findMaxD2 {edgeR} | R Documentation |
Maximizes the negative binomial likelihood (a weighted version using the common likelihood given weight alpha) for each tag
findMaxD2(x, alpha = 0.5, grid = TRUE, tol = 1e-05, n.iter = 5, grid.length = 200)
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 |
list with elements lr
(likelihood ratio test), r
(estimates of 1/overdispersion), ps
(list containing proportion estimates)
Mark Robinson
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)