Description Usage Arguments Details Value See Also Examples
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.
1 2 3 
object 
a DESeqDataSet 
full 
the full model formula, this should be the formula in

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 zeromean normal prior on
the nonintercept 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 
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 
modelMatrixType 
either "standard" or "expanded", which describe
how the model matrix, X of the formula in 
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 
The difference in deviance is compared to a chisquared 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
.
a DESeqDataSet with new results columns accessible
with the results
function. The coefficients and standard errors are
reported on a log2 scale.
1 2 3 4 5  dds < makeExampleDESeqDataSet()
dds < estimateSizeFactors(dds)
dds < estimateDispersions(dds)
dds < nbinomLRT(dds, reduced = ~ 1)
res < results(dds)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.