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 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
