library(lattice)
## count matrix
fl <- system.file(package="DirichletMultinomial", "extdata",
"Twins.csv")
count <- t(as.matrix(read.csv(fl, row.names=1)))
## phenotype
fl <- system.file(package="DirichletMultinomial", "extdata",
"TwinStudy