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@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@gmail.com>
> To: array chip <arrayprofile@yahoo.com>
> Cc: r-sig-mixed-models@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@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@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
> >
> >
>
>
>
>
>
>


-- 
Daniel Marcelino
Skype: dmsilv
http://bit.ly/pol4vc

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