)48 -1.003898 0.289781 -3.46 0.00055 ***
## factor(t)49 -1.265900 0.290366 -4.36 1.4e-05 ***
## factor(t)50 -0.459816 0.290584 -1.58 0.11380
## factor(t)51 -0.679803 0.289037 -2.35 0.01882 *
## factor(t)52 -0.575724 0.289242 -1.99 0.04675 *
## factor(t)53 -0.004649 0.289438 -0.02 0.98719
## factor(t)54 -0.419746 0.290571 -1.44 0.14882
## factor(t)55 -0.459232 0.289277 -1.59 0.11263
## factor(t)56 -0.712068 0.290680 -2.45 0.01443 *
## factor(t)57 -1.102425 0.289050 -3.81 0.00014 ***
## factor(t)58 -1.490591 0.289112 -5.16 2.9e-07 ***
## factor(t)59 -0.026249 0.289114 -0.09 0.92767
## factor(t)60 -1.707202 0.289021 -5.91 4.4e-09 ***
## factor(t)61 -1.722122 0.289193 -5.95 3.3e-09 ***
## factor(t)62 -1.191795 0.289153 -4.12 4.0e-05 ***
## factor(t)63 -1.342300 0.290662 -4.62 4.2e-06 ***
## factor(t)64 -1.248210 0.289038 -4.32 1.7e-05 ***
## factor(t)65 -0.728921 0.290508 -2.51 0.01222 *
## factor(t)66 -0.824090 0.289401 -2.85 0.00447 **
## factor(t)67 -0.645883 0.289059 -2.23 0.02562 *
## factor(t)68 -0.886871 0.289121 -3.07 0.00220 **
## factor(t)69 -1.209969 0.289435 -4.18 3.1e-05 ***
## factor(t)70 -1.361651 0.291557 -4.67 3.3e-06 ***
## factor(t)71 -0.170566 0.290022 -0.59 0.55656
## factor(t)72 -1.784850 0.289800 -6.16 9.7e-10 ***
## factor(t)73 -1.603498 0.290497 -5.52 4.1e-08 ***
## factor(t)74 -0.785328 0.289977 -2.71 0.00685 **
## factor(t)75 -1.052261 0.289024 -3.64 0.00028 ***
## factor(t)76 -1.056232 0.289208 -3.65 0.00027 ***
## factor(t)77 -0.472331 0.289036 -1.63 0.10246
## factor(t)78 -0.811887 0.289214 -2.81 0.00507 **
## factor(t)79 -0.661212 0.289196 -2.29 0.02239 *
## factor(t)80 -0.848656 0.289106 -2.94 0.00339 **
## factor(t)81 -1.041650 0.289104 -3.60 0.00033 ***
## factor(t)82 -1.166131 0.289048 -4.03 5.8e-05 ***
## factor(t)83 0.450128 0.289793 1.55 0.12060
## factor(t)84 -1.508661 0.289032 -5.22 2.1e-07 ***
## factor(t)85 -1.332588 0.289405 -4.60 4.5e-06 ***
## factor(t)86 -0.407160 0.289212 -1.41 0.15942
## factor(t)87 -0.798511 0.289026 -2.76 0.00581 **
## factor(t)88 -0.823650 0.289049 -2.85 0.00445 **
## factor(t)89 -0.145785 0.289026 -0.50 0.61406
## factor(t)90 -0.416826 0.289391 -1.44 0.15000
## factor(t)91 -0.388358 0.289455 -1.34 0.17993
## factor(t)92 -0.392977 0.290095 -1.35 0.17576
## factor(t)93 -0.778251 0.289103 -2.69 0.00719 **
## factor(t)94 -0.706456 0.289025 -2.44 0.01464 *
## factor(t)95 0.529791 0.289028 1.83 0.06702 .
## factor(t)96 -1.265658 0.289290 -4.38 1.3e-05 ***
## factor(t)97 -0.862635 0.289033 -2.98 0.00289 **
## factor(t)98 -0.097971 0.289052 -0.34 0.73471
## factor(t)99 -0.556736 0.290450 -1.92 0.05548 .
## factor(t)100 -0.448049 0.290284 -1.54 0.12295
## County.AreaCamden 0.010660 0.114254 0.09 0.92568
## County.AreaCarteret 0.010660 0.114254 0.09 0.92568
## County.AreaDurham 0.010660 0.114254 0.09 0.92568
## County.AreaDuval (Jacksonville) 0.384924 0.114251 3.37 0.00078 ***
## County.AreaLeon (Tallahassee) 0.399493 0.114267 3.50 0.00049 ***
## County.AreaLewiston 0.031238 0.114247 0.27 0.78457
## County.AreaLewistonSub -0.000339 0.114247 0.00 0.99763
## County.AreaMiami-Dade 0.496020 0.114265 4.34 1.5e-05 ***
## County.AreaOrange (Orlando) 0.508756 0.114252 4.45 9.2e-06 ***
## County.AreaPinellas (Tampa) 0.249287 0.114248 2.18 0.02929 *
## County.AreaPortland 0.003330 0.114250 0.03 0.97675
## County.AreaPortlandSub -0.015233 0.114250 -0.13 0.89395
## County.AreaSt. Lucie 0.477151 0.114256 4.18 3.2e-05 ***
## County.AreaStanly 0.010660 0.114254 0.09 0.92568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.792 on 1329 degrees of freedom
## Multiple R-squared: 0.39, Adjusted R-squared: 0.339
## F-statistic: 7.71 on 110 and 1329 DF, p-value: <2e-16
m3b = lm(TotalSTD ~ PRECIPcm + TMEANcm + factor(t) + County.Area, data = All)
summary(m3b) # nonsig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPcm + TMEANcm + factor(t) + County.Area,
## data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.507 -0.512 -0.034 0.482 3.524
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.547010 0.219192 2.50 0.01270 *
