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library(gdata) | |
Data = read.xls("http://www.pnas.org/content/suppl/2014/05/30/1402786111.DCSupplemental/pnas.1402786111.sd01.xlsx", | |
nrows = 92, as.is = TRUE) | |
library(glmmTMB) | |
originalModelGAM = glmmTMB(alldeaths ~ scale(MasFem) * | |
(scale(Minpressure_Updated.2014) + scale(NDAM)), | |
data = Data, family = nbinom2) | |
# Residual checks with DHARMa | |
library(DHARMa) | |
res <- simulateResiduals(originalModelGAM) | |
plot(res) | |
# no significant deviation in the general plot, but try this | |
# which was highlighted by https://www.theguardian.com/science/grrlscientist/2014/jun/04/hurricane-gender-name-bias-sexism-statistics | |
plotResiduals(res, Data$NDAM) | |
# we also find temporal autocorrelation | |
res2 = recalculateResiduals(res, group = Data$Year) | |
testTemporalAutocorrelation(res2, time = unique(Data$Year)) |
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