nbinomLRT: Likelihood ratio test (chi-squared test) for GLMs

Description

This function tests for significance of change in deviance between a full and reduced model which are provided as formula. Fitting uses previously calculated sizeFactors (or normalizationFactors) and dispersion estimates.

Usage

nbinomLRT(object, full = design(object), reduced, betaPrior = FALSE,
  betaPriorVar, maxit = 100, useOptim = TRUE, quiet = FALSE,
  useQR = TRUE)

Arguments

object

a DESeqDataSet

full

the full model formula, this should be the formula in design(object). alternatively, can be a matrix

reduced

a reduced formula to compare against, e.g. the full model with a term or terms of interest removed. alternatively, can be a matrix

betaPrior

whether or not to put a zero-mean normal prior on the non-intercept coefficients While the beta prior is used typically, for the Wald test, it can also be specified for the likelihood ratio test. For more details on the calculation, see nbinomWaldTest.

betaPriorVar

a vector with length equal to the number of model terms including the intercept. which if missing is estimated from the rows which do not have any zeros

maxit

the maximum number of iterations to allow for convergence of the coefficient vector

useOptim

whether to use the native optim function on rows which do not converge within maxit

quiet

whether to print messages at each step

useQR

whether to use the QR decomposition on the design matrix X while fitting the GLM

Value

a DESeqDataSet with new results columns accessible with the results function. The coefficients and standard errors are reported on a log2 scale.

Details

The difference in deviance is compared to a chi-squared distribution with df = (reduced residual degrees of freedom - full residual degrees of freedom). This function is comparable to the nbinomGLMTest of the previous version of DESeq and an alternative to the default nbinomWaldTest.

Examples

Run this code

dds <- makeExampleDESeqDataSet()
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
dds <- nbinomLRT(dds, reduced = ~ 1)
res <- results(dds)