## PRECIPcm -0.007202 0.009706 -0.74 0.45821
## TMEANcm -0.005958 0.011398 -0.52 0.60125
## factor(t)6 -0.216876 0.290129 -0.75 0.45488
## factor(t)7 -0.339899 0.290498 -1.17 0.24219
## factor(t)8 -0.573832 0.289103 -1.98 0.04736 *
## factor(t)9 -0.364303 0.289339 -1.26 0.20822
## factor(t)10 -0.297432 0.289413 -1.03 0.30427
## factor(t)11 0.778455 0.289555 2.69 0.00727 **
## factor(t)12 -0.933215 0.289328 -3.23 0.00129 **
## factor(t)13 -0.765203 0.290203 -2.64 0.00847 **
## factor(t)14 0.451687 0.290713 1.55 0.12049
## factor(t)15 -0.020929 0.290168 -0.07 0.94251
## factor(t)16 0.149819 0.292103 0.51 0.60811
## factor(t)17 0.161555 0.290335 0.56 0.57800
## factor(t)18 -0.318178 0.289514 -1.10 0.27197
## factor(t)19 -0.265329 0.290365 -0.91 0.36100
## factor(t)20 -0.363426 0.290931 -1.25 0.21182
## factor(t)21 -0.783066 0.292539 -2.68 0.00752 **
## factor(t)22 -0.750914 0.290121 -2.59 0.00975 **
## factor(t)23 1.212085 0.291087 4.16 3.3e-05 ***
## factor(t)24 -0.937836 0.290847 -3.22 0.00129 **
## factor(t)25 -1.033108 0.289212 -3.57 0.00037 ***
## factor(t)26 -0.038905 0.290038 -0.13 0.89331
## factor(t)27 -0.457502 0.289110 -1.58 0.11379
## factor(t)28 -0.441005 0.290651 -1.52 0.12943
## factor(t)29 0.387822 0.290681 1.33 0.18237
## factor(t)30 -0.081996 0.289879 -0.28 0.77733
## factor(t)31 -0.066713 0.289359 -0.23 0.81770
## factor(t)32 -0.357249 0.289204 -1.24 0.21694
## factor(t)33 -0.421571 0.290302 -1.45 0.14669
## factor(t)34 -0.719994 0.294778 -2.44 0.01472 *
## factor(t)35 0.708987 0.295107 2.40 0.01642 *
## factor(t)36 -0.787595 0.289216 -2.72 0.00655 **
## factor(t)37 -1.051557 0.289243 -3.64 0.00029 ***
## factor(t)38 -0.146962 0.289524 -0.51 0.61182
## factor(t)39 -0.594647 0.289935 -2.05 0.04047 *
## factor(t)40 -0.371823 0.289244 -1.29 0.19884
## factor(t)41 0.149658 0.289156 0.52 0.60485
## factor(t)42 -0.351985 0.289750 -1.21 0.22466
## factor(t)43 -0.037177 0.289312 -0.13 0.89777
## factor(t)44 -0.523902 0.293089 -1.79 0.07408 .
## factor(t)45 -0.618278 0.289499 -2.14 0.03289 *
## factor(t)46 -0.681552 0.291036 -2.34 0.01934 *
## factor(t)47 0.407140 0.289232 1.41 0.15947
## factor(t)48 -0.979232 0.293676 -3.33 0.00088 ***
## factor(t)49 -1.260254 0.290646 -4.34 1.6e-05 ***
## factor(t)50 -0.463443 0.290747 -1.59 0.11118
## factor(t)51 -0.678264 0.289131 -2.35 0.01913 *
## factor(t)52 -0.569528 0.289564 -1.97 0.04941 *
## factor(t)53 -0.002619 0.289543 -0.01 0.99278
## factor(t)54 -0.417337 0.290687 -1.44 0.15133
## factor(t)55 -0.459113 0.289356 -1.59 0.11283
## factor(t)56 -0.716671 0.290893 -2.46 0.01388 *
## factor(t)57 -1.116139 0.290317 -3.84 0.00013 ***
## factor(t)58 -1.478314 0.290143 -5.10 4.0e-07 ***
## factor(t)59 -0.029006 0.289241 -0.10 0.92014
## factor(t)60 -1.719961 0.290128 -5.93 3.9e-09 ***
## factor(t)61 -1.724440 0.289307 -5.96 3.2e-09 ***
## factor(t)62 -1.191669 0.289232 -4.12 4.0e-05 ***
## factor(t)63 -1.333403 0.291239 -4.58 5.1e-06 ***
## factor(t)64 -1.240725 0.289471 -4.29 1.9e-05 ***
## factor(t)65 -0.733003 0.290693 -2.52 0.01180 *
## factor(t)66 -0.827356 0.289547 -2.86 0.00434 **
## factor(t)67 -0.639305 0.289412 -2.21 0.02735 *
## factor(t)68 -0.883833 0.289258 -3.06 0.00229 **
## factor(t)69 -1.206220 0.289603 -4.17 3.3e-05 ***
## factor(t)70 -1.350992 0.292349 -4.62 4.2e-06 ***
## factor(t)71 -0.186199 0.291639 -0.64 0.52329
## factor(t)72 -1.801248 0.291572 -6.18 8.6e-10 ***
## factor(t)73 -1.607711 0.290688 -5.53 3.8e-08 ***
## factor(t)74 -0.767938 0.291958 -2.63 0.00863 **
## factor(t)75 -1.031363 0.291854 -3.53 0.00042 ***
## factor(t)76 -1.032657 0.292782 -3.53 0.00043 ***
## factor(t)77 -0.460287 0.290032 -1.59 0.11275
## factor(t)78 -0.798411 0.290440 -2.75 0.00606 **
## factor(t)79 -0.648828 0.290243 -2.24 0.02555 *
## factor(t)80 -0.841315 0.289526 -2.91 0.00372 **
## factor(t)81 -1.040921 0.289186 -3.60 0.00033 ***
## factor(t)82 -1.202309 0.297295 -4.04 5.6e-05 ***
## factor(t)83 0.440623 0.290443 1.52 0.12949
## factor(t)84 -1.498049 0.289822 -5.17 2.7e-07 ***
## factor(t)85 -1.332806 0.289485 -4.60 4.5e-06 ***
## factor(t)86 -0.393687 0.290437 -1.36 0.17549
## factor(t)87 -0.794818 0.289191 -2.75 0.00607 **
## factor(t)88 -0.814823 0.289621 -2.81 0.00497 **
## factor(t)89 -0.138041 0.289485 -0.48 0.63355
## factor(t)90 -0.404050 0.290500 -1.39 0.16450
## factor(t)91 -0.384856 0.289612 -1.33 0.18412
## factor(t)92 -0.393909 0.290180 -1.36 0.17486
## factor(t)93 -0.765329 0.290236 -2.64 0.00846 **
## factor(t)94 -0.680999 0.293177 -2.32 0.02034 *
## factor(t)95 0.549868 0.291647 1.89 0.05960 .
## factor(t)96 -1.240237 0.293428 -4.23 2.5e-05 ***
## factor(t)97 -0.827233 0.296939 -2.79 0.00541 **
## factor(t)98 -0.080076 0.291150 -0.28 0.78333
## factor(t)99 -0.542127 0.291871 -1.86 0.06347 .
## factor(t)100 -0.446441 0.290380 -1.54 0.12442
## County.AreaCamden 0.010217 0.114288 0.09 0.92878
## County.AreaCarteret 0.010217 0.114288 0.09 0.92878
## County.AreaDurham 0.010217 0.114288 0.09 0.92878
## County.AreaDuval (Jacksonville) 0.385635 0.114290 3.37 0.00076 ***
## County.AreaLeon (Tallahassee) 0.400311 0.114309 3.50 0.00048 ***
## County.AreaLewiston 0.031186 0.114278 0.27 0.78498
## County.AreaLewistonSub -0.000392 0.114278 0.00 0.99727
## County.AreaMiami-Dade 0.495554 0.114299 4.34 1.6e-05 ***
## County.AreaOrange (Orlando) 0.508856 0.114284 4.45 9.2e-06 ***
## County.AreaPinellas (Tampa) 0.248074 0.114303 2.17 0.03016 *
## County.AreaPortland 0.003160 0.114282 0.03 0.97794
## County.AreaPortlandSub -0.015403 0.114282 -0.13 0.89281
## County.AreaSt. Lucie 0.477180 0.114287 4.18 3.2e-05 ***
## County.AreaStanly 0.010217 0.114288 0.09 0.92878
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.792 on 1328 degrees of freedom
## Multiple R-squared: 0.39, Adjusted R-squared: 0.339
## F-statistic: 7.64 on 111 and 1328 DF, p-value: <2e-16
## 1st differences model with quadratic time trends and County FE
m1c = lm(dTotalSTD ~ TMEANcm + t + t2 + County.Area, data = All)
summary(m1c) # nonsig
##
## Call:
## lm(formula = dTotalSTD ~ TMEANcm + t + t2 + County.Area, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.280 -0.483 -0.017 0.548 3.340
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.41e-02 1.41e-01 0.31 0.75
## TMEANcm -4.32e-03 1.04e-02 -0.42 0.68
## t -2.53e-03 4.29e-03 -0.59 0.56
## t2 2.59e-05 3.98e-05 0.65 0.52
## County.AreaCamden -3.92e-03 1.49e-01 -0.03 0.98
## County.AreaCarteret -1.79e-02 1.49e-01 -0.12 0.90
## County.AreaDurham -9.47e-03 1.49e-01 -0.06 0.95
## County.AreaDuval (Jacksonville) 2.37e-03 1.48e-01 0.02 0.99
## County.AreaLeon (Tallahassee) -4.78e-03 1.48e-01 -0.03 0.97
## County.AreaLewiston -1.72e-03 1.48e-01 -0.01 0.99
## County.AreaLewistonSub 1.94e-03 1.48e-01 0.01 0.99
## County.AreaMiami-Dade 1.42e-02 1.48e-01 0.10 0.92
## County.AreaOrange (Orlando) 1.41e-02 1.48e-01 0.10 0.92
## County.AreaPinellas (Tampa) -2.98e-03 1.48e-01 -0.02 0.98
## County.AreaPortland -2.74e-03 1.48e-01 -0.02 0.99
## County.AreaPortlandSub 5.27e-03 1.48e-01 0.04 0.97
## County.AreaSt. Lucie -1.12e-03 1.48e-01 -0.01 0.99
## County.AreaStanly -2.48e-02 1.49e-01 -0.17 0.87
##
## Residual standard error: 1.03 on 1418 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.000496, Adjusted R-squared: -0.0115
## F-statistic: 0.0414 on 17 and 1418 DF, p-value: 1
SCtest(m1c) # residuals still highly correlated over time
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.220 -0.439 0.099 0.626 3.163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000104 0.024808 0.0 1
## res[-1] -0.390734 0.024317 -16.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.94 on 1433 degrees of freedom
## Multiple R-squared: 0.153, Adjusted R-squared: 0.152
## F-statistic: 258 on 1 and 1433 DF, p-value: <2e-16
m2c = lm(dTotalSTD ~ PRECIPcm + t + t2 + County.Area, data = All)
summary(m2c) # nonsig
##
## Call:
## lm(formula = dTotalSTD ~ PRECIPcm + t + t2 + County.Area, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.264 -0.490 -0.019 0.549 3.389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.54e-02 1.41e-01 0.32 0.75
## PRECIPcm -5.05e-03 1.08e-02 -0.47 0.64
## t -2.44e-03 4.29e-03 -0.57 0.57
## t2 2.43e-05 3.96e-05 0.61 0.54
## County.AreaCamden -4.22e-03 1.49e-01 -0.03 0.98
## County.AreaCarteret -1.83e-02 1.49e-01 -0.12 0.90
## County.AreaDurham -9.96e-03 1.49e-01 -0.07 0.95
## County.AreaDuval (Jacksonville) 1.26e-03 1.48e-01 0.01 0.99
## County.AreaLeon (Tallahassee) -6.55e-03 1.48e-01 -0.04 0.96
## County.AreaLewiston -1.95e-03 1.48e-01 -0.01 0.99
## County.AreaLewistonSub 1.71e-03 1.48e-01 0.01 0.99
## County.AreaMiami-Dade 1.34e-02 1.48e-01 0.09 0.93
## County.AreaOrange (Orlando) 1.33e-02 1.48e-01 0.09 0.93
## County.AreaPinellas (Tampa) -2.49e-03 1.48e-01 -0.02 0.99
## County.AreaPortland -3.16e-03 1.48e-01 -0.02 0.98
## County.AreaPortlandSub 4.85e-03 1.48e-01 0.03 0.97
## County.AreaSt. Lucie -1.97e-03 1.48e-01 -0.01 0.99
## County.AreaStanly -2.51e-02 1.49e-01 -0.17 0.87
##
## Residual standard error: 1.03 on 1418 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.000526, Adjusted R-squared: -0.0115
## F-statistic: 0.0439 on 17 and 1418 DF, p-value: 1
SCtest(m2c) # residuals still highly correlated over time (also true for time fixed effects)
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.204 -0.442 0.096 0.631 3.209
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000117 0.024807 0.0 1
## res[-1] -0.390779 0.024317 -16.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.94 on 1433 degrees of freedom
## Multiple R-squared: 0.153, Adjusted R-squared: 0.152
## F-statistic: 258 on 1 and 1433 DF, p-value: <2e-16
## Setting up data
pdata = plm.data(All, index = c("County.Area", "t"))
## Assuming same intercepts, no time effects, with lags
m1d = plm(TotalSTD ~ TMEANcm + lag(TotalSTD, 1) + lag(TotalSTD, 2) + lag(TotalSTD,
3), data = pdata)
summary(m1d) #nonsig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ TMEANcm + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + lag(TotalSTD, 3), data = pdata)
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.0600 -0.5040 -0.0968 0.4090 3.5500
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## TMEANcm 0.00380 0.00832 0.46 0.647
## lag(TotalSTD, 1) 0.31154 0.02601 11.98 <2e-16 ***
## lag(TotalSTD, 2) 0.04847 0.02726 1.78 0.076 .
## lag(TotalSTD, 3) 0.25618 0.02590 9.89 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 948
## R-Squared : 0.191
## Adj. R-Squared : 0.188
## F-statistic: 108.708 on 4 and 1376 DF, p-value: <2e-16
SCtest(m1d) # no sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.101 -0.505 -0.102 0.400 3.567
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.62e-05 2.21e-02 0.00 1.0
## res[-1] 2.77e-02 2.68e-02 1.03 0.3
##
## Residual standard error: 0.825 on 1392 degrees of freedom
## Multiple R-squared: 0.000768, Adjusted R-squared: 4.99e-05
## F-statistic: 1.07 on 1 and 1392 DF, p-value: 0.301
m2d = plm(TotalSTD ~ PRECIPcm + lag(TotalSTD, 1) + lag(TotalSTD, 2) + lag(TotalSTD,
3), data = pdata)
summary(m2d) #nonsig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPcm + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + lag(TotalSTD, 3), data = pdata)
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.070 -0.505 -0.100 0.411 3.540
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPcm -0.00767 0.00898 -0.85 0.39
