[R] R-help Digest, Vol 171, Issue 20

Brigitte Mangin brigitte.mangin at inra.fr
Mon May 29 08:53:18 CEST 2017

Thanks Ron,

In fact, I want to make a model choice using different fixed structures and using the results of:
Gurka MJ (2006) Selecting the best linear mixed model under reml. The American Statistician 60(1):19{26,
the best criterium uses the reml likelihood.

I asked the ASREML-r developpers and they answered that their results were checked against GENSTAT.

I think it is not really a good think for the R community to compute a REML likelihood that is probably not the REML likelihood.


Brigitte Mangin, INRA, LIPM, CS 52627, 31326 CASTANET-TOLOSAN
tel: 33 + (0)5 61 28 54 58

De : Crump, Ron <R.E.Crump at warwick.ac.uk>
Envoyé : mardi 23 mai 2017 10:29
À : r-help at r-project.org; Brigitte Mangin
Objet : Re: R-help Digest, Vol 171, Issue 20

Hi Brigitte,

>Did somebody know why asreml does not provide the same REML loglikehood
>as coxme, lme4 or lmne.

I don't know the answer to this, but I'd guess it is either to do with the
use of the average information REML algorithm or asreml-r is for some
reason ending up with a different subset of the data.

>If it was just a constant value between the two models (with or without
>the fixed effect) it would not be important. But it is not.
>I checked that the variance component estimators were equal.

I'm still not clear that it is important (if the data subset analysed is
the same). You would only use the REML likelihoods to compare models with
different random effects and the same fixed effect structure (is there
another use for the REML likelihood other than that?), so then it is
really a question of whether for a given pair of random effect models and
the same data the likelihood ratio test statistic  changes across analysis
methods. Unless for some reason you are comparing two random effect models
fitted with different routines (one of which is asreml-r).


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