Hello, first time poster so forgive any mistakes.
I have limited familiarity with R, but am working on a project to find the
relative risk of mortality due to changes in diurnal temperature range.
What I am trying to do is find the relative risk of mortality at the 10th,
50th and 90th percentiles of diurnal temperature range and its additive
effects at lags of 0, 1, 3 and 5 days. I'm doing this for a subset of
months May-Sept (I call the subset here for mortality, temperature is
already subsetted when read in). I have a code that works below, but no
matter what city and what lag I introduce, I get a RR of essentially 1.0,
so I believe that something is off or I am missing an argument somewhere.
If anyone has more experience with these problems than I, your help would
be greatly appreciated. Code is below:
library('dlnm')
library('splines')
mortdata <- read.table('STLmort.txt', sep="\t", header=T)
morts <- subset(mortdata, Month %in% 5:9)
deaths <- morts$AllMort
tempdata <- read.csv('STLRanges.csv',sep=',',header=T)
temp <- tempdata$Trange
HI <- tempdata$HIrange
#basis.var <- onebasis(1:5, knots=3)#mklagbasis(maxlag=5, type="poly",
degree=3)
basis.temp <- crossbasis(temp,vardegree=3,lag=5)
summary(basis.temp)
model <- glm (deaths ~ basis.temp, family=quasipoisson())
pred.temp <- crosspred(basis.temp, model,
at=quantile(temp,c(.10,.50,.90),na.rm=TRUE) , cumul=T)
plot(pred.temp, "slices", var=c(quantile(temp, c(.10, .50,
.90),na.rm=TRUE)) ,lag=c(0,1,5))
--
Daniel J. Vecellio
PhD Student, Department of Geography
Texas A&M University
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