## lag(TotalSTD, 1) 0.31173 0.02598 12.00 <2e-16 ***
## lag(TotalSTD, 2) 0.04767 0.02723 1.75 0.08 .
## lag(TotalSTD, 3) 0.25695 0.02588 9.93 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 947
## R-Squared : 0.191
## Adj. R-Squared : 0.188
## F-statistic: 108.879 on 4 and 1376 DF, p-value: <2e-16
SCtest(m2d) # no sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.111 -0.510 -0.103 0.392 3.555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.67e-05 2.21e-02 0.00 1.0
## res[-1] 2.79e-02 2.68e-02 1.04 0.3
##
## Residual standard error: 0.825 on 1392 degrees of freedom
## Multiple R-squared: 0.000779, Adjusted R-squared: 6.15e-05
## F-statistic: 1.09 on 1 and 1392 DF, p-value: 0.298
## With quadratic time trends and County FE
m1e = plm(TotalSTD ~ TMEANcm + lag(TotalSTD, 1) + lag(TotalSTD, 2) + as.numeric(t) +
t2 + County.Area, data = pdata)
summary(m1e) #nonsig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ TMEANcm + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + as.numeric(t) + t2 + County.Area, data = pdata)
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.940 -0.509 -0.095 0.440 3.240
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## TMEANcm 9.71e-03 8.61e-03 1.13 0.25959
## lag(TotalSTD, 1) 3.30e-01 2.65e-02 12.44 < 2e-16 ***
## lag(TotalSTD, 2) 1.18e-01 2.64e-02 4.47 8.3e-06 ***
## as.numeric(t) -1.70e-02 3.86e-03 -4.39 1.2e-05 ***
## t2 1.22e-04 3.49e-05 3.50 0.00047 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1270
## Residual Sum of Squares: 999
## R-Squared : 0.166
## Adj. R-Squared : 0.163
## F-statistic: 74.1574 on 5 and 1390 DF, p-value: <2e-16
SCtest(m1e) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.879 -0.507 -0.083 0.436 3.191
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000183 0.022432 0.01 0.99
## res[-1] -0.037339 0.026645 -1.40 0.16
##
## Residual standard error: 0.842 on 1407 degrees of freedom
## Multiple R-squared: 0.00139, Adjusted R-squared: 0.000684
## F-statistic: 1.96 on 1 and 1407 DF, p-value: 0.161
m2e = plm(TotalSTD ~ PRECIPcm + lag(TotalSTD, 1) + lag(TotalSTD, 2) + as.numeric(t) +
t2 + County.Area, data = pdata)
summary(m2e) #nonsig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPcm + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + as.numeric(t) + t2 + County.Area, data = pdata)
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9400 -0.5090 -0.0978 0.4300 3.2200
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPcm -8.03e-03 9.04e-03 -0.89 0.37490
## lag(TotalSTD, 1) 3.31e-01 2.65e-02 12.51 < 2e-16 ***
## lag(TotalSTD, 2) 1.18e-01 2.65e-02 4.45 9.4e-06 ***
## as.numeric(t) -1.73e-02 3.86e-03 -4.48 8.2e-06 ***
## t2 1.26e-04 3.48e-05 3.63 0.00029 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1270
## Residual Sum of Squares: 999
## R-Squared : 0.165
## Adj. R-Squared : 0.163
## F-statistic: 74.0349 on 5 and 1390 DF, p-value: <2e-16
SCtest(m2e) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.883 -0.515 -0.084 0.438 3.172
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00021 0.02244 0.01 0.99
## res[-1] -0.03761 0.02664 -1.41 0.16
##
## Residual standard error: 0.842 on 1407 degrees of freedom
## Multiple R-squared: 0.00141, Adjusted R-squared: 0.000704
## F-statistic: 1.99 on 1 and 1407 DF, p-value: 0.158
## With month*year and County FE
m1f = plm(TotalSTD ~ TMEANcm + lag(TotalSTD, 1) + lag(TotalSTD, 2), data = pdata,
model = "within", effect = "twoways")
summary(m1f) #nonsig
## Twoways effects Within Model
##
## Call:
## plm(formula = TotalSTD ~ TMEANcm + lag(TotalSTD, 1) + lag(TotalSTD,
## 2), data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9600 -0.3660 -0.0263 0.3680 3.1700
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## TMEANcm -0.00342 0.00922 -0.37 0.71
## lag(TotalSTD, 1) 0.50664 0.02739 18.50 < 2e-16 ***
## lag(TotalSTD, 2) 0.11431 0.02732 4.18 3.1e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 805
## Residual Sum of Squares: 528
## R-Squared : 0.117
## Adj. R-Squared : 0.108
## F-statistic: 226.785 on 3 and 1299 DF, p-value: <2e-16
SCtest(m1f) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9277 -0.3656 -0.0208 0.3668 3.1196
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000182 0.016317 0.01 0.99
## res[-1] -0.017345 0.026668 -0.65 0.52
##
## Residual standard error: 0.612 on 1407 degrees of freedom
## Multiple R-squared: 0.000301, Adjusted R-squared: -0.00041
## F-statistic: 0.423 on 1 and 1407 DF, p-value: 0.516
m2f = plm(TotalSTD ~ PRECIPcm + lag(TotalSTD, 1) + lag(TotalSTD, 2), data = pdata,
model = "within", effect = "twoways")
summary(m2f) #nonsig
## Twoways effects Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPcm + lag(TotalSTD, 1) + lag(TotalSTD,
## 2), data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9600 -0.3660 -0.0239 0.3630 3.1600
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPcm -0.00378 0.00789 -0.48 0.63
## lag(TotalSTD, 1) 0.50631 0.02739 18.49 <2e-16 ***
## lag(TotalSTD, 2) 0.11444 0.02732 4.19 3e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 805
## Residual Sum of Squares: 528
## R-Squared : 0.117
## Adj. R-Squared : 0.108
## F-statistic: 226.832 on 3 and 1299 DF, p-value: <2e-16
SCtest(m2f) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9291 -0.3731 -0.0217 0.3661 3.1163
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000187 0.016316 0.01 0.99
## res[-1] -0.016933 0.026668 -0.63 0.53
##
## Residual standard error: 0.612 on 1407 degrees of freedom
## Multiple R-squared: 0.000286, Adjusted R-squared: -0.000424
## F-statistic: 0.403 on 1 and 1407 DF, p-value: 0.526
## 5% and 1% cutoffs
quantile(All$PRECIPcm, prob = seq(0, 1, 0.01))
## 0% 1% 2% 3% 4% 5% 6% 7% 8%
## -8.6523 -4.7344 -4.1146 -3.8667 -3.5444 -3.3875 -3.1439 -3.0478 -2.8607
## 9% 10% 11% 12% 13% 14% 15% 16% 17%
## -2.7571 -2.6600 -2.5512 -2.4222 -2.3270 -2.2248 -2.1191 -2.0487 -2.0122
## 18% 19% 20% 21% 22% 23% 24% 25% 26%
## -1.9600 -1.9018 -1.8073 -1.7343 -1.6851 -1.6331 -1.5585 -1.4926 -1.4576
## 27% 28% 29% 30% 31% 32% 33% 34% 35%
## -1.3902 -1.3409 -1.2957 -1.2358 -1.1956 -1.1425 -1.0950 -1.0245 -0.9778
## 36% 37% 38% 39% 40% 41% 42% 43% 44%
## -0.9321 -0.9020 -0.8346 -0.8074 -0.7730 -0.7456 -0.6787 -0.6189 -0.5804
## 45% 46% 47% 48% 49% 50% 51% 52% 53%
## -0.5369 -0.4993 -0.4538 -0.4199 -0.3837 -0.3253 -0.2881 -0.2600 -0.2062
## 54% 55% 56% 57% 58% 59% 60% 61% 62%
## -0.1782 -0.1278 -0.0844 -0.0150 0.0367 0.0609 0.1286 0.1807 0.2662
## 63% 64% 65% 66% 67% 68% 69% 70% 71%
## 0.3186 0.3892 0.4706 0.5272 0.6000 0.6499 0.6935 0.7322 0.8000
## 72% 73% 74% 75% 76% 77% 78% 79% 80%
## 0.8744 0.9220 1.0185 1.0931 1.1961 1.2418 1.3253 1.4133 1.5548
## 81% 82% 83% 84% 85% 86% 87% 88% 89%
## 1.6519 1.7522 1.8904 2.0319 2.1653 2.4457 2.6180 2.8578 3.0733
## 90% 91% 92% 93% 94% 95% 96% 97% 98%
## 3.2300 3.5273 3.7282 3.9515 4.3794 4.7647 5.2184 5.8109 7.0914
## 99% 100%
## 8.0644 12.9992
All$PRECIPlevels = cut(All$PRECIPcm, breaks = c(-10, -4.7344, -3.1439, 4.3794,
8.0644, 15), labels = c("Drought", "Dry", "Normal", "Wet", "Soaked"))
tally(~All$PRECIPlevels)
##
## Drought Dry Normal Wet Soaked
## 15 72 1266 71 16
quantile(All$TMEANcm, prob = seq(0, 1, 0.01))
## 0% 1% 2% 3% 4% 5% 6% 7%
## -12.1000 -7.5205 -6.0098 -5.5222 -4.6268 -4.3008 -3.9915 -3.6944
## 8% 9% 10% 11% 12% 13% 14% 15%
## -3.5312 -3.2184 -3.0846 -2.8779 -2.7036 -2.5992 -2.4765 -2.3754
## 16% 17% 18% 19% 20% 21% 22% 23%
## -2.2778 -2.1330 -2.0375 -1.8954 -1.8144 -1.6889 -1.6117 -1.5335
## 24% 25% 26% 27% 28% 29% 30% 31%
## -1.4768 -1.4111 -1.3383 -1.2667 -1.1875 -1.1375 -1.0821 -1.0384
## 32% 33% 34% 35% 36% 37% 38% 39%
## -0.9685 -0.8667 -0.7903 -0.7385 -0.6846 -0.6556 -0.5778 -0.5000
## 40% 41% 42% 43% 44% 45% 46% 47%
## -0.4515 -0.4154 -0.3692 -0.3398 -0.2778 -0.2538 -0.2000 -0.1538
## 48% 49% 50% 51% 52% 53% 54% 55%
## -0.0778 -0.0359 0.0226 0.0923 0.1333 0.1750 0.2158 0.3111
## 56% 57% 58% 59% 60% 61% 62% 63%
## 0.3623 0.4470 0.5154 0.5778 0.6250 0.6733 0.7552 0.8615
## 64% 65% 66% 67% 68% 69% 70% 71%
## 0.9370 1.0237 1.0615 1.1261 1.1685 1.2608 1.3250 1.4000
## 72% 73% 74% 75% 76% 77% 78% 79%
## 1.4224 1.5277 1.5871 1.7000 1.7615 1.8308 1.9318 2.0229
## 80% 81% 82% 83% 84% 85% 86% 87%
## 2.1292 2.2333 2.3384 2.4625 2.5366 2.6769 2.7625 2.9492
## 88% 89% 90% 91% 92% 93% 94% 95%
## 3.1222 3.3222 3.4000 3.5311 3.6330 3.7270 3.8974 4.1457
## 96% 97% 98% 99% 100%
## 4.5399 4.8571 5.2619 6.2111 9.3625
All$TMEANlevels = cut(All$TMEANcm, breaks = c(-16, -7.5205, -3.9915, 3.8974,
6.2111, 16), labels = c("Freezing", "Cold", "Normal", "Warm", "Hot"))
tally(~All$TMEANlevels)
##
## Freezing Cold Normal Warm Hot
## 15 72 1266 71 16
## Setting ref levels to Normal
All = within(All, PRECIPlevels <- relevel(PRECIPlevels, ref = "Normal"))
All = within(All, TMEANlevels <- relevel(TMEANlevels, ref = "Normal"))
## Validating choice of levels
table(All$PRECIPlevels, All$County.Area)
##
## Bar Harbor Camden Carteret Durham Duval (Jacksonville)
## Normal 85 84 83 92 87
## Drought 0 0 0 0 0
## Dry 4 6 7 3 3
## Wet 6 4 6 1 6
## Soaked 1 2 0 0 0
##
## Leon (Tallahassee) Lewiston LewistonSub Miami-Dade
## Normal 81 88 88 83
## Drought 2 0 0 4
## Dry 6 2 2 4
## Wet 7 6 6 4
## Soaked 0 0 0 1
##
## Orange (Orlando) Pinellas (Tampa) Portland PortlandSub St. Lucie
## Normal 78 77 88 88 75
## Drought 4 5 0 0 0
## Dry 8 4 3 3 12
## Wet 3 6 4 4 6
## Soaked 3 4 1 1 3
##
## Stanly
## Normal 89
## Drought 0
## Dry 5
## Wet 2
## Soaked 0
table(All$PRECIPlevels, All$Year)
