A function to estimate false discovery rates for the nearest shrunken centroid classifier
A function to estimate false discovery rates for the nearest shrunken centroid classifier
pamr.fdr(trained.obj, data, nperms=100,
xl.mode=c("regular","firsttime","onetime","lasttime"),xl.time=NULL, xl.prevfit=NULL)trained.obj |
The result of a call to pamr.train |
data |
Data object; same as the one passed to pamr.train |
nperms |
Number of permutations for estimation of FDRs. Default is 100 |
xl.mode |
Used by Excel interface |
xl.time |
Used by Excel interface |
xl.prevfit |
Used by Excel interface |
pamr.fdr estimates false discovery rates for a nearest shrunken
centroid classifier
A list with components:
results |
Matrix of estimates FDRs for various various threshold values. Reported are both the median and 90th percentile of the FDR over permutations |
pi0 |
The estimated proportion of genes that are null, i.e. not significantly different |
Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu
suppressWarnings(RNGversion("3.5.0"))
set.seed(120)
x <- matrix(rnorm(1000*20),ncol=20)
y <- sample(c(1:4),size=20,replace=TRUE)
mydata <- list(x=x,y=factor(y), geneid=as.character(1:nrow(x)),
genenames=paste("g",as.character(1:nrow(x)),sep=""))
mytrain <- pamr.train(mydata)
myfdr <- pamr.fdr(mytrain, mydata)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.