adjustedProfileLik(dispersion, y, design, offset, weights=NULL, adjust=TRUE, start=NULL, get.coef=FALSE)y giving offsets for the log-linear models. Can be a scalor or a vector of length ncol(y), in which case it is expanded out to a matrix.TRUE then Cox-Reid adjustment is made to the log-likelihood, if FALSE then the log-likelihood is returned without adjustment.glmFit.get.coef==FALSE, a vector of adjusted profile log-likelihood values is returned containing one element for each row of y.Otherwise, a list is returned containing apl, the aforementioned vector of adjusted profile likelihoods; and beta, a numeric matrix of fitted GLM coefficients.
The adjusted profile likelihood is an approximate log-likelihood for the dispersion parameter, conditional on the estimated values of the coefficients in the NB log-linear models. The conditional likelihood approach is a technique for adjusting the likelihood function to allow for the fact that nuisance parameters have to be estimated in order to evaluate the likelihood. When estimating the dispersion, the nuisance parameters are the coefficients in the linear model.
This implementation calls the LAPACK library to perform the Cholesky decomposition during adjustment estimation.
The purpose of start and get.coef is to allow hot-starting for multiple calls to adjustedProfileLik, when only the dispersion is altered.
Specifically, the returned GLM coefficients from one call with get.coef==TRUE can be used as the start values for the next call.
McCarthy, DJ, Chen, Y, Smyth, GK (2012). Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Research 40, 4288-4297. http://nar.oxfordjournals.org/content/40/10/4288
glmFit
y <- matrix(rnbinom(1000, mu=10, size=2), ncol=4)
design <- matrix(1, 4, 1)
dispersion <- 0.5
apl <- adjustedProfileLik(dispersion, y, design, offset=0)
apl
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