Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack)
# I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR EXCHANGEABLE AND AR(1) CORRELATION STRUCTURES model1 <- gee(y ~ x, family=poisson(),id=id, corstr="exchangeable") model2 <- gee(y ~ x, family=poisson(),id=id, corstr="AR-M") # NOW 50 OBS FOR EACH OF 10 GROUPS id2 <- rep(1:10,each=50) model3 <- gee(y ~ x, family=poisson(),id=id2, corstr="exchangeable") model4 <- gee(y ~ x, family=poisson(),id=id2, corstr="AR-M") # ERROR model5 <- geeglm(y ~ x, family=poisson(),id=id2, corstr="ar1") ########################################################## Basically, it seems that the gee command (package gee) doesn't work when the id groups are large, as in my dataset (observations from several summer seasons, for which I imagine an AR correlation structure within each season). The command geeglm (package geepack) seems to work, but provides only few corstr choices (for example not stat_M_dep, which can be useful to investigate models with different correlation structures). Any suggestions? Thanks so much for your time ______________________________________________ [email protected] 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.
