[R] lmer() vs. lme() gave different variance component estimates

[array chip]

Hi, I asked this on mixed model mailing list, but that list is not very active, 
so I'd like to try the general R mailing list. Sorry if anyone receives the 
double post.


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


      
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