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pamr.fdr

A function to estimate false discovery rates for the nearest shrunken centroid classifier


Description

A function to estimate false discovery rates for the nearest shrunken centroid classifier

Usage

pamr.fdr(trained.obj, data,  nperms=100, 
 xl.mode=c("regular","firsttime","onetime","lasttime"),xl.time=NULL, xl.prevfit=NULL)

Arguments

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

Details

pamr.fdr estimates false discovery rates for a nearest shrunken centroid classifier

Value

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

Author(s)

Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu

Examples

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)

pamr

Pam: Prediction Analysis for Microarrays

v1.56.1
GPL-2
Authors
T. Hastie, R. Tibshirani, Balasubramanian Narasimhan, Gil Chu
Initial release

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