I think the `gamlss' package can do this. Simon
On Fri, 16 May 2008, Markus Loecher wrote: > Dear list members, > while I appreciate the possibility to deal with overdispersion for count > data either by specifying the family argument to be quasipoisson() or > negative.binomial(), it estimates just one overdispersion parameter for the > entire data set. > In my applications I often would like the estimate for overdispersion to > depend on the covariates in the same manner as the mean. > > For example, > #either library(mgcv) or library(gam): > > x <- seq(0,1,length = 100)*2*pi > mu <- 4+ 2*sin(x) > size <- 4 + 2*cos(x) > data <- cbind.data.frame(x<- rep(x,10), y = > rnbinom(10*100,mu=rep(mu,10),size=rep(size,10))) > > x.gam <- gam(y~s(x), data=data,family=quasipoisson()) > plot(x.gam) > summary(x.gam) > > How would I get a smooth estimate of the overdispersion ? > > Thanks, > > Markus > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > 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. > ______________________________________________ R-help@r-project.org mailing list 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.
