[R] Mixed Modeling in lme4
Indrajit Sengupta
indrajitsg2013 at gmail.com
Tue Apr 30 08:26:11 CEST 2013
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
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