Hi All, I am trying to shift from running mixed models in SAS using PROC MIXED to using lme4 package in R. In trying to match the coefficients of R output to that of SAS output, I came across this problem.
The dataset I am using is this one: http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect034.htm If I run the following code: proc mixed data=rc method=ML covtest; class Batch; model Y = Month / s; random Int Month / type=cs sub=Batch s; run; The Fixed effect coefficients match with that of R. But the random effect does not. Here is the R code: rc <- read.table('rc.csv', sep = ',', header=T, na.strings=".") m1 <- lmer(formula = Y ~ Month + (Month|Batch), data = rc, REML = F) summary(m1) fixef(m1) ranef(m1) But if I change the SAS code as follows: proc mixed data=rc method=ML covtest; class Batch; model Y = Month / s; random Int / type=cs sub=Batch s; run; and the R code as follows: m2 <- lmer(formula = Y ~ Month + (1|Batch), data = rc, REML = F) summary(m2) fixef(m2) ranef(m2) both fixed and random effect coefficients match. I am unable to understand this discrepancy. Am I wrongly specifying the model in the first case? It would be helpful if someone can throw some light on this. Regards, Indrajit ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
