Welcome to the list. You have provided a nic clear question but I think the one thing missing in dealing with it is some sample data.
Have a look at ?dput or see the dput() discussions in one of these links : http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example and/or http://adv-r.had.co.nz/Reproducibility.html Someone may easily find the problem without sample data but it usually is best to troubleshoot with the "real" data. John Kane Kingston ON Canada > -----Original Message----- > From: djv5...@gmail.com > Sent: Sun, 6 Dec 2015 19:47:12 -0600 > To: r-help@r-project.org > Subject: [R] Simple DLNM in R > > 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 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ____________________________________________________________ FREE ONLINE PHOTOSHARING - Share your photos online with your friends and family! Visit http://www.inbox.com/photosharing to find out more! ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
