Hello Thierry,
Based on the code below, it looks like you do not need to worry about
likelihoods from lm() and gls(). They are defined the same way. I agree
with you that caution needs to be exercised. Simply because
mathematically the same likelihood may be defined using different constant.
Regards,
Have a good weekend
Andrzej Galecki
library(nlme)
data("sleepstudy", package = "lme4")
lmx <- lm(Reaction ~ Days, sleepstudy)
glsx<- gls(Reaction ~ Days, sleepstudy)
lmex <- lme(Reaction ~ Days, random= ~1|Subject, sleepstudy)
# LogLik values are the same
logLik(lmx) #
'log Lik.' -950.1465 (df=3)
logLik(glsx,REML=FALSE) # 'log Lik.'
-950.1465 (df=3)
# REML values are the same
logLik(lmx, REML=TRUE) # 'log Lik.'
-946.8318 (df=3)
logLik(glsx, REML=TRUE) # 'log Lik.'
-946.8318 (df=3)
# Two anovas give the same results
# (NOTE: Do not use anova for testing the need of random intercept!
Reference distribution other than chi-squre should be used)
anova(lmex, lmx)
anova(lmex, glsx)
# Model df AIC BIC logLik Test L.Ratio p-value
# lmex 1 4 1794.465 1807.192 -893.2325
# lmx 2 3 1899.664 1909.209 -946.8318 1 vs 2 107.1986 <.0001
On 3/18/2011 4:55 AM, ONKELINX, Thierry wrote:
Dear Mark,
I'm cc'ing this to the mixed models list to get some input from other experts.
For them a link to the entire thread:
http://r.789695.n4.nabble.com/lmm-WITHOUT-random-factor-lme4-tp3384054p3384823.html
My comment was based on what I have read in Zuur et al. (2009).
What worries me is that the loglikelihood of a lm() model and the equivalent
gls() model is different. Although both models should be mathematically
identical. Assuming that the loglikelihood is calculated on the same way within
a package, I therefore have more confidence in comparing two models from the
same package, thus gls() versus lme().
Furthermore, I get an error when doing an anova between a lm() and a lme()
model.
Best regards,
Thierry
library(nlme)
data("sleepstudy", package = "lme4")
fm<- lm(Reaction ~ Days, sleepstudy)
fm0<- gls(Reaction ~ Days, sleepstudy)
logLik(fm)
'log Lik.' -950.1465 (df=3)
logLik(fm0)
'log Lik.' -946.8318 (df=3)
fm1<- lme(Reaction ~ Days, random= ~1|Subject, sleepstudy)
anova(fm, fm1)
Error in anova.lmlist(object, ...) :
models were not all fitted to the same size of dataset
anova(fm0, fm1)
Model df AIC BIC logLik Test L.Ratio p-value
fm0 1 3 1899.664 1909.209 -946.8318
fm1 2 4 1794.465 1807.192 -893.2325 1 vs 2 107.1986<.0001
sessionInfo()
R version 2.12.2 (2011-02-25)
Platform: i386-pc-mingw32/i386 (32-bit)
locale:
[1] LC_COLLATE=Dutch_Belgium.1252 LC_CTYPE=Dutch_Belgium.1252
[3] LC_MONETARY=Dutch_Belgium.1252 LC_NUMERIC=C
[5] LC_TIME=Dutch_Belgium.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] nlme_3.1-98
loaded via a namespace (and not attached):
[1] grid_2.12.2 lattice_0.19-19 tools_2.12.2
----------------------------------------------------------------------------
ir. Thierry Onkelinx
Instituut voor natuur- en bosonderzoek
team Biometrie& Kwaliteitszorg
Gaverstraat 4
9500 Geraardsbergen
Belgium
Research Institute for Nature and Forest
team Biometrics& Quality Assurance
Gaverstraat 4
9500 Geraardsbergen
Belgium
tel. + 32 54/436 185
thierry.onkel...@inbo.be
www.inbo.be
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
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