Another thought on checking the validity of the suggested 2*log(likelihood ratio) procedure I suggested: If it were my problem, I think I would do some checking using Monte Carlo, e.g., as described in sec. 2.6 of the vignette "MlmSoftRev" in the "mlmRev" package. This is particularly relevant for testing a parameter at a boundary, e.g., whether a particular variance component is 0, because the assumptions for the traditional chi-square approximation to 2*log(LR) do not hold in that case, as documented in sec. 2.4 of Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer).
Spencer Graves wrote: > You are correct on both counts. The exta line is inserted below; > obviously, I had it but failed to copy it into the email. > > And you are also correct that one needs to be careful that both > glm and lmer are using comparable definitions for the log(likelihood). > My crude check on that was just to look compare the lglk0 and lglk.ID1.; > the numbers seemed too close to be based on different definitions. In > addition, I think I may have checked this once before, but my memory > could be faulty on that point. > > Thanks for pointing out both deficiencies in my reply. > spencer graves > > hadley wickham wrote: > >>> ### To get around that, I computed 2*log(likelihood ratio) manually: >>> >>> lglk0 <- logLik(fit0) >>> lglk.ID1. <- logLik(Fit.ID1.) > > chisq.ID. <- 2*(lglk.ID1.-lglk0) > >>> pchisq(as.numeric(chisq.ID.), 1, lower=FALSE) >>> > [1] 0.008545848 >> >> >> >> (I think you're missing a line in there) >> >> But isn't this rather perilous unless you are confident that the two >> models are using exactly the same formulation of the likelihood? (ie. >> that they are truly nested) >> >> Hadley > > ______________________________________________ [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
