I'm a SAS user who is slowly but surely migrating over to R. I'm trying to find the proper code to analyze a nested design. I have four classification variables, L (fixed), A (random within L), D (random within L), and I (random within L). The model I'm interested in is
L A(L) D(L) I(L) A:D:I(L), where the interaction is interpreted as the lack-of-fit term. I've tried variants of the lme function similar to these, lme(response~L, data, random=~Lab/(A+L+I+A:D:I), lme(response~1, data, random=~Lab/(A+L+I+A:D:I), lme(response~L, data, random=~1/(A+L+I+A:D:I). All give results different from SAS, and all give warning messages regarding either false- or non-convergence. For reference, the abbreviated SAS code is, model response = L; random A(L) D(L) I(L) A:D:I(L); Can anyone shed some light? I'd be very appreciative. Thanks. Greg Steeno LEGAL NOTICE\ Unless expressly stated otherwise, this message is ... [[dropped]] ______________________________________________ [EMAIL PROTECTED] mailing list http://www.stat.math.ethz.ch/mailman/listinfo/r-help
