Thanks for the reply Christian, > I have never used mmlcr for this, but quite generally when fitting such > models, the likelihood has often very many local optima. This means that the > result of the EM (or a similar) algorithm depends on the initialisation, > which in flexmix (and perhaps also in mmlcr) is done in a random fashion. > This means that results may differ even if the same method is applied twice, > and unfortunately, depending on the dataset, the result may be quite > unstable. This may explain that the two functions give you strongly > different results, not of course implying that one of them is generally > better. I though this might be the issue here. So is the solution to simply run it many times and look for the best likelihood? I am still confused as to why the mmlcr function consistently produces 'poorer' results? Perhaps the flexmix package is being a bit more 'clever' in terms of avoiding local optima? I will have to dig a bit deeper.
Cheers, Carson >> Dear list, >> >> I have been comparing the outputs of two packages for latent class >> regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that >> the flexmix package appears to come up with a much better fit than the >> mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has >> anyone else observed such behaviour? Has anyone else been successful >> in using the mmlcr package? I ask because I am interested in latent >> class negative binomial regression, which the mmlcr package appears to >> support, however, the results for basic Poisson latent class >> regression appear to be inferior to the results from flexmix. Below is >> a simple reproducible example to illustrate the comparison: >> >> library(flexmix) >> library(mmlcr) >> data(NPreg) # from package flexmix >> m1 <- flexmix(yp ~ x, k=2, data=NPreg, model=FLXMRglm(family='poisson')) >> NPreg$id <- 1:200 # mmlcr requires an id column >> m2 <- mmlcr(outer=~1|id, components=list(list(formula=yp~x, >> class="poisonce")), data=NPreg, n.groups=2) >> >> # summary and coefficients for flexmix model >> summary(m1) >> summary(refit(m1)) >> >> # summary and coefficients for mmlcr model >> summary(m2) >> m2 >> >> Regards, >> >> Carson >> >> P.S. I have attached a copy of the mmlcr package with a modified >> mmlcr.poisonce function due to errors in the version available here: >> http://cran.r-project.org/src/contrib/Archive/mmlcr/. See also >> http://jeldi.com/Members/jthacher/tips-and-tricks/programs/r/mmlcr >> section "Bugs?" subsection "Poisson". >> >> -- >> Carson J. Q. Farmer >> ISSP Doctoral Fellow >> National Centre for Geocomputation >> National University of Ireland, Maynooth, >> http://www.carsonfarmer.com/ >> > > *** --- *** > Christian Hennig > University College London, Department of Statistical Science > Gower St., London WC1E 6BT, phone +44 207 679 1698 > chr...@stats.ucl.ac.uk, www.homepages.ucl.ac.uk/~ucakche > -- Carson J. Q. Farmer ISSP Doctoral Fellow National Centre for Geocomputation National University of Ireland, Maynooth, http://www.carsonfarmer.com/ ______________________________________________ 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.
