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

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

sessionInfo()

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