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Factor level comparisons in lme
2 messages · Martin Biuw, Douglas Bates
Martin Biuw <emb7 at st-andrews.ac.uk> writes:
Hello, I'm trying to fit a linear mixed effects model of the form: lme(y ~ x * Sex * Year, random=x|subject)
Did you mean lme(y ~ x * Sex * Year, random= ~ x|subject) The random argument should be a formula or a list.
where Sex and Year are factors with two and three levels respectively. I want to compare the fixed effects for each level to the overall mean, but the default in R is to compare to the first level. This can be changed by adding the term -1 to the righthand side of the model formula. But what I can't figure out is how to do this for both factors simultaneously. If I specify the model as: lme(y ~ x * Sex * Year-1, random=x|subject) the output gives me the fixed effects for each level of "Sex" compared to the overall mean, but still only gives me the effects of the second two levels in the "Year" factor compared to the first level. How do I specify the fixed effects structure to allow comparisons to the overall mean for each level of both factors?
This isn't really an lme question - it is a question about the
parameterization used in the model matrix for a linear model formula.
I'm not sure what you mean by "allow comparisons to the overall
mean". You may find that setting
options(contrasts=c('contr.sum', 'contr.poly'))
does what you want or what you want to do may be impossible. With two
levels of Sex and, say, 4 levels of year, the number of degrees of
freedom in the crossed factors plus intercept is
1(constant) + 1(Sex) + 3(Year) + 3(Sex * Year) = 8
If you want to get separate means for each level of Sex and Year and
Sex*Year you would need
2(Sex) + 4(Year) + 8(Sex*Year) = 14
degrees of freedom.