approx.expected.info {edgeR} | R Documentation |
Using a linear fit (for simplicity), the expected information from the conditional log likelihood of the dispersion parameter of the negative binomial is calculated over all genes.
approx.expected.info(object, d, pseudo, robust = FALSE)
object |
|
d |
numeric vector giving the delta parameter for negative binomial - |
pseudo |
numeric matrix of pseudocounts from output of |
robust |
logical on whether to use a robust fit, default |
numeric vector of approximate values of the Fisher information for each tag/transcript (with length same as the number of rows of the original counts)
Mark Robinson
This function is used in the algorithm for estimating an appropriate amount of smoothing for the dipsersion estimates carried out by estimateSmoothing
.
set.seed(0) y<-matrix(rnbinom(40,size=1,mu=10),ncol=4) d<-DGEList(counts=y,group=rep(1:2,each=2),lib.size=rep(c(1000:1001),2)) d<-estimateCommonDisp(d) d<-estimateTagwiseDisp(d,prior.n=10) exp.inf<-approx.expected.info(d,1/(1 + d$common.dispersion),d$pseudo.alt)