### R code from vignette source 'LPEadj.Rnw' ################################################### ### code chunk number 1: LPEadj.Rnw:259-266 ################################################### # Loading the library and null dataset (two groups with three # replicates each). library(LPEadj) dat <- matrix(rnorm(6000), ncol=6) # Applying LPE lpe.result <- lpeAdj(dat, labels=c(0,0,0,1,1,1), doMax=FALSE, doAdj=TRUE) ################################################### ### code chunk number 2: LPEadj.Rnw:275-294 ################################################### # Loading the library and null dataset (two groups with three # replicates each) library(LPEadj) dat <- matrix(rnorm(6000), ncol=6) ADJ.VALUES <- c(1, 1, 1.34585905516761 ,1.19363228146169 ,1.436849413109 ,1.289652132873 ,1.47658053092781 ,1.34382984852146 ,1.49972130857404, 1.3835405678718) # calculate base line error distributions var1 <- adjBaseOlig.error(dat[,1:3], setMax1=FALSE, q=.05) var2 <- adjBaseOlig.error(dat[,4:6], setMax1=FALSE, q=.05) # The correct variance adjustments can be fetched using the replicate # number for each group as in index for the ADJ.VALUES vector. # eg: ADJ.VALUES[n] if there are n replicates in a group results <- calculateLpeAdj(dat[,1:3],dat[,4:6],var1,var2, probe.set.name=c(1:1000), adjust1=ADJ.VALUES[3], adjust2=ADJ.VALUES[3])