Dear Experts, We conducted an experiment with 2 within-subject factors. We have very good reason to use gamlss, which works fine for our dataset, but unfortunately in the final model the vcov matrix can not be produced. This possibility is documented in the gamlss manual, but no hint is given how to resolve the problem if multiple comparisons are needed. (Actually I prefer building models with and without the given term and check the information criteria or running likelihood-ratio tests, but reviewers on my field do insist on such direct comparisons.)
Here I give you a short example (the actual design is far more complicated, but the problem is essentially the same): ############################# # EXAMPLE ############################# library(gamlss) # make it reproducable set.seed(1234) # define the subject factor and two within-subject factors # (fa & fb, with 2 and 3 levels, respectively) datfr <- expand.grid(subj=1:20,fa=0:1,fb=0:2)[rep(1:120,5),] # define dependent variable datfr$y <- with(datfr, rep(sample(rnorm(20,0,2)),30) + fa*2+ifelse(fa==1,rnorm(600,0,2),rnorm(600,0,1)) + fb*3+rnorm(600,0,fb) + fa*fb*3) datfr$fa <- factor(datfr$fa) datfr$fb <- factor(datfr$fb) datfr$subj <- factor(datfr$subj) # our dataset is unbalanced, that's why datfr <- datfr[sample(600,400),] # gamlss model m.gamlss <- gamlss(y~ fa*fb + random(subj), sigma.fo= ~fa +fb, data=datfr) summary(m.gamlss) ############################### Regards, Denes ______________________________________________ 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.