[R-sig-ME] lmer() vs. lme() gave different variance component estimates
Andrew Robinson
A.Robinson at ms.unimelb.edu.au
Sat Sep 18 22:34:09 CEST 2010
So far the results split on platform lines: John's is pc, Daniel's and
Reinhold's are Mac.
I get the Mac result on FreeBSD.
> lmer(score~trt+(1|id/eye),dat)
Linear mixed model fit by REML
Formula: score ~ trt + (1 | id/eye)
Data: dat
AIC BIC logLik deviance REMLdev
425.2 474.2 -201.6 412.7 403.2
Random effects:
Groups Name Variance Std.Dev.
eye:id (Intercept) 3.59531 1.89613
id (Intercept) 3.51025 1.87357
Residual 0.01875 0.13693
Number of obs: 640, groups: eye:id, 160; id, 80
Fixed effects:
Estimate Std. Error t value
(Intercept) 2.5500 0.7287 3.499
trtB 0.9875 1.0305 0.958
trtC 0.8625 1.0305 0.837
trtControl 1.1500 1.0305 1.116
trtD 2.4125 1.0305 2.341
trtE 1.4375 1.0305 1.395
trtF 2.1750 1.0305 2.111
trtG -2.5500 1.0305 -2.474
Correlation of Fixed Effects:
(Intr) trtB trtC trtCnt trtD trtE trtF
trtB -0.707
trtC -0.707 0.500
trtControl -0.707 0.500 0.500
trtD -0.707 0.500 0.500 0.500
trtE -0.707 0.500 0.500 0.500 0.500
trtF -0.707 0.500 0.500 0.500 0.500 0.500
trtG -0.707 0.500 0.500 0.500 0.500 0.500 0.500
> sessionInfo()
R version 2.11.1 (2010-05-31)
x86_64-unknown-freebsd8.0
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] lme4_0.999375-35 Matrix_0.999375-43 lattice_0.19-11
loaded via a namespace (and not attached):
[1] grid_2.11.1 nlme_3.1-96 stats4_2.11.1
Cheers
Andrew
On Fri, Sep 17, 2010 at 04:57:59PM -0300, Daniel wrote:
> I really don't know what is going wrong.
>
> sessionInfo()
> R version 2.11.1 (2010-05-31)
> x86_64-apple-darwin9.8.0
>
> locale:
> [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] lme4_0.999375-35 Matrix_0.999375-43 lattice_0.18-8
>
> loaded via a namespace (and not attached):
> [1] grid_2.11.1 nlme_3.1-96 stats4_2.11.1
>
>
> On Fri, Sep 17, 2010 at 4:33 PM, array chip <arrayprofile at yahoo.com> wrote:
>
> > Hi, Reinhold and Daniel,
> >
> > I just re-installed lme4 package, still got different results than yours.
> >
> > Here is my sessionInfo():
> >
> > R version 2.11.1 (2010-05-31)
> > i386-pc-mingw32
> >
> > locale:
> > [1] LC_COLLATE=English_United States.1252
> > [2] LC_CTYPE=English_United States.1252
> > [3] LC_MONETARY=English_United States.1252
> > [4] LC_NUMERIC=C
> > [5] LC_TIME=English_United States.1252
> >
> > attached base packages:
> > [1] stats graphics grDevices datasets utils methods base
> >
> > other attached packages:
> > [1] lme4_0.999375-35 Matrix_0.999375-43 lattice_0.18-8
> >
> > loaded via a namespace (and not attached):
> > [1] grid_2.11.1 nlme_3.1-96 stats4_2.11.1
> >
> > John
> >
> >
> >
> >
> >
> >
> >
> >
> >
> > ----- Original Message ----
> > From: Reinhold Kliegl <reinhold.kliegl at gmail.com>
> > To: array chip <arrayprofile at yahoo.com>
> > Cc: r-sig-mixed-models at r-project.org
> > Sent: Fri, September 17, 2010 12:14:13 PM
> > Subject: Re: [R-sig-ME] lmer() vs. lme() gave different variance component
> > estimates
> >
> > Not on my computer. Perhaps you could provide sessionInfo()?
> >
> > > lmer(score~trt+(1|id/eye), dat)
> > Linear mixed model fit by REML
> > Formula: score ~ trt + (1 | id/eye)
> > Data: dat
> > AIC BIC logLik deviance REMLdev
> > 425.2 474.2 -201.6 412.7 403.2
> > Random effects:
> > Groups Name Variance Std.Dev.
