lmer() vs. lme() gave different variance component estimates

lmer(score~trt+(1|id/eye),dat)

sessionInfo()

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

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

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