You can reproduce the problem by having a data.frame (or anything else) in your
environment:
new - data.frame(ï..VAR1 = rep(c(TRUE,NA,FALSE), c(10,2,8)),
random=rep(1:3,len=20), clustno=rep(c(1:5),len=20),
validatedRS6=rep(0:1,len=20))
model1- glmer(validatedRS6 ~ random + (1|clustno),
You can reproduce the problem by having a data.frame (or anything else) in
your
environment:
I left out called 'new' in the above statement. The example is correct.
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-Original Message-
From: r-help-boun...@r-project.org
William Dunlap wdunlap at tibco.com writes:
You can reproduce the problem by having a data.frame (or anything
else) in your environment: I left out called 'new' in the
above statement. The example is correct.
Bill Dunlap
Spotfire, TIBCO Software
wdunlap tibco.com
-Original
On Nov 5, 2013, at 3:36 PM, EmmaB wrote:
I am running a multi-level model. I use the following commands with
validatedRS6 as the outcome, random as the predictor and clustno as the
random effects variable.
new-as.data.frame(read.delim(BABEX.dat, header=TRUE))
install.packages(lme4)
str(new)
'data.frame': 1214 obs. of 4 variables:
$ ï..VAR1 : logi NA NA NA NA NA NA ...
$ random : int 1 1 1 1 1 1 1 1 1 1 ...
$ clustno : int 1 1 1 1 1 1 1 1 1 1 ...
$ validatedRS6: int 0 0 0 0 0 0 0 0 0 0 ...
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