findMaxD2 {edgeR}R Documentation

Maximizes the negative binomial likelihood

Description

Maximizes the negative binomial likelihood (a weighted version using the common likelihood given weight alpha) for each tag

Usage

 
findMaxD2(x, alpha = 0.5, grid = TRUE, tol = 1e-05, n.iter = 5, grid.length = 200)

Arguments

x list with elements data, lib.size and group
alpha weight given to common likelihood, set to 0 for individual estimates or large (e.g. 100) for common likelihood
grid logical, whether to use a grid search (default = TRUE); if FALSE use Newton-Rhapson steps
tol if grid=FALSE, tolerance for Newton-Rhapson iterations
n.iter if grid=FALSE, number of Newton-Rhapson iterations
grid.length length of the grid to maximize over; default 200

Value

list with elements lr (likelihood ratio test), r (estimates of 1/overdispersion), ps (list containing proportion estimates)

Author(s)

Mark Robinson

Examples

y<-matrix(rnbinom(1000,mu=10,size=2),ncol=4)
d<-list(data=y,group=c(1,1,2,2),lib.size=c(1000:1003))
cml1<-findMaxD2(d,alpha=10)
cml2<-findMaxD2(d,alpha=0)

[Package edgeR version 1.0.4 Index]