On Nov 7, 2011, at 10:07 PM, array chip wrote:
Thanks David. The only category that has no cases is "treat 1-group
2":
> with(test,table(treat,group))
group
treat 1 2
1 8 0
2 1 5
3 5 5
4 7 3
5 7 4
6 3 3
7 8 2
But why the coefficient for "treat 7-group 2" is not estimable?
Well, it had to omit one of them didn't it?
(But I don't know why that level was chosen.)
--
David.
Thanks
John
From: David Winsemius <dwinsem...@comcast.net>
To: array chip <arrayprof...@yahoo.com>
Cc: "r-help@r-project.org" <r-help@r-project.org>
Sent: Monday, November 7, 2011 5:13 PM
Subject: Re: [R] why NA coefficients
On Nov 7, 2011, at 7:33 PM, array chip wrote:
> Hi, I am trying to run ANOVA with an interaction term on 2 factors
(treat has 7 levels, group has 2 levels). I found the coefficient
for the last interaction term is always 0, see attached dataset and
the code below:
>
>> test<-read.table("test.txt",sep='\t',header=T,row.names=NULL)
>> lm(y~factor(treat)*factor(group),test)
>
> Call:
> lm(formula = y ~ factor(treat) * factor(group), data = test)
>
> Coefficients:
> (Intercept)
factor(treat)2 factor(treat)3
> 0.429244
0.499982 0.352971
> factor(treat)4
factor(treat)5 factor(treat)6
> -0.204752
0.142042 0.044155
> factor(treat)7 factor(group)2
factor(treat)2:factor(group)2
> -0.007775
-0.337907 -0.208734
> factor(treat)3:factor(group)2 factor(treat)4:factor(group)2
factor(treat)5:factor(group)2
> -0.195138
0.800029 0.227514
> factor(treat)6:factor(group)2 factor(treat)7:factor(group)2
> 0.331548 NA
>
>
> I guess this is due to model matrix being singular or collinearity
among the matrix columns? But I can't figure out how the matrix is
singular in this case? Can someone show me why this is the case?
Because you have no cases in one of the crossed categories.
--David Winsemius, MD
West Hartford, CT
David Winsemius, MD
West Hartford, CT
______________________________________________
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.
