Dear Caroline, Yes, it seems you have complete separation for the 'Timepoint' variable. This means that the likelihood is unbounded for that parameter and the optimizer just terminates when it gets far enough out on an asymptote and improvements are below a threshold. This is also the reason the variance-covariance matrix of the parameters i singular, and so standard errors and summary-p-values are not available. This doesn't mean that you cannot perform the likelihood ratio test though, so the anova() comparisons should not be misleading.
I would recommend, though, that (1) if you really want to fit the mixed-effects models, use the newer implementation in clmm() instead of clmm2(), and that you (2) fit your models without the random-effects term, since the variance of that is clearly zero (and hence use clm()) - this is going to add to the instability of the optimization and singularity of the variance-covariance matrix of the parameters. Hope this helps, Rune On 16 March 2014 19:45, Caroline Lustenberger <caroline.lustenber...@hotmail.com> wrote: > Dear all > > I have ordinal data (from a questionnaire with 4 levels) for 2 groups (30 > subjects each) and 2 timepoints. So I used a cumulative link mixed model to > fit the data (nr = subject number). > > mod_FV<-clmm2(FV~GruppeVerbelendung+Timepoint,random=nr,data=data,Hess=TRUE,nAGQ=10,na.action=na.omit) > mod_FV1<-clmm2(FV~GruppeVerbelendung,random=nr,data=data,Hess=TRUE,nAGQ=10,na.action=na.omit) > > > For some questions (e.g. FV) I had the following output with NANs instead of > p-values > > Call: > clmm2(location = FV ~ GruppeVerbelendung * Timepoint, random = nr, > data = data, na.action = na.omit, Hess = TRUE, nAGQ = 10) > > Random effects: > Var Std.Dev > nr 5.881958e-07 0.0007669392 > > Location coefficients: > Estimate Std. Error z value Pr(>|z|) > GruppeVerbelendung1 -0.0178 NaN NaN NA > Timepoint1 18.6316 NaN NaN NA > GruppeVerbelendung1:Timepoint1 0.3505 NaN NaN NA > > No scale coefficients > > Threshold coefficients: > Estimate Std. Error z value > 1|2 20.7741 NaN NaN > 2|3 20.9486 NaN NaN > > log-likelihood: -24.01783 > AIC: 60.03566 > Condition number of Hessian: 5479162.40 > (4 observations deleted due to missingness) > Warnmeldung: > In sqrt(diag(vc)) : NaNs wurden erzeugt > > However when comparing the models (mod_FV, mod_FV1) I obtain a usable result > (p-val) even though the models seem not to be a good fit (high AIC and > condition number of Hessian). > > anova(mod_FV,mod_FV1) > Likelihood ratio tests of cumulative link models > > Response: FV > Model Resid. df -2logLik Test Df LR > stat. Pr(Chi) > 1 GruppeVerbelendung | | 112 58.44384 > 2 GruppeVerbelendung + Timepoint | | 111 27.78259 1 vs 2 1 > 30.66125 3.072406e-08 > > What could be the problem? Timepoint 1 was always scored with level 1 (60 of > 60 subjects had scored 1) and during timepoint 2 subjects scored level 1-3. > Might it be a problem that I only have 1 type of scoring level for timepoint > 1? > Can I use the results obtained by Likelihod ratio test for clm even if the > model was not a good fit? What could I do instead? > > > Thank you so much for your help and all the best > Caroline > > > > > > [[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.