##
## 2004 2005 2006 2007 2008 2009 2010 2011 2012
## Normal 91 153 157 161 149 157 158 169 71
## Drought 0 1 2 3 1 3 3 2 0
## Dry 8 6 12 13 11 7 10 5 0
## Wet 5 15 8 3 17 12 7 1 3
## Soaked 1 5 1 0 2 1 2 3 1
# Drought rare, but not dry. Both sig so all good!
## assuming same intercepts, no time effects
m1g = lm(TotalSTD ~ TMEANlevels, data = All)
summary(m1g) # sig
##
## Call:
## lm(formula = TotalSTD ~ TMEANlevels, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.932 -0.711 -0.086 0.603 3.609
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1882 0.0272 6.91 7.3e-12 ***
## TMEANlevelsFreezing -0.5886 0.2517 -2.34 0.0195 *
## TMEANlevelsCold -0.2619 0.1174 -2.23 0.0259 *
## TMEANlevelsWarm -0.0083 0.1182 -0.07 0.9440
## TMEANlevelsHot -0.6780 0.2438 -2.78 0.0055 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.969 on 1435 degrees of freedom
## Multiple R-squared: 0.012, Adjusted R-squared: 0.00929
## F-statistic: 4.37 on 4 and 1435 DF, p-value: 0.00162
SCtest(m1g) # highly serially correlated
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.112 -0.574 -0.078 0.467 3.527
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000464 0.022884 0.02 0.98
## res[-1] 0.442981 0.023645 18.73 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.868 on 1437 degrees of freedom
## Multiple R-squared: 0.196, Adjusted R-squared: 0.196
## F-statistic: 351 on 1 and 1437 DF, p-value: <2e-16
m2g = lm(TotalSTD ~ PRECIPlevels, data = All)
summary(m2g) # sig
##
## Call:
## lm(formula = TotalSTD ~ PRECIPlevels, data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.923 -0.727 -0.082 0.605 3.617
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.1798 0.0274 6.57 7e-11 ***
## PRECIPlevelsDrought -0.1162 0.2528 -0.46 0.646
## PRECIPlevelsDry -0.2272 0.1179 -1.93 0.054 .
## PRECIPlevelsWet -0.0875 0.1187 -0.74 0.461
## PRECIPlevelsSoaked -0.1683 0.2449 -0.69 0.492
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.973 on 1435 degrees of freedom
## Multiple R-squared: 0.00323, Adjusted R-squared: 0.000454
## F-statistic: 1.16 on 4 and 1435 DF, p-value: 0.325
SCtest(m2g) # highly serially correlated
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.187 -0.588 -0.080 0.471 3.530
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000488 0.022881 0.02 0.98
## res[-1] 0.451155 0.023538 19.17 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.868 on 1437 degrees of freedom
## Multiple R-squared: 0.204, Adjusted R-squared: 0.203
## F-statistic: 367 on 1 and 1437 DF, p-value: <2e-16
## 1st differences model with quadratic time trends and County FE
m1h = lm(dTotalSTD ~ TMEANlevels + t + t2 + County.Area, data = All)
summary(m1h) # sig
##
## Call:
## lm(formula = dTotalSTD ~ TMEANlevels + t + t2 + County.Area,
## data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.259 -0.473 -0.018 0.564 3.362
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.39e-02 1.41e-01 0.31 0.756
## TMEANlevelsFreezing -1.21e-01 2.68e-01 -0.45 0.652
## TMEANlevelsCold 1.18e-01 1.24e-01 0.95 0.341
## TMEANlevelsWarm 3.09e-01 1.25e-01 2.47 0.014 *
## TMEANlevelsHot -3.95e-01 2.61e-01 -1.51 0.131
## t -3.57e-03 4.30e-03 -0.83 0.406
## t2 3.59e-05 3.98e-05 0.90 0.368
## County.AreaCamden -1.08e-02 1.48e-01 -0.07 0.942
## County.AreaCarteret -1.06e-02 1.48e-01 -0.07 0.943
## County.AreaDurham -1.12e-02 1.48e-01 -0.08 0.940
## County.AreaDuval (Jacksonville) 5.90e-03 1.48e-01 0.04 0.968
## County.AreaLeon (Tallahassee) 1.56e-03 1.48e-01 0.01 0.992
## County.AreaLewiston 1.30e-02 1.48e-01 0.09 0.930
## County.AreaLewistonSub 1.66e-02 1.48e-01 0.11 0.910
## County.AreaMiami-Dade 2.18e-02 1.48e-01 0.15 0.883
## County.AreaOrange (Orlando) 1.81e-02 1.48e-01 0.12 0.903
## County.AreaPinellas (Tampa) -8.89e-03 1.48e-01 -0.06 0.952
## County.AreaPortland 2.71e-03 1.48e-01 0.02 0.985
## County.AreaPortlandSub 1.07e-02 1.48e-01 0.07 0.942
## County.AreaSt. Lucie 4.15e-03 1.48e-01 0.03 0.978
## County.AreaStanly -2.53e-02 1.48e-01 -0.17 0.864
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.02 on 1415 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.00707, Adjusted R-squared: -0.00696
## F-statistic: 0.504 on 20 and 1415 DF, p-value: 0.966
SCtest(m1h) # residuals still highly correlated over time
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.196 -0.440 0.098 0.619 3.193
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000113 0.024745 0 1
## res[-1] -0.389070 0.024336 -16 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.937 on 1433 degrees of freedom
## Multiple R-squared: 0.151, Adjusted R-squared: 0.151
## F-statistic: 256 on 1 and 1433 DF, p-value: <2e-16
m2h = lm(dTotalSTD ~ PRECIPlevels + t + t2 + County.Area, data = All)
summary(m2h) # sig
##
## Call:
## lm(formula = dTotalSTD ~ PRECIPlevels + t + t2 + County.Area,
## data = All)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.277 -0.475 -0.019 0.540 3.413
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.43e-02 1.42e-01 0.38 0.701
## PRECIPlevelsDrought -2.86e-01 2.71e-01 -1.06 0.291
## PRECIPlevelsDry -2.63e-01 1.26e-01 -2.10 0.036 *
## PRECIPlevelsWet -5.94e-02 1.26e-01 -0.47 0.637
## PRECIPlevelsSoaked -9.43e-02 2.60e-01 -0.36 0.717
## t -1.95e-03 4.29e-03 -0.45 0.650
## t2 1.87e-05 3.97e-05 0.47 0.638
## County.AreaCamden 1.87e-03 1.48e-01 0.01 0.990
## County.AreaCarteret -1.02e-02 1.48e-01 -0.07 0.945
## County.AreaDurham -1.59e-02 1.49e-01 -0.11 0.915
## County.AreaDuval (Jacksonville) -1.89e-03 1.48e-01 -0.01 0.990
## County.AreaLeon (Tallahassee) 5.67e-03 1.48e-01 0.04 0.969
## County.AreaLewiston -8.16e-03 1.48e-01 -0.06 0.956
## County.AreaLewistonSub -4.50e-03 1.48e-01 -0.03 0.976
## County.AreaMiami-Dade 2.52e-02 1.48e-01 0.17 0.865
## County.AreaOrange (Orlando) 3.70e-02 1.49e-01 0.25 0.804
## County.AreaPinellas (Tampa) 1.57e-02 1.49e-01 0.11 0.916
## County.AreaPortland -6.62e-03 1.48e-01 -0.04 0.964
## County.AreaPortlandSub 1.39e-03 1.48e-01 0.01 0.992
## County.AreaSt. Lucie 2.27e-02 1.48e-01 0.15 0.878
## County.AreaStanly -2.51e-02 1.49e-01 -0.17 0.866
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.03 on 1415 degrees of freedom
## (4 observations deleted due to missingness)
## Multiple R-squared: 0.00428, Adjusted R-squared: -0.0098
## F-statistic: 0.304 on 20 and 1415 DF, p-value: 0.999
SCtest(m2h) # residuals still highly correlated over time
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.221 -0.421 0.094 0.621 3.224
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.000127 0.024725 -0.01 1
## res[-1] -0.393889 0.024282 -16.22 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.937 on 1433 degrees of freedom
## Multiple R-squared: 0.155, Adjusted R-squared: 0.155
## F-statistic: 263 on 1 and 1433 DF, p-value: <2e-16
## Setting up data
pdata = plm.data(All, index = c("County.Area", "t"))
## Assuming same intercepts, no time effects, with lags
m1i = plm(TotalSTD ~ TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD, 2) + lag(TotalSTD,
3), data = pdata)
summary(m1i) #sig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + lag(TotalSTD, 3), data = pdata)
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.0600 -0.5010 -0.0966 0.4210 3.5400
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## TMEANlevelsFreezing -0.3527 0.2167 -1.63 0.1039
## TMEANlevelsCold -0.0548 0.1022 -0.54 0.5921
## TMEANlevelsWarm 0.1115 0.1015 1.10 0.2720
## TMEANlevelsHot -0.5805 0.2103 -2.76 0.0059 **
## lag(TotalSTD, 1) 0.3117 0.0260 11.98 <2e-16 ***
## lag(TotalSTD, 2) 0.0396 0.0273 1.45 0.1471
## lag(TotalSTD, 3) 0.2611 0.0259 10.08 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 940
## R-Squared : 0.197
## Adj. R-Squared : 0.194
## F-statistic: 64.184 on 7 and 1373 DF, p-value: <2e-16
SCtest(m1i) # no sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.105 -0.506 -0.106 0.411 3.555
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.14e-05 2.20e-02 0.00 1.00
## res[-1] 3.00e-02 2.68e-02 1.12 0.26
##
## Residual standard error: 0.821 on 1392 degrees of freedom
## Multiple R-squared: 0.0009, Adjusted R-squared: 0.000182
## F-statistic: 1.25 on 1 and 1392 DF, p-value: 0.263
xyplot(m1i$residuals ~ fitted(m1i)) # not terrible
qqPlot(m1i$residuals) # not great
m2i = plm(TotalSTD ~ PRECIPlevels + lag(TotalSTD, 1) + lag(TotalSTD, 2) + lag(TotalSTD,
3), data = pdata)
summary(m2i) #sig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + lag(TotalSTD, 3), data = pdata)
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.0900 -0.4800 -0.0975 0.4140 3.5200
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -0.4188 0.2185 -1.92 0.0555 .
## PRECIPlevelsDry -0.3185 0.1024 -3.11 0.0019 **
## PRECIPlevelsWet -0.0956 0.1049 -0.91 0.3623
## PRECIPlevelsSoaked -0.2894 0.2170 -1.33 0.1825
## lag(TotalSTD, 1) 0.3074 0.0259 11.86 <2e-16 ***
## lag(TotalSTD, 2) 0.0531 0.0272 1.95 0.0509 .
## lag(TotalSTD, 3) 0.2577 0.0258 10.00 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 938
## R-Squared : 0.198
## Adj. R-Squared : 0.195
## F-statistic: 64.7304 on 7 and 1373 DF, p-value: <2e-16
SCtest(m2i) # no sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.122 -0.489 -0.098 0.405 3.537
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.46e-05 2.20e-02 0.00 1.00
## res[-1] 2.04e-02 2.68e-02 0.76 0.45
##
## Residual standard error: 0.821 on 1392 degrees of freedom
## Multiple R-squared: 0.000415, Adjusted R-squared: -0.000303
## F-statistic: 0.578 on 1 and 1392 DF, p-value: 0.447
xyplot(m2i$residuals ~ fitted(m2i)) # not bad
qqPlot(m2i$residuals) # not great
## With quadratic time trends and County FE
m1j = plm(TotalSTD ~ TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD, 2) + as.numeric(t) +
t2, data = pdata, effect = "individual", model = "within")
summary(m1j) #sig!
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + as.numeric(t) + t2, data = pdata, effect = "individual",
## model = "within")
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9300 -0.5150 -0.0956 0.4490 3.2200
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## TMEANlevelsFreezing -0.384375 0.221902 -1.73 0.083 .