> > eye:id (Intercept) 3.59532 1.89613
> > id (Intercept) 3.51024 1.87356
> > Residual 0.01875 0.13693
> > Number of obs: 640, groups: eye:id, 160; id, 80
> >
> > > sessionInfo()
> > R version 2.11.1 Patched (2010-07-16 r52550)
> > x86_64-apple-darwin9.8.0
> >
> > locale:
> > [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
> >
> > attached base packages:
> > [1] stats graphics grDevices utils datasets methods base
> >
> > other attached packages:
> > [1] lme4_0.999375-34 Matrix_0.999375-43 lattice_0.18-8
> >
> > loaded via a namespace (and not attached):
> > [1] grid_2.11.1 nlme_3.1-96 stats4_2.11.1 tools_2.11.1
> >
> > Reinhold Kliegl
> >
> >
> > On Fri, Sep 17, 2010 at 8:39 PM, array chip <arrayprofile at yahoo.com>
> > wrote:
> > > Hi, I have a dataset of animals receiving some eye treatments. There are
> > 8
> > > treatments, each animal's right and left eye was measured with some
> > scores
> > > (ranging from 0 to 7) 4 times after treatment. So there are nesting
> > groups
> > eyes
> > > within animal. Dataset attached
> > >
> > >> dat<-read.table("dat.txt",sep='\t',header=T,row.names=1)
> > >> dat$id<-factor(dat$id)
> > >> str(dat)
> > > 'data.frame': 640 obs. of 5 variables:
> > > $ score: int 0 2 0 7 4 7 0 2 0 7 ...
> > > $ id : Factor w/ 80 levels "1","3","6","10",..: 7 48 66 54 18 26 38 52
> > 39
> > 63
> > > ...
> > > $ rep : int 1 1 1 1 1 1 1 1 1 1 ...
> > > $ eye : Factor w/ 2 levels "L","R": 2 2 2 2 2 2 2 2 2 2 ...
> > > $ trt : Factor w/ 8 levels "A","B","C","Control",..: 1 1 1 1 1 1 1 1 1
> > 1 ...
> > >
> > > I fit a mixed model using both lmer() from lme4 package and lme() from
> > nlme
> > > package:
> > >
> > >> lmer(score~trt+(1|id/eye),dat)
> > >
> > > Linear mixed model fit by REML
> > > Formula: score ~ trt + (1 | id/eye)
> > > Data: dat
> > > AIC BIC logLik deviance REMLdev
> > > 446.7 495.8 -212.4 430.9 424.7
> > > Random effects:
> > > Groups Name Variance Std.Dev.
> > > eye:id (Intercept) 6.9208e+00 2.630742315798
> > > id (Intercept) 1.4471e-16 0.000000012030
> > > Residual 1.8750e-02 0.136930641909
> > > Number of obs: 640, groups: eye:id, 160; id, 80
> > >
> > >> summary(lme(score~trt, random=(~1|id/eye), dat))
> > >
> > > Linear mixed-effects model fit by REML
> > > Data: dat
> > > AIC BIC logLik
> > > 425.1569 474.0947 -201.5785
> > >
> > > Random effects:
> > > Formula: ~1 | id
> > > (Intercept)
> > > StdDev: 1.873576
> > >
> > > Formula: ~1 | eye %in% id
> > > (Intercept) Residual
> > > StdDev: 1.896126 0.1369306
> > >
> > > As you can see, the variance components estimates of random effects are
> > quite
> > > different between the 2 model fits. From the data, I know that the
> > variance
> > > component for "id" can't be near 0, which is what lmer() fit produced, so
> > I
> > > think the lme() fit is correct while lmer() fit is off. This can also be
> > seen
> > > from AIC, BIC etc. lme() fit has better values than lmer() fit.
> > >
> > >
> > > I guess this might be due to lmer() didn't converge very well, is there
> > anyway
> > > to adjust to make lmer() converge better to get similar results as lme()?
> > >
> > > Thanks
> > >
> > > John
> > >
> > >
> > >
> > > _______________________________________________
> > > R-sig-mixed-models at r-project.org mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> > >
> > >
> >
> >
> >
> >
> >
> >
>
>
> --
> Daniel Marcelino
> Skype: dmsilv
> http://bit.ly/pol4vc
>
> [[alternative HTML version deleted]]
>
> _______________________________________________
> R-sig-mixed-models at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
--
Andrew Robinson
Program Manager, ACERA
Department of Mathematics and Statistics Tel: +61-3-8344-6410
University of Melbourne, VIC 3010 Australia (prefer email)
http://www.ms.unimelb.edu.au/~andrewpr Fax: +61-3-8344-4599
http://www.acera.unimelb.edu.au/
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