[R] mixture models/latent class regression comparison

[Carson Farmer]

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
> chrish at 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/