## TMEANlevelsCold -0.081522 0.103913 -0.78 0.433
## TMEANlevelsWarm 0.150719 0.103774 1.45 0.147
## TMEANlevelsHot -0.440112 0.216112 -2.04 0.042 *
## lag(TotalSTD, 1) 0.331735 0.026546 12.50 < 2e-16 ***
## lag(TotalSTD, 2) 0.111190 0.026481 4.20 2.9e-05 ***
## as.numeric(t) -0.018233 0.003869 -4.71 2.7e-06 ***
## t2 0.000137 0.000035 3.91 9.7e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1270
## Residual Sum of Squares: 993
## R-Squared : 0.17
## Adj. R-Squared : 0.168
## F-statistic: 47.6381 on 8 and 1387 DF, p-value: <2e-16
SCtest(m1j) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.878 -0.521 -0.084 0.451 3.170
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.00022 0.02236 0.01 0.99
## res[-1] -0.03764 0.02664 -1.41 0.16
##
## Residual standard error: 0.839 on 1407 degrees of freedom
## Multiple R-squared: 0.00142, Adjusted R-squared: 0.000707
## F-statistic: 2 on 1 and 1407 DF, p-value: 0.158
xyplot(m1j$residuals ~ fitted(m1j)) # not bad
qqPlot(m1j$residuals) # not great
m2j = plm(TotalSTD ~ PRECIPlevels + lag(TotalSTD, 1) + lag(TotalSTD, 2) + lag(TotalSTD,
3) + as.numeric(t) + t2, data = pdata, effect = "individual", model = "within")
summary(m2j) #sig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels + lag(TotalSTD, 1) + lag(TotalSTD,
## 2) + lag(TotalSTD, 3) + as.numeric(t) + t2, data = pdata,
## effect = "individual", model = "within")
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9800 -0.4780 -0.0833 0.4010 3.2700
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -3.78e-01 2.17e-01 -1.74 0.08253 .
## PRECIPlevelsDry -3.21e-01 1.02e-01 -3.15 0.00167 **
## PRECIPlevelsWet -9.31e-02 1.04e-01 -0.89 0.37239
## PRECIPlevelsSoaked -3.00e-01 2.16e-01 -1.39 0.16458
## lag(TotalSTD, 1) 2.90e-01 2.60e-02 11.15 < 2e-16 ***
## lag(TotalSTD, 2) 4.02e-02 2.71e-02 1.48 0.13842
## lag(TotalSTD, 3) 2.40e-01 2.59e-02 9.27 < 2e-16 ***
## as.numeric(t) -1.51e-02 3.89e-03 -3.88 0.00011 ***
## t2 1.14e-04 3.48e-05 3.27 0.00111 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 924
## R-Squared : 0.209
## Adj. R-Squared : 0.206
## F-statistic: 53.3225 on 9 and 1371 DF, p-value: <2e-16
SCtest(m2j) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.015 -0.481 -0.080 0.404 3.281
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000114 0.021817 0.01 1.0
## res[-1] 0.018204 0.026798 0.68 0.5
##
## Residual standard error: 0.815 on 1392 degrees of freedom
## Multiple R-squared: 0.000331, Adjusted R-squared: -0.000387
## F-statistic: 0.461 on 1 and 1392 DF, p-value: 0.497
xyplot(m2j$residuals ~ fitted(m2j)) # not bad
qqPlot(m2j$residuals) # bad
## With month*year and County FE
m1k = plm(TotalSTD ~ TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD, 2), data = pdata,
model = "within", effect = "twoways")
summary(m1k) #nonsig
## Twoways effects Within Model
##
## Call:
## plm(formula = TotalSTD ~ TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
## 2), data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9600 -0.3710 -0.0238 0.3620 3.1600
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## TMEANlevelsFreezing 0.1831 0.2309 0.79 0.43
## TMEANlevelsCold -0.0551 0.0958 -0.58 0.57
## TMEANlevelsWarm 0.0157 0.0996 0.16 0.87
## TMEANlevelsHot -0.2905 0.1930 -1.50 0.13
## lag(TotalSTD, 1) 0.5056 0.0274 18.43 < 2e-16 ***
## lag(TotalSTD, 2) 0.1137 0.0275 4.14 3.7e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 805
## Residual Sum of Squares: 527
## R-Squared : 0.118
## Adj. R-Squared : 0.109
## F-statistic: 113.963 on 6 and 1296 DF, p-value: <2e-16
SCtest(m1k) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9206 -0.3698 -0.0207 0.3612 3.1089
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000179 0.016296 0.01 0.99
## res[-1] -0.018690 0.026667 -0.70 0.48
##
## Residual standard error: 0.612 on 1407 degrees of freedom
## Multiple R-squared: 0.000349, Adjusted R-squared: -0.000361
## F-statistic: 0.491 on 1 and 1407 DF, p-value: 0.483
m2k = plm(TotalSTD ~ PRECIPlevels + lag(TotalSTD, 1) + lag(TotalSTD, 2), data = pdata,
model = "within", effect = "twoways")
summary(m2k) #sig!
## Twoways effects Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels + lag(TotalSTD, 1) + lag(TotalSTD,
## 2), data = pdata, effect = "twoways", model = "within")
##
## Balanced Panel: n=15, T=94, N=1410
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.9800 -0.3550 -0.0203 0.3630 3.1800
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -0.4060 0.1767 -2.30 0.02173 *
## PRECIPlevelsDry -0.3279 0.0847 -3.87 0.00011 ***
## PRECIPlevelsWet -0.1273 0.0848 -1.50 0.13338
## PRECIPlevelsSoaked -0.1111 0.1696 -0.66 0.51234
## lag(TotalSTD, 1) 0.4979 0.0274 18.20 < 2e-16 ***
## lag(TotalSTD, 2) 0.1196 0.0272 4.39 1.2e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 805
## Residual Sum of Squares: 520
## R-Squared : 0.123
## Adj. R-Squared : 0.113
## F-statistic: 118.395 on 6 and 1296 DF, p-value: <2e-16
SCtest(m2k) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9330 -0.3610 -0.0193 0.3728 3.1009
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000183 0.016184 0.01 0.99
## res[-1] -0.026655 0.026661 -1.00 0.32
##
## Residual standard error: 0.608 on 1407 degrees of freedom
## Multiple R-squared: 0.00071, Adjusted R-squared: -3.2e-07
## F-statistic: 1 on 1 and 1407 DF, p-value: 0.318
xyplot(m2k$residuals ~ fitted(m2k)) # not bad
qqPlot(m2k$residuals) # not bad
m3k = plm(TotalSTD ~ PRECIPlevels + TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
2) + lag(TotalSTD, 3) + factor(t) + County.Area, data = pdata)
summary(m3k) # sig!
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels + TMEANlevels + lag(TotalSTD,
## 1) + lag(TotalSTD, 2) + lag(TotalSTD, 3) + factor(t) + County.Area,
## data = pdata)
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.0000 -0.3510 -0.0197 0.3540 3.1800
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -0.45187 0.17667 -2.56 0.01065 *
