[R-sig-ME] logLik (old-fashion way) for mixed-effects models
Gabor Grothendieck
ggrothendieck at gmail.com
Thu Apr 16 15:55:14 CEST 2009
The regress package maximizes log likelihood (as well as other
criteria) for mixed models whose covariance matrix is a linear
combination of known matrices.
On Thu, Apr 16, 2009 at 5:03 AM, Christian Salas
<christian.salas at yale.edu> wrote:
> If i already fit a lm() model, i can obtain the log-likelihood [i do not
> want to use AIC()] using the residuals from the model, and using the
> RMSE of the model as sigma for my normal pdf. This would be in R like
>
>> sum(dnorm(-resi,mean=0,sd=sigma,log=T)) [1]
>
> if i fit a gls model i can do the same
>
> for a lme() model, i know that we cannot just use the same loglik model
> [1], because they are different. I wonder if somedody already have some
> syntax in R similar to [1] but for mixed-effects models, i mean something
> that compute the log-likelihood but without using lme() directly as
> summary(lme.obj)$AIC
>
> thanks in advance!
>
> -------------------------------------------------------------------------------
> Christian Salas E-mail:christian.salas at yale.edu
> PhD candidate http://environment.yale.edu/salas
> School of Forestry and Environmental Studies
> Yale University Tel: +1-(203)-432 5126
> 360 Prospect St Fax:+1-(203)-432 3809
> New Haven, CT 06511-2189 Office: Room 35, Marsh Hall
> USA
>
> Yale Biometrics Lab http://environment.yale.edu/biometrics
>
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