Have you also tried "intervals" on the output of "nlme"? I have not used "nlme", but it works in "lme". Also, have you looked at Pinhiero and Bates (2000) Mixed-Effects Models in S and S-PLUS (Springer)? I had to read and carefully work through a portion of this book before I was able to use "lme" successfully. However, I found it well worth the effort, both for how to use "lme" and for understanding the theory behind it. Bates and his graduate students including Pinhiero wrote "lme" and "nlme", and I know of no better source on this subject.

hope this helps. spencer graves

Philip Turk wrote:

I asked this before and I am going to try again in more applied terms.  I
am trying to use R to extract variance components for a two-factor random
effects model with both factors crossed.  It would also be nice to
generate some confidence intervals as well.  For example, a data set
using SAS Proc Mixed is below followed by the four variance component
estimates and the respective confidence intervals.  Currently, I have been
unable to reproduce this in NLME but I am sure I have not correctly
specified the "random" option.

Any help and/or ideas would be greatly appreciated!

## SAS PROGRAM WITH DATA FOLLOWS

data hw7;
input mpg driver car obs;
cards;
25.3    1       1       1
25.2    1       1       2
28.9    1       2       1
30      1       2       2
24.8    1       3       1
25.1    1       3       2
28.4    1       4       1
27.9    1       4       2
27.1    1       5       1
26.6    1       5       2
33.6    2       1       1
32.9    2       1       2
36.7    2       2       1
36.5    2       2       2
31.7    2       3       1
31.9    2       3       2
35.6    2       4       1
35      2       4       2
33.7    2       5       1
33.9    2       5       2
27.7    3       1       1
28.5    3       1       2
30.7    3       2       1
30.4    3       2       2
26.9    3       3       1
26.3    3       3       2
29.7    3       4       1
30.2    3       4       2
29.2    3       5       1
28.9    3       5       2
29.2    4       1       1
29.3    4       1       2
32.4    4       2       1
32.4    4       2       2
27.7    4       3       1
28.9    4       3       2
31.8    4       4       1
30.7    4       4       2
30.3    4       5       1
29.9    4       5       2
;

proc mixed data = hw7 method = reml cl asycov;
class driver car;
model mpg =;
random driver car driver*car;
run;
quit;

## SELECTED OUTPUT FOLLOWS

Covariance Parameter Estimates

Cov Parm Estimate Alpha Lower Upper

driver           9.3224      0.05      2.9864      130.79
car              2.9343      0.05      1.0464     24.9038
driver*car      0.01406      0.05    0.001345    3.592E17
Residual         0.1757      0.05      0.1029      0.3665

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