getVarianceComponents {variancePartition} | R Documentation |
Extract variance terms from a model fit with lm() or lmer()
getVarianceComponents(fit)
fit |
list of lmer() model fits |
variance explained by each variable
# library(variancePartition) # optional step to run analysis in parallel on multicore machines # Here, we used 4 threads library(doParallel) cl <- makeCluster(4) registerDoParallel(cl) # or by using the doSNOW package # load simulated data: # geneExpr: matrix of gene expression values # info: information/metadata about each sample data(varPartData) # Specify variables to consider # Age is continuous so we model it as a fixed effect # Individual and Tissue are both categorical, so we model them as random effects form <- ~ Age + (1|Individual) + (1|Tissue) # Fit model and extract variance in two separate steps # Step 1: fit model for each gene, store model fit for each gene in a list modelList <- fitVarPartModel( geneExpr, form, info ) fit <- modelList[[1]] getVarianceComponents( fit )