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edge (version 2.4.2)

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 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
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 gene's likelihood; default 10 means that the common likelihood is given 10 times the weight of the individual gene's likelihood in the estimation of the genewise dispersion
ntags
numeric scalar number of genes in the dataset to be analysed
der
derivative, either 0 (the function), 1 (first derivative) or 2 (second derivative)

Value

  • numeric scalar of function/derivative evaluated for the given gene and delta

Details

This function computes the weighted conditional log-likelihood for a given gene, 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 gene. The delta scale for convenience (delta is bounded between 0 and 1). Users should note that `tag' and `gene' are synonymous when interpreting the names of the arguments for this function.

Examples

Run this code
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)

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