On Sat, Jul 12, 2008 at 6:23 AM, Lan Wei <[EMAIL PROTECTED]> wrote: > Hi all, > > I have a problem when running lmer. > In my data set, Agree is a binary(0/1) response. WalkerID and ObsID is > the identification number of the subjects. the description of the > other variables are as follows: >> >> levels(regdat$Display) > > [1] "Dynamic" "Static" >> >> levels(regdat$Survey) > > [1] "HM1_A" "HM1_B" "HM1_C" "HM2_A" "HM2_B" "HM2_C" "ST_A" "ST_B" > "ST_C" >> >> levels(regdat$Emotion) > > [1] "aneu" "ang" "con" "joy" "joy " "sad" >> >> levels(regdat$ObsGender) > > [1] "F" "M" >> >> levels(regdat$WalkerGender) > > [1] "F" "M" > > the watning is: >> > fit1<-lmer(Agree~Display+Survey+Emotion+WalkerGender+ObsGender+(1|WalkerID)+(1|ObsID),family=binomial(link='logit'),data=regdat) > Warning message: > In mer_finalize(ans, verbose) : gr cannot be computed at initial par > (65)
> Does anybody have some hint to solve this problem? I'd very much appreciate > it! In situations like this it is best to add the argument verbose = TRUE in the call to lmer so that you can see the progress of the iterations. (Also, you may want to call glmer directly. When you call lmer with a non-gaussian family it simply calls glmer. You can avoid the extra step.) This call is returning a warning about evaluation of the gradient at the initial values of the parameters. I'm not sure if it then goes on to optimize the approximated deviance. If the approximated deviance is not being minimized for this model you may want to start with a simpler model, omitting some of the terms in the fixed effects. ______________________________________________ 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.
