R help - May 2017 - mixed Model: asreml-r versus nmle,lme4 or coxme

Thank's  Thierry, but as i mentioned, it is not a constant depending only of
the data, since with the same observed trait:


the difference (between asreml and R packages) is equal to 29.40 in the model
with a fixed effect (Type)

and the difference is equal to 32.16 in the model with only mu.


And that, it is a big concern.


________________________________
De : Thierry Onkelinx <thierry.onkelinx at inbo.be>
Envoy? : vendredi 19 mai 2017 16:40
? : Brigitte Mangin
Cc : R-help at lists.R-project.org
Objet : Re: [R] mixed Model: asreml-r versus nmle,lme4 or coxme

Dear Brigitte,

Maybe because the log likelihood is calculated differently. Note that the log
likelihood contains a constant which only depends on the data. So one can safely
omit that part for model comparison, assuming that use you the same formula to
calculate the likelihood for all models.

Best regards,

ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek / Research Institute for Nature and
Forest
team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance
Kliniekstraat 25
1070 Anderlecht
Belgium

To call in the statistician after the experiment is done may be no more than
asking him to perform a post-mortem examination: he may be able to say what the
experiment died of. ~ Sir Ronald Aylmer Fisher
The plural of anecdote is not data. ~ Roger Brinner
The combination of some data and an aching desire for an answer does not ensure
that a reasonable answer can be extracted from a given body of data. ~ John
Tukey

2017-05-19 14:30 GMT+02:00 Brigitte Mangin <brigitte.mangin at
inra.fr<mailto:brigitte.mangin at inra.fr>>:



Hi,

Did somebody know why asreml does not provide the same REML loglikehood  as
coxme, lme4 or lmne.
Here is a simple example showing the differences:


#######################################################################
library(lme4)
library(coxme)
library(asreml)
library(nlme)

data(ergoStool, package="nlme") # use a data set from nlme

fit1 <- lmekin(effort ~ Type+(1|Subject),
data=ergoStool,method="REML")
fit1$loglik #-60.56539
fit2 <- lmer(effort ~ Type+(1|Subject), data=ergoStool,REML=TRUE)
logLik(fit2) #'log Lik.' -60.56539 (df=6)
fit3<-asreml(fixed=effort ~ Type,random=~Subject,data=ergoStool,
        na.method.X="omit",na.method.Y="omit")
fit3$loglik #-31.15936
fit4<-lme(effort ~ Type,random=~1|Subject, data =
ergoStool,method="REML")
fit4$logLik  #-60.56539

fit1 <- lmekin(effort ~ (1|Subject), data=ergoStool,method="REML")
fit1$loglik #-78.91898
fit2 <- lmer(effort ~ (1|Subject), data=ergoStool,REML=TRUE)
logLik(fit2) #'log Lik.' -78.91898 (df=3)
fit3<-asreml(fixed=effort ~ 1,random=~Subject,data=ergoStool,
        na.method.X="omit",na.method.Y="omit")
fit3$loglik #-46.75614
fit4<-lme(effort ~ 1,random=~1|Subject, data =
ergoStool,method="REML")
fit4$logLik #-78.91898


############################
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.

Thanks



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