## PRECIPlevelsDry -0.35976 0.08539 -4.21 2.7e-05 ***
## PRECIPlevelsWet -0.13430 0.08656 -1.55 0.12103
## PRECIPlevelsSoaked -0.10836 0.17484 -0.62 0.53553
## TMEANlevelsFreezing 0.17014 0.22914 0.74 0.45792
## TMEANlevelsCold -0.06191 0.09598 -0.64 0.51907
## TMEANlevelsWarm 0.01719 0.09872 0.17 0.86179
## TMEANlevelsHot -0.35851 0.19236 -1.86 0.06258 .
## lag(TotalSTD, 1) 0.47567 0.02778 17.12 < 2e-16 ***
## lag(TotalSTD, 2) 0.06622 0.03080 2.15 0.03177 *
## lag(TotalSTD, 3) 0.10068 0.02770 3.63 0.00029 ***
## factor(t)9 0.43747 0.23169 1.89 0.05922 .
## factor(t)10 0.33785 0.23142 1.46 0.14457
## factor(t)11 1.36380 0.23159 5.89 5.0e-09 ***
## factor(t)12 -0.86549 0.23376 -3.70 0.00022 ***
## factor(t)13 0.05549 0.23640 0.23 0.81445
## factor(t)14 1.19922 0.23572 5.09 4.2e-07 ***
## factor(t)15 0.28414 0.23458 1.21 0.22601
## factor(t)16 0.63807 0.23780 2.68 0.00739 **
## factor(t)17 0.44348 0.23146 1.92 0.05559 .
## factor(t)18 0.01224 0.23181 0.05 0.95791
## factor(t)19 0.36565 0.23208 1.58 0.11539
## factor(t)20 0.14594 0.23182 0.63 0.52913
## factor(t)21 -0.16077 0.23472 -0.68 0.49350
## factor(t)22 0.07234 0.23151 0.31 0.75473
## factor(t)23 2.01393 0.23472 8.58 < 2e-16 ***
## factor(t)24 -0.90113 0.24690 -3.65 0.00027 ***
## factor(t)25 -0.16949 0.23938 -0.71 0.47906
## factor(t)26 0.78053 0.23621 3.30 0.00098 ***
## factor(t)27 0.15819 0.23350 0.68 0.49823
## factor(t)28 0.26325 0.23390 1.13 0.26060
## factor(t)29 1.02863 0.23238 4.43 1.0e-05 ***
## factor(t)30 0.28599 0.23343 1.23 0.22073
## factor(t)31 0.45908 0.23303 1.97 0.04905 *
## factor(t)32 0.09834 0.23154 0.42 0.67112
## factor(t)33 0.12863 0.23124 0.56 0.57812
## factor(t)34 -0.15625 0.23846 -0.66 0.51243
## factor(t)35 1.46363 0.24028 6.09 1.5e-09 ***
## factor(t)36 -0.63280 0.23484 -2.69 0.00714 **
## factor(t)37 -0.19053 0.23774 -0.80 0.42303
## factor(t)38 0.70878 0.23357 3.03 0.00246 **
## factor(t)39 0.02421 0.23365 0.10 0.91751
## factor(t)40 0.39594 0.23303 1.70 0.08954 .
## factor(t)41 0.78983 0.23163 3.41 0.00067 ***
## factor(t)42 0.17023 0.23444 0.73 0.46790
## factor(t)43 0.62446 0.23238 2.69 0.00730 **
## factor(t)44 -0.16386 0.23373 -0.70 0.48338
## factor(t)45 0.15294 0.23208 0.66 0.51002
## factor(t)46 -0.00156 0.23705 -0.01 0.99474
## factor(t)47 1.23485 0.23346 5.29 1.4e-07 ***
## factor(t)48 -0.72884 0.23437 -3.11 0.00191 **
## factor(t)49 -0.35399 0.23530 -1.50 0.13272
## factor(t)50 0.54198 0.23376 2.32 0.02058 *
## factor(t)51 0.11073 0.23318 0.47 0.63495
## factor(t)52 0.43810 0.23512 1.86 0.06265 .
## factor(t)53 0.74169 0.23178 3.20 0.00141 **
## factor(t)54 0.12878 0.23410 0.55 0.58236
## factor(t)55 0.28107 0.23303 1.21 0.22798
## factor(t)56 -0.05608 0.23305 -0.24 0.80986
## factor(t)57 -0.25522 0.23263 -1.10 0.27280
## factor(t)58 -0.49827 0.23181 -2.15 0.03178 *
## factor(t)59 1.20311 0.23275 5.17 2.7e-07 ***
## factor(t)60 -1.08532 0.23718 -4.58 5.2e-06 ***
## factor(t)61 -0.38853 0.23784 -1.63 0.10259
## factor(t)62 0.12567 0.23554 0.53 0.59376
## factor(t)63 -0.09812 0.23603 -0.42 0.67768
## factor(t)64 0.09998 0.23482 0.43 0.67033
## factor(t)65 0.45858 0.23389 1.96 0.05013 .
## factor(t)66 0.28682 0.23760 1.21 0.22760
## factor(t)67 0.29936 0.23290 1.29 0.19891
## factor(t)68 -0.01781 0.23185 -0.08 0.93877
## factor(t)69 -0.29882 0.23197 -1.29 0.19791
## factor(t)70 -0.29194 0.23247 -1.26 0.20941
## factor(t)71 1.06875 0.24060 4.44 9.7e-06 ***
## factor(t)72 -1.10950 0.23987 -4.63 4.1e-06 ***
## factor(t)73 -0.21735 0.24019 -0.90 0.36568
## factor(t)74 0.54687 0.23727 2.30 0.02134 *
## factor(t)75 0.04309 0.23540 0.18 0.85479
## factor(t)76 0.09468 0.23530 0.40 0.68748
## factor(t)77 0.60918 0.23238 2.62 0.00886 **
## factor(t)78 -0.01337 0.23311 -0.06 0.95429
## factor(t)79 0.30717 0.23333 1.32 0.18826
## factor(t)80 0.03005 0.23189 0.13 0.89693
## factor(t)81 -0.11861 0.23184 -0.51 0.60901
## factor(t)82 -0.28538 0.26821 -1.06 0.28752
## factor(t)83 1.55077 0.23420 6.62 5.2e-11 ***
## factor(t)84 -1.15969 0.23587 -4.92 9.9e-07 ***
## factor(t)85 -0.15274 0.23839 -0.64 0.52182
## factor(t)86 0.65533 0.23565 2.78 0.00550 **
## factor(t)87 0.06248 0.23465 0.27 0.79008
## factor(t)88 0.15251 0.23397 0.65 0.51462
## factor(t)89 0.74056 0.23147 3.20 0.00141 **
## factor(t)90 0.21359 0.23326 0.92 0.36002
## factor(t)91 0.27171 0.23229 1.17 0.24235
## factor(t)92 0.21425 0.23156 0.93 0.35502
## factor(t)93 -0.14224 0.23132 -0.61 0.53872
## factor(t)94 0.10611 0.23280 0.46 0.64862
## factor(t)95 1.33306 0.23151 5.76 1.1e-08 ***
## factor(t)96 -1.02785 0.23513 -4.37 1.3e-05 ***
## factor(t)97 0.26529 0.25136 1.06 0.29143
## factor(t)98 0.81186 0.23927 3.39 0.00071 ***
## factor(t)99 0.05008 0.23419 0.21 0.83069
## factor(t)100 0.29305 0.23296 1.26 0.20863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 509
## R-Squared : 0.536
## Adj. R-Squared : 0.491
## F-statistic: 17.9954 on 103 and 1277 DF, p-value: <2e-16
SCtest(m3k) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.008 -0.350 -0.020 0.353 3.189
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000175 0.016192 0.01 0.99
## res[-1] 0.002126 0.026802 0.08 0.94
##
## Residual standard error: 0.605 on 1392 degrees of freedom
## Multiple R-squared: 4.52e-06, Adjusted R-squared: -0.000714
## F-statistic: 0.00629 on 1 and 1392 DF, p-value: 0.937
xyplot(m3k$residuals ~ fitted(m3k)) # not bad
qqPlot(m3k$residuals) # not bad
plot(m3k$residuals ~ m3k$model[[7]])
plot(m3k$residuals ~ m3k$model[[8]])
plot(m3k$residuals[which(m3k$model[[8]] == "Carteret")] ~ m3k$model[[7]][which(m3k$model[[8]] ==
"Carteret")]) # Res vs time
plot(m3k$residuals[which(m3k$model[[8]] == "Camden")] ~ m3k$model[[7]][which(m3k$model[[8]] ==
"Camden")]) # Res vs time
# Removing outliers
findoutliers = plm(TotalSTD ~ PRECIPlevels + TMEANlevels + factor(t) + County.Area,
data = pdata)
outliers = which(findoutliers$residuals > 1)
pdata2 = pdata[-outliers, ]
# New reg without outliers
m3l = plm(TotalSTD ~ PRECIPlevels + TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
2) + lag(TotalSTD, 3) + factor(t) + County.Area, data = pdata2)
summary(m3l) # still sig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels + TMEANlevels + lag(TotalSTD,
## 1) + lag(TotalSTD, 2) + lag(TotalSTD, 3) + factor(t) + County.Area,
## data = pdata2)
##
## Unbalanced Panel: n=15, T=50-82, N=1054
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.4800 -0.2950 0.0116 0.3230 1.6000
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -0.32767 0.15821 -2.07 0.03862 *
