nbinomLRT {DESeq2} | R Documentation

## 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, betaTol = 1e-08,
      maxit = 100, useOptim = TRUE, quiet = FALSE, useQR = TRUE,
      minmu = 0.5)

### 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
`betaTol` | control parameter defining convergence
`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
`minmu` | lower bound on the estimated count while fitting the GLM

### 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`.

### Value

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

### See Also

`DESeq`, `nbinomWaldTest`

### Examples

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

[Package DESeq2 version 1.20.0 Index]