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 = if (type == "glmGamPoi") 1e-06 else 0.5,
      type = c("DESeq2", "glmGamPoi")
    )

### 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
`type` | either "DESeq2" or "glmGamPoi". If `type = "DESeq2"` a classical likelihood ratio test based on the Chi-squared distribution is conducted. If `type = "glmGamPoi"` and previously the dispersion has been estimated with glmGamPoi as well, a quasi-likelihood ratio test based on the F-distribution is conducted. It is supposed to be more accurate, because it takes the uncertainty of dispersion estimate into account in the same way that a t-test improves upon a Z-test.

### 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 1.32.0 Index]