## PRECIPlevelsDry -0.34303 0.08235 -4.17 3.4e-05 ***
## PRECIPlevelsWet -0.07918 0.08367 -0.95 0.34425
## PRECIPlevelsSoaked 0.04579 0.16175 0.28 0.77719
## TMEANlevelsFreezing 0.20848 0.20558 1.01 0.31081
## TMEANlevelsCold -0.03696 0.09384 -0.39 0.69375
## TMEANlevelsWarm 0.01008 0.09426 0.11 0.91484
## TMEANlevelsHot -0.14670 0.17649 -0.83 0.40608
## lag(TotalSTD, 1) 0.37088 0.03288 11.28 < 2e-16 ***
## lag(TotalSTD, 2) 0.11945 0.03499 3.41 0.00067 ***
## lag(TotalSTD, 3) 0.06174 0.03193 1.93 0.05344 .
## factor(t)9 0.51234 0.22228 2.30 0.02139 *
## factor(t)10 0.14207 0.22715 0.63 0.53182
## factor(t)11 1.42019 0.22171 6.41 2.4e-10 ***
## factor(t)12 -0.80720 0.23235 -3.47 0.00054 ***
## factor(t)13 -0.12431 0.23454 -0.53 0.59623
## factor(t)14 0.91472 0.23923 3.82 0.00014 ***
## factor(t)15 0.21090 0.23160 0.91 0.36273
## factor(t)16 0.52448 0.23780 2.21 0.02766 *
## factor(t)17 0.96896 0.24191 4.01 6.7e-05 ***
## factor(t)18 0.15793 0.25436 0.62 0.53484
## factor(t)19 0.61792 0.25394 2.43 0.01515 *
## factor(t)20 0.19140 0.22759 0.84 0.40057
## factor(t)21 -0.05300 0.22401 -0.24 0.81302
## factor(t)22 0.04398 0.22155 0.20 0.84268
## factor(t)23 1.67839 0.24409 6.88 1.1e-11 ***
## factor(t)24 -0.76114 0.25517 -2.98 0.00293 **
## factor(t)25 -0.33928 0.24389 -1.39 0.16453
## factor(t)26 0.55175 0.23134 2.39 0.01728 *
## factor(t)27 0.28571 0.22463 1.27 0.20373
## factor(t)28 0.15088 0.22596 0.67 0.50446
## factor(t)29 1.08184 0.22870 4.73 2.6e-06 ***
## factor(t)30 0.39174 0.23007 1.70 0.08896 .
## factor(t)31 0.50361 0.23582 2.14 0.03298 *
## factor(t)32 0.26703 0.22304 1.20 0.23151
## factor(t)33 0.22765 0.21713 1.05 0.29470
## factor(t)34 0.00891 0.22053 0.04 0.96777
## factor(t)35 1.00533 0.23655 4.25 2.4e-05 ***
## factor(t)36 -0.14434 0.23007 -0.63 0.53055
## factor(t)37 -0.16216 0.23221 -0.70 0.48514
## factor(t)38 0.53607 0.22719 2.36 0.01850 *
## factor(t)39 0.19713 0.21945 0.90 0.36925
## factor(t)40 0.46026 0.21910 2.10 0.03594 *
## factor(t)41 0.84715 0.22170 3.82 0.00014 ***
## factor(t)42 0.25597 0.21777 1.18 0.24013
## factor(t)43 0.63063 0.21922 2.88 0.00411 **
## factor(t)44 0.01558 0.21939 0.07 0.94341
## factor(t)45 0.35496 0.21824 1.63 0.10418
## factor(t)46 0.26770 0.22325 1.20 0.23079
## factor(t)47 1.23838 0.22275 5.56 3.5e-08 ***
## factor(t)48 -0.41126 0.21765 -1.89 0.05913 .
## factor(t)49 -0.33012 0.21756 -1.52 0.12951
## factor(t)50 0.51499 0.22279 2.31 0.02102 *
## factor(t)51 0.15283 0.21867 0.70 0.48478
## factor(t)52 0.66429 0.22156 3.00 0.00279 **
## factor(t)53 0.78306 0.22751 3.44 0.00060 ***
## factor(t)54 0.04922 0.22567 0.22 0.82738
## factor(t)55 0.19168 0.22366 0.86 0.39165
## factor(t)56 -0.03444 0.22161 -0.16 0.87654
## factor(t)57 -0.14148 0.22380 -0.63 0.52742
## factor(t)58 -0.37602 0.21686 -1.73 0.08326 .
## factor(t)59 1.30240 0.21347 6.10 1.5e-09 ***
## factor(t)60 -0.89219 0.21329 -4.18 3.1e-05 ***
## factor(t)61 -0.55193 0.21801 -2.53 0.01152 *
## factor(t)62 0.25461 0.21308 1.19 0.23244
## factor(t)63 -0.03807 0.21487 -0.18 0.85940
## factor(t)64 0.16234 0.21404 0.76 0.44837
## factor(t)65 0.39727 0.21146 1.88 0.06059 .
## factor(t)66 0.08878 0.22365 0.40 0.69147
## factor(t)67 0.17999 0.22357 0.81 0.42096
## factor(t)68 -0.10829 0.22807 -0.47 0.63505
## factor(t)69 -0.28219 0.22278 -1.27 0.20558
## factor(t)70 -0.37797 0.22250 -1.70 0.08970 .
## factor(t)71 1.40807 0.22589 6.23 6.9e-10 ***
## factor(t)72 -1.15038 0.22554 -5.10 4.1e-07 ***
## factor(t)73 -0.36223 0.23192 -1.56 0.11865
## factor(t)74 0.61982 0.21945 2.82 0.00484 **
## factor(t)75 0.07848 0.21899 0.36 0.72015
## factor(t)76 0.11043 0.21419 0.52 0.60629
## factor(t)77 0.57209 0.20976 2.73 0.00651 **
## factor(t)78 -0.27503 0.22296 -1.23 0.21768
## factor(t)79 0.18686 0.22414 0.83 0.40468
## factor(t)80 0.08270 0.22273 0.37 0.71048
## factor(t)81 -0.11220 0.21778 -0.52 0.60654
## factor(t)82 -0.23052 0.25650 -0.90 0.36904
## factor(t)83 1.79109 0.21836 8.20 7.7e-16 ***
## factor(t)84 -1.04781 0.22043 -4.75 2.3e-06 ***
## factor(t)85 -0.22361 0.21944 -1.02 0.30846
## factor(t)86 0.65586 0.22139 2.96 0.00313 **
## factor(t)87 0.11289 0.22191 0.51 0.61105
## factor(t)88 0.25063 0.21660 1.16 0.24751
## factor(t)89 0.77796 0.21678 3.59 0.00035 ***
## factor(t)90 0.10266 0.22378 0.46 0.64651
## factor(t)91 0.23799 0.22234 1.07 0.28472
## factor(t)92 0.24816 0.22190 1.12 0.26370
## factor(t)93 0.08436 0.22191 0.38 0.70392
## factor(t)94 0.24286 0.22298 1.09 0.27636
## factor(t)95 1.40164 0.21673 6.47 1.6e-10 ***
## factor(t)96 -0.92529 0.21974 -4.21 2.8e-05 ***
## factor(t)97 0.09653 0.24009 0.40 0.68773
## factor(t)98 0.73805 0.23278 3.17 0.00157 **
## factor(t)99 -0.00962 0.23124 -0.04 0.96684
## factor(t)100 0.41086 0.22349 1.84 0.06632 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 627
## Residual Sum of Squares: 249
## R-Squared : 0.493
## Adj. R-Squared : 0.438
## F-statistic: 13.8308 on 103 and 936 DF, p-value: <2e-16
SCtest(m3l)
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4605 -0.2960 0.0095 0.3255 1.6062
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000349 0.014988 0.02 0.98
## res[-1] -0.014330 0.030958 -0.46 0.64
##
## Residual standard error: 0.486 on 1051 degrees of freedom
## Multiple R-squared: 0.000204, Adjusted R-squared: -0.000747
## F-statistic: 0.214 on 1 and 1051 DF, p-value: 0.644
xyplot(m3l$residuals ~ fitted(m3l)) # looks ok
qqPlot(m3l$residuals) #looks OK
plot(m3l$residuals ~ m3l$model[[7]])
plot(m3l$residuals ~ m3l$model[[8]])
# New reg without outliers or Camden
pdata3 = pdata2[which(pdata2$County.Area != "Camden"), ]
m3m = plm(TotalSTD ~ PRECIPlevels + TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
2) + lag(TotalSTD, 3) + factor(t) + County.Area, data = pdata3)
summary(m3m) # still sig
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels + TMEANlevels + lag(TotalSTD,
## 1) + lag(TotalSTD, 2) + lag(TotalSTD, 3) + factor(t) + County.Area,
## data = pdata3)
##
## Unbalanced Panel: n=14, T=52-82, N=1004
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.63000 -0.27900 0.00999 0.32100 1.65000
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -0.30748 0.15600 -1.97 0.04903 *
## PRECIPlevelsDry -0.31151 0.08347 -3.73 0.00020 ***
## PRECIPlevelsWet -0.05200 0.08448 -0.62 0.53835
## PRECIPlevelsSoaked -0.12987 0.17394 -0.75 0.45547
## TMEANlevelsFreezing 0.20852 0.20169 1.03 0.30148
## TMEANlevelsCold -0.02851 0.09370 -0.30 0.76103
## TMEANlevelsWarm 0.00235 0.09465 0.02 0.98023
## TMEANlevelsHot -0.14581 0.17318 -0.84 0.40006
## lag(TotalSTD, 1) 0.39402 0.03365 11.71 < 2e-16 ***
## lag(TotalSTD, 2) 0.10101 0.03638 2.78 0.00560 **
## lag(TotalSTD, 3) 0.05815 0.03291 1.77 0.07759 .
## factor(t)9 0.33054 0.22879 1.44 0.14889
## factor(t)10 0.08699 0.22760 0.38 0.70242
## factor(t)11 1.37457 0.22250 6.18 9.9e-10 ***
## factor(t)12 -0.88843 0.23296 -3.81 0.00015 ***
## factor(t)13 -0.14189 0.23611 -0.60 0.54802
## factor(t)14 0.86614 0.23934 3.62 0.00031 ***
## factor(t)15 0.13640 0.23202 0.59 0.55676
## factor(t)16 0.46617 0.23877 1.95 0.05121 .
## factor(t)17 0.91003 0.24186 3.76 0.00018 ***
## factor(t)18 0.11271 0.25403 0.44 0.65737
## factor(t)19 0.56880 0.25385 2.24 0.02530 *
## factor(t)20 0.13294 0.23504 0.57 0.57180
## factor(t)21 -0.04165 0.23114 -0.18 0.85703
## factor(t)22 -0.00093 0.22239 0.00 0.99666
## factor(t)23 1.64139 0.24394 6.73 3.1e-11 ***
## factor(t)24 -0.84970 0.25517 -3.33 0.00090 ***
## factor(t)25 -0.34816 0.24553 -1.42 0.15653
## factor(t)26 0.54831 0.23784 2.31 0.02138 *
## factor(t)27 0.30275 0.23089 1.31 0.19013
## factor(t)28 0.10888 0.23168 0.47 0.63850
## factor(t)29 1.07845 0.23504 4.59 5.1e-06 ***
## factor(t)30 0.37597 0.23727 1.58 0.11342
## factor(t)31 0.43039 0.24429 1.76 0.07845 .
## factor(t)32 0.21632 0.22958 0.94 0.34632
## factor(t)33 0.21405 0.22273 0.96 0.33679
## factor(t)34 -0.05479 0.22491 -0.24 0.80760
## factor(t)35 1.05683 0.24275 4.35 1.5e-05 ***
## factor(t)36 -0.22326 0.23682 -0.94 0.34607
## factor(t)37 -0.19793 0.24045 -0.82 0.41064
## factor(t)38 0.50723 0.23416 2.17 0.03056 *
## factor(t)39 0.12577 0.22505 0.56 0.57640
## factor(t)40 0.48888 0.22478 2.17 0.02990 *
## factor(t)41 0.85914 0.22796 3.77 0.00017 ***
## factor(t)42 0.21092 0.22389 0.94 0.34641
## factor(t)43 0.63282 0.22453 2.82 0.00493 **
## factor(t)44 -0.13448 0.22451 -0.60 0.54933
## factor(t)45 0.34982 0.22371 1.56 0.11824
## factor(t)46 0.28435 0.22797 1.25 0.21260
## factor(t)47 1.09215 0.22951 4.76 2.3e-06 ***
## factor(t)48 -0.47591 0.22175 -2.15 0.03213 *
## factor(t)49 -0.37509 0.22268 -1.68 0.09245 .
## factor(t)50 0.51892 0.22857 2.27 0.02343 *
## factor(t)51 0.08882 0.21948 0.40 0.68581
## factor(t)52 0.60817 0.22282 2.73 0.00647 **
## factor(t)53 0.72560 0.22802 3.18 0.00151 **
## factor(t)54 0.01756 0.22669 0.08 0.93827
## factor(t)55 0.16068 0.23028 0.70 0.48551
## factor(t)56 -0.10719 0.22778 -0.47 0.63807
## factor(t)57 -0.21111 0.23028 -0.92 0.35951
## factor(t)58 -0.44655 0.22227 -2.01 0.04484 *
## factor(t)59 1.29203 0.21848 5.91 4.8e-09 ***
## factor(t)60 -0.99107 0.21804 -4.55 6.2e-06 ***
## factor(t)61 -0.56757 0.22038 -2.58 0.01017 *
## factor(t)62 0.21353 0.21428 1.00 0.31928
## factor(t)63 -0.09365 0.21586 -0.43 0.66451
## factor(t)64 0.10898 0.21549 0.51 0.61317
## factor(t)65 0.28723 0.21615 1.33 0.18423
## factor(t)66 -0.01953 0.22957 -0.09 0.93222
## factor(t)67 0.08298 0.22954 0.36 0.71780
## factor(t)68 -0.16885 0.22846 -0.74 0.46006
## factor(t)69 -0.32932 0.22352 -1.47 0.14101
## factor(t)70 -0.42339 0.22317 -1.90 0.05813 .
## factor(t)71 1.35736 0.22670 5.99 3.1e-09 ***
## factor(t)72 -1.23695 0.22614 -5.47 5.9e-08 ***
## factor(t)73 -0.37987 0.23407 -1.62 0.10497
## factor(t)74 0.57121 0.22029 2.59 0.00967 **
## factor(t)75 0.00383 0.22002 0.02 0.98613
## factor(t)76 0.06182 0.21578 0.29 0.77456
## factor(t)77 0.47742 0.21460 2.22 0.02636 *
## factor(t)78 -0.07065 0.22911 -0.31 0.75787
## factor(t)79 -0.02834 0.22977 -0.12 0.90187
## factor(t)80 -0.01753 0.22881 -0.08 0.93893
## factor(t)81 -0.23240 0.22259 -1.04 0.29674
## factor(t)82 -0.27511 0.25505 -1.08 0.28104
## factor(t)83 1.73805 0.21918 7.93 6.6e-15 ***
## factor(t)84 -1.13542 0.22163 -5.12 3.7e-07 ***
## factor(t)85 -0.22045 0.22214 -0.99 0.32128
## factor(t)86 0.60980 0.22234 2.74 0.00622 **
## factor(t)87 0.04133 0.22278 0.19 0.85287
## factor(t)88 0.18437 0.22223 0.83 0.40698
## factor(t)89 0.74277 0.22237 3.34 0.00087 ***
## factor(t)90 0.05883 0.22936 0.26 0.79763
## factor(t)91 0.18471 0.22873 0.81 0.41955
## factor(t)92 0.15950 0.22813 0.70 0.48463
## factor(t)93 -0.04096 0.22798 -0.18 0.85744
## factor(t)94 0.20061 0.22376 0.90 0.37020
## factor(t)95 1.35311 0.21768 6.22 7.8e-10 ***
## factor(t)96 -1.00230 0.22103 -4.53 6.6e-06 ***
## factor(t)97 0.09063 0.24196 0.37 0.70806
## factor(t)98 0.68851 0.23310 2.95 0.00322 **
## factor(t)99 -0.07455 0.23174 -0.32 0.74775
## factor(t)100 0.36409 0.23003 1.58 0.11382
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 602
## Residual Sum of Squares: 226
## R-Squared : 0.513
## Adj. R-Squared : 0.454
## F-statistic: 14.339 on 103 and 887 DF, p-value: <2e-16
SCtest(m3m)
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.6199 -0.2823 0.0082 0.3253 1.6551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000356 0.014997 0.02 0.98
## res[-1] -0.017896 0.031728 -0.56 0.57
##
## Residual standard error: 0.475 on 1001 degrees of freedom
## Multiple R-squared: 0.000318, Adjusted R-squared: -0.000681
## F-statistic: 0.318 on 1 and 1001 DF, p-value: 0.573
xyplot(m3m$residuals ~ fitted(m3m)) # looks ok
qqPlot(m3m$residuals) #looks OK
plot(m3m$residuals ~ m3m$model[[7]])
plot(m3m$residuals ~ m3m$model[[8]])
# Reg with outliers and interaction terms
m3n = plm(TotalSTD ~ PRECIPlevels * TMEANlevels + lag(TotalSTD, 1) + lag(TotalSTD,
2) + lag(TotalSTD, 3) + factor(t) + County.Area, data = pdata)
summary(m3n) # sig!
## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = TotalSTD ~ PRECIPlevels * TMEANlevels + lag(TotalSTD,
## 1) + lag(TotalSTD, 2) + lag(TotalSTD, 3) + factor(t) + County.Area,
## data = pdata)
##
## Balanced Panel: n=15, T=93, N=1395
##
## Residuals :
## Min. 1st Qu. Median 3rd Qu. Max.
## -3.0000 -0.3530 -0.0206 0.3560 3.1800
##
## Coefficients :
## Estimate Std. Error t-value Pr(>|t|)
## PRECIPlevelsDrought -0.4579 0.1769 -2.59 0.00974
## PRECIPlevelsDry -0.3421 0.0859 -3.98 7.2e-05
## PRECIPlevelsWet -0.1481 0.0932 -1.59 0.11246
## PRECIPlevelsSoaked -0.1077 0.1749 -0.62 0.53833
## TMEANlevelsFreezing 0.1396 0.2436 0.57 0.56672
## TMEANlevelsCold -0.0719 0.0994 -0.72 0.46937
## TMEANlevelsWarm 0.0474 0.1014 0.47 0.64046
## TMEANlevelsHot -0.3561 0.1926 -1.85 0.06476
## lag(TotalSTD, 1) 0.4753 0.0278 17.08 < 2e-16
## lag(TotalSTD, 2) 0.0629 0.0309 2.03 0.04221
## lag(TotalSTD, 3) 0.1039 0.0279 3.73 0.00020
## factor(t)9 0.4330 0.2317 1.87 0.06192
## factor(t)10 0.3376 0.2315 1.46 0.14499
## factor(t)11 1.3643 0.2317 5.89 5.0e-09
## factor(t)12 -0.8644 0.2339 -3.70 0.00023
## factor(t)13 0.0535 0.2369 0.23 0.82143
## factor(t)14 1.1968 0.2360 5.07 4.5e-07
## factor(t)15 0.2856 0.2347 1.22 0.22380
## factor(t)16 0.6412 0.2379 2.70 0.00712
## factor(t)17 0.4417 0.2315 1.91 0.05663
## factor(t)18 0.0125 0.2319 0.05 0.95692
## factor(t)19 0.3613 0.2322 1.56 0.11991
## factor(t)20 0.1461 0.2319 0.63 0.52886
## factor(t)21 -0.1581 0.2349 -0.67 0.50089
## factor(t)22 0.0732 0.2316 0.32 0.75195
## factor(t)23 2.0001 0.2350 8.51 < 2e-16
## factor(t)24 -0.9015 0.2470 -3.65 0.00027
## factor(t)25 -0.1646 0.2395 -0.69 0.49213
## factor(t)26 0.7720 0.2363 3.27 0.00112
## factor(t)27 0.1555 0.2336 0.67 0.50581
## factor(t)28 0.2689 0.2340 1.15 0.25071
## factor(t)29 1.0308 0.2326 4.43 1.0e-05
## factor(t)30 0.2829 0.2335 1.21 0.22592
## factor(t)31 0.4581 0.2331 1.97 0.04960
## factor(t)32 0.0949 0.2316 0.41 0.68207
## factor(t)33 0.1295 0.2313 0.56 0.57559
## factor(t)34 -0.1755 0.2390 -0.73 0.46288
## factor(t)35 1.4429 0.2409 5.99 2.7e-09
## factor(t)36 -0.6324 0.2350 -2.69 0.00721
## factor(t)37 -0.1851 0.2380 -0.78 0.43681
## factor(t)38 0.7037 0.2337 3.01 0.00265
## factor(t)39 0.0260 0.2338 0.11 0.91163
## factor(t)40 0.3986 0.2331 1.71 0.08759
## factor(t)41 0.7886 0.2317 3.40 0.00069
## factor(t)42 0.2483 0.2383 1.04 0.29762
## factor(t)43 0.6213 0.2325 2.67 0.00762
## factor(t)44 -0.1756 0.2340 -0.75 0.45311
## factor(t)45 0.1490 0.2322 0.64 0.52105
## factor(t)46 -0.0196 0.2375 -0.08 0.93422
## factor(t)47 1.2377 0.2337 5.30 1.4e-07
## factor(t)48 -0.7351 0.2345 -3.14 0.00176
## factor(t)49 -0.3480 0.2354 -1.48 0.13964
## factor(t)50 0.5378 0.2339 2.30 0.02163
## factor(t)51 0.1095 0.2333 0.47 0.63876
## factor(t)52 0.4333 0.2352 1.84 0.06569
## factor(t)53 0.7426 0.2319 3.20 0.00140
## factor(t)54 0.1284 0.2342 0.55 0.58352
## factor(t)55 0.2835 0.2331 1.22 0.22422
## factor(t)56 -0.0524 0.2334 -0.22 0.82245
## factor(t)57 -0.2532 0.2329 -1.09 0.27714
## factor(t)58 -0.4996 0.2319 -2.15 0.03139
## factor(t)59 1.2013 0.2328 5.16 2.9e-07
## factor(t)60 -1.0835 0.2375 -4.56 5.6e-06
## factor(t)61 -0.3844 0.2380 -1.62 0.10647
## factor(t)62 0.1204 0.2357 0.51 0.60939
## factor(t)63 -0.0961 0.2362 -0.41 0.68407
## factor(t)64 0.0975 0.2349 0.42 0.67813
## factor(t)65 0.4602 0.2340 1.97 0.04948
## factor(t)66 0.2719 0.2396 1.13 0.25670
## factor(t)67 0.3006 0.2330 1.29 0.19730
## factor(t)68 -0.0208 0.2319 -0.09 0.92865
## factor(t)69 -0.2986 0.2321 -1.29 0.19838
## factor(t)70 -0.2934 0.2326 -1.26 0.20740
## factor(t)71 1.0658 0.2407 4.43 1.0e-05
## factor(t)72 -1.1009 0.2405 -4.58 5.2e-06
## factor(t)73 -0.2424 0.2454 -0.99 0.32348
## factor(t)74 0.5448 0.2448 2.23 0.02621
## factor(t)75 0.0408 0.2355 0.17 0.86254
## factor(t)76 0.0900 0.2354 0.38 0.70226
## factor(t)77 0.6071 0.2325 2.61 0.00912
## factor(t)78 -0.0149 0.2332 -0.06 0.94908
## factor(t)79 0.3081 0.2334 1.32 0.18709
## factor(t)80 0.0266 0.2320 0.11 0.90857
## factor(t)81 -0.1197 0.2319 -0.52 0.60591
## factor(t)82 -0.2660 0.2727 -0.98 0.32947
## factor(t)83 1.5538 0.2345 6.62 5.1e-11
## factor(t)84 -1.1621 0.2360 -4.93 9.5e-07
## factor(t)85 -0.1481 0.2385 -0.62 0.53476
## factor(t)86 0.6465 0.2358 2.74 0.00620
## factor(t)87 0.0603 0.2347 0.26 0.79727
## factor(t)88 0.1544 0.2341 0.66 0.50971
## factor(t)89 0.7378 0.2316 3.19 0.00148
## factor(t)90 0.2125 0.2333 0.91 0.36256
## factor(t)91 0.2737 0.2324 1.18 0.23913
## factor(t)92 0.2140 0.2316 0.92 0.35567
## factor(t)93 -0.1424 0.2314 -0.62 0.53855
## factor(t)94 0.0977 0.2330 0.42 0.67501
## factor(t)95 1.3315 0.2316 5.75 1.1e-08
## factor(t)96 -1.0336 0.2352 -4.39 1.2e-05
## factor(t)97 0.2540 0.2516 1.01 0.31290
## factor(t)98 0.8030 0.2394 3.35 0.00082
## factor(t)99 0.0531 0.2343 0.23 0.82089
## factor(t)100 0.2961 0.2330 1.27 0.20404
## PRECIPlevelsWet:TMEANlevelsFreezing 0.2788 0.7071 0.39 0.69342
## PRECIPlevelsWet:TMEANlevelsCold 0.1320 0.2966 0.44 0.65644
## PRECIPlevelsDry:TMEANlevelsWarm -1.2626 0.6698 -1.89 0.05964
## PRECIPlevelsWet:TMEANlevelsWarm -0.0628 0.5024 -0.13 0.90050
##
## PRECIPlevelsDrought **
## PRECIPlevelsDry ***
## PRECIPlevelsWet
## PRECIPlevelsSoaked
## TMEANlevelsFreezing
## TMEANlevelsCold
## TMEANlevelsWarm
## TMEANlevelsHot .
## lag(TotalSTD, 1) ***
## lag(TotalSTD, 2) *
## lag(TotalSTD, 3) ***
## factor(t)9 .
## factor(t)10
## factor(t)11 ***
## factor(t)12 ***
## factor(t)13
## factor(t)14 ***
## factor(t)15
## factor(t)16 **
## factor(t)17 .
## factor(t)18
## factor(t)19
## factor(t)20
## factor(t)21
## factor(t)22
## factor(t)23 ***
## factor(t)24 ***
## factor(t)25
## factor(t)26 **
## factor(t)27
## factor(t)28
## factor(t)29 ***
## factor(t)30
## factor(t)31 *
## factor(t)32
## factor(t)33
## factor(t)34
## factor(t)35 ***
## factor(t)36 **
## factor(t)37
## factor(t)38 **
## factor(t)39
## factor(t)40 .
## factor(t)41 ***
## factor(t)42
## factor(t)43 **
## factor(t)44
## factor(t)45
## factor(t)46
## factor(t)47 ***
## factor(t)48 **
## factor(t)49
## factor(t)50 *
## factor(t)51
## factor(t)52 .
## factor(t)53 **
## factor(t)54
## factor(t)55
## factor(t)56
## factor(t)57
## factor(t)58 *
## factor(t)59 ***
## factor(t)60 ***
## factor(t)61
## factor(t)62
## factor(t)63
## factor(t)64
## factor(t)65 *
## factor(t)66
## factor(t)67
## factor(t)68
## factor(t)69
## factor(t)70
## factor(t)71 ***
## factor(t)72 ***
## factor(t)73
## factor(t)74 *
## factor(t)75
## factor(t)76
## factor(t)77 **
## factor(t)78
## factor(t)79
## factor(t)80
## factor(t)81
## factor(t)82
## factor(t)83 ***
## factor(t)84 ***
## factor(t)85
## factor(t)86 **
## factor(t)87
## factor(t)88
## factor(t)89 **
## factor(t)90
## factor(t)91
## factor(t)92
## factor(t)93
## factor(t)94
## factor(t)95 ***
## factor(t)96 ***
## factor(t)97
## factor(t)98 ***
## factor(t)99
## factor(t)100
## PRECIPlevelsWet:TMEANlevelsFreezing
## PRECIPlevelsWet:TMEANlevelsCold
## PRECIPlevelsDry:TMEANlevelsWarm .
## PRECIPlevelsWet:TMEANlevelsWarm
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1250
## Residual Sum of Squares: 507
## R-Squared : 0.537
## Adj. R-Squared : 0.49
## F-statistic: 17.3575 on 107 and 1273 DF, p-value: <2e-16
SCtest(m3n) # non sig serial correlation!
##
## Call:
## lm(formula = res[-n] ~ res[-1])
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.011 -0.355 -0.021 0.356 3.197
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.000175 0.016167 0.01 0.99
## res[-1] 0.005093 0.026802 0.19 0.85
##
## Residual standard error: 0.604 on 1392 degrees of freedom
## Multiple R-squared: 2.59e-05, Adjusted R-squared: -0.000692
## F-statistic: 0.0361 on 1 and 1392 DF, p-value: 0.849
xyplot(m3n$residuals ~ fitted(m3n)) # not bad
qqPlot(m3n$residuals) # not terrible
plot(m3n$residuals ~ m3n$model[[7]])
plot(m3n$residuals ~ m3n$model[[8]])
plot(m3n$residuals[which(m3n$model[[8]] == "Carteret")] ~ m3n$model[[7]][which(m3n$model[[8]] ==
"Carteret")]) # Res vs time
plot(m3n$residuals[which(m3n$model[[8]] == "Miami-Dade")] ~ m3n$model[[7]][which(m3n$model[[8]] ==
"Miami-Dade")]) # Res vs time
