On 08/04/2011 08:53 AM, Belmont, John W wrote:
I am trying to correct for the effect of 2 covariates in a gene
expression data set.
design<-model.matrix(~0 + Factor + cov1 + cov2)
QUESTION: How to set up the contrast matrix?
The usual commands
fit <- lmFit(selDataMatrix, design)
cont.matrix <- makeContrasts(FacCont=Group1-Group2, levels=design)
fit2 <- contrasts.fit(fit, cont.matrix)
does not work because the original design matrix includes the
covariates.
I think I don't really understand how the contrast matrix works.
1) Unless you are sure you know what you're doing and have given this a
lot of thought, I doubt you want to remove the intercept in this model.
2) A contrast specifies the coefficients of the linear combination of
parameters that you wish to estimate/test. If you don't know what this
means, then I would advise learning more about regression or consulting
a statistician before proceeding.
3) If the first two comments haven't scared you off, then you can
specify contrasts via:
cont.matrix <- matrix(c(-1,1,0,0),ncol=1)
under the original parameterization, or
cont.matrix <- matrix(c(0,1,0,0),ncol=1)
if you take the advice of point 1) and put an intercept in your model.
--
Patrick Breheny
Assistant Professor
Department of Biostatistics
Department of Statistics
University of Kentucky
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