findMaxD2 {edgeR} | R Documentation |
Maximizes the negative binomial likelihood (a weighted version using the common likelihood given weight alpha) for each tag
findMaxD2(object, alpha = 0.5, grid = TRUE, tol = 1e-05, n.iter = 10, grid.length = 200)
object |
list containing the raw data with elements data (table of counts), group (vector indicating group) and lib.size (vector of library sizes) |
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 over which to maximize; default 200 |
vector of the values of delta that maximize the negative binomial likelihood for each tag (where delta = phi / (phi+1)
and phi
is the overdispersion parameter)
Mark Robinson, Davis McCarthy
y<-matrix(rnbinom(1000,mu=10,size=2),ncol=4) d<-DGEList(data=y,group=c(1,1,2,2),lib.size=c(1000:1003)) cml1<-findMaxD2(d,alpha=10) cml2<-findMaxD2(d,alpha=0)