Build model matrices for differential expression
Description
Builds the model matrices for testing for differential expression by comparing a model with a grouping factor versus one without it. It adjusts for the confounders specified and the median coverage of each sample. The resulting models can be used in calculateStats.
Usage
1  makeModels(sampleDepths, testvars, adjustvars = NULL, testIntercept = FALSE)

Arguments
sampleDepths 
Per sample library size adjustments calculated with sampleDepth. 
testvars 
A vector or matrix specifying the variables to test. For
example, a factor with the group memberships when testing for differences
across groups. It's length should match the number of columns used from

adjustvars 
Optional matrix of adjustment variables (e.g. measured confounders, output from SVA, etc.) to use in fitting linear models to each nucleotide. These variables have to be specified by sample and the number of rows must match the number of columns used. It will also work if it is a vector of the correct length. 
testIntercept 
If 
Value
A list with two components.
 mod
The alternative model matrix.
 mod0
The null model matrix.
Author(s)
Leonardo ColladoTorres
See Also
sampleDepth, calculateStats
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Collapse the coverage information
collapsedFull < collapseFullCoverage(list(genomeData$coverage),
verbose=TRUE)
## Calculate library size adjustments
sampleDepths < sampleDepth(collapsedFull, probs=c(0.5), nonzero=TRUE,
verbose=TRUE)
## Build the models
group < genomeInfo$pop
adjustvars < data.frame(genomeInfo$gender)
models < makeModels(sampleDepths, testvars=group, adjustvars=adjustvars)
names(models)
models
