[R] Using lmer with huge amount of data

[Gang Chen]

Based on the examples I've seen in using statistical analysis  
packages such as lmer, it seems that people usually tabulate all the  
input data into one file with the first line indicating the variable  
names (or labels), and then read the file inside R. However, in my  
case I can't do that because of the huge amount of imaging data.

Suppose I have a one-way within-subject ANCOVA with one covariate,  
and I would like to use lmer in R package lme4 to analyze the data.  
In the terminology of linear mixed models, I have a fixed factor A  
with 3 levels, a random factor B (subject), and a covariate (age)  
with a model like this

MyResult <- lmer(Response ~ FactorA + Age + (1 | subject), MyData, ...)

My input data are like this: For each subject I have a file (a huge  
matrix) storing the response values of the subject at many locations  
(~30,000 voxels) corresponding to factor A at the 1st level, another  
file for factor A at the 2nd level, and a 3rd file for factor A at  
the 3rd level. Then I have another file storing the age of those  
subjects. The analysis with the linear mixed model above would be  
done at each voxel separately.

It seems impractical to create one gigantic file or matrix to feed  
into the above command line because of the big number of voxels. I'm  
not sure how to proceed in this case. Any suggestions would be highly  
appreciated.

Also if I'm concerned about any potential violation of sphericity  
among the 3 levels of factor A, how can I test sphericity violation  
in lmer? And if violation exists, how can I make corrections in  
contrast testing?

Thank you very much,
Gang