extractVarPart {variancePartition} | R Documentation

## Extract variance statistics

### Description

Extract variance statistics from list of models fit with lm() or lmer()

### Usage

    extractVarPart(
      modelList,
      adjust = NULL,
      adjustAll = FALSE,
      showWarnings = TRUE,
      ...
    )

### Arguments

`modelList` | list of lmer() model fits
`adjust` | remove variation from specified variables from the denominator. This computes the adjusted ICC with respect to the specified variables
`adjustAll` | adjust for all variables. This computes the adjusted ICC with respect to all variables. This overrides the previous argument, so all variables are include in adjust.
`showWarnings` | show warnings about model fit (default TRUE)
`...` | other arguments

### Value

data.frame of fraction of variance explained by each variable, after correcting for all others.

### Examples

    # library(variancePartition)

    # optional step to run analysis in parallel on multicore machines
    # Here, we used 4 threads
    library(doParallel)
    cl <- makeCluster(4)
    registerDoParallel(cl)
    on.exit(stopCluster(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)

    # Step 1: fit linear mixed model on gene expresson
    # If categoritical variables are specified, a linear mixed model is used
    # If all variables are modeled as continuous, a linear model is used
    # each entry in results is a regression model fit on a single gene
    # Step 2: extract variance fractions from each model fit
    # for each gene, returns fraction of variation attributable to each variable
    # Interpretation: the variance explained by each variable
    # after correction for all other variables
    varPart <- fitExtractVarPartModel( geneExpr, form, info )

    # violin plot of contribution of each variable to total variance
    plotVarPart( sortCols( varPart ) )

    # Advanced:
    # 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
    results <- fitVarPartModel( geneExpr, form, info )

    # Step 2: extract variance fractions
    varPart <- extractVarPart( results )

[Package variancePartition version 1.16.1 Index]