edgeR (version 3.8.6)

weightedCondLogLikDerDelta: Weighted Conditional Log-Likelihood in Terms of Delta

Description

Weighted conditional log-likelihood parameterized in terms of delta (phi / (phi+1)) for a given tag/gene - maximized to find the smoothed (moderated) estimate of the dispersion parameter

Usage

weightedCondLogLikDerDelta(y, delta, tag, prior.n=10, ntags=nrow(y[[1]]), der=0)

Arguments

y

list with elements comprising the matrices of count data (or pseudocounts) for the different groups

delta

delta (phi / (phi+1))parameter of negative binomial

tag

tag/gene at which the weighted conditional log-likelihood is evaluated

prior.n

smoothing paramter that indicates the weight to put on the common likelihood compared to the individual tag's likelihood; default 10 means that the common likelihood is given 10 times the weight of the individual tag/gene's likelihood in the estimation of the tag/genewise dispersion

ntags

numeric scalar number of tags/genes in the dataset to be analysed

der

derivative, either 0 (the function), 1 (first derivative) or 2 (second derivative)

Value

Details

This function computes the weighted conditional log-likelihood for a given tag, parameterized in terms of delta. The value of delta that maximizes the weighted conditional log-likelihood is converted back to the phi scale, and this value is the estimate of the smoothed (moderated) dispersion parameter for that particular tag. The delta scale for convenience (delta is bounded between 0 and 1).

Examples

counts<-matrix(rnbinom(20,size=1,mu=10),nrow=5)
d<-DGEList(counts=counts,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2))
y<-splitIntoGroups(d)
ll1<-weightedCondLogLikDerDelta(y,delta=0.5,tag=1,prior.n=10,der=0)
ll2<-weightedCondLogLikDerDelta(y,delta=0.5,tag=1,prior.n=10,der=1)