Pfthb

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 (call 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.

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))

The problem is you did not put any time variables to control the long-term and seasonal trends in the time-series using DLNM .