DESeq2 (version 1.12.3)

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


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.


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


a DESeqDataSet
the full model formula, this should be the formula in design(object). alternatively, can be a matrix
a reduced formula to compare against, e.g. the full model with a term or terms of interest removed. alternatively, can be a matrix
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.
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
the maximum number of iterations to allow for convergence of the coefficient vector
whether to use the native optim function on rows which do not converge within maxit
whether to print messages at each step
whether to use the QR decomposition on the design matrix X while fitting the GLM


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


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.

See Also

DESeq, nbinomWaldTest


Run this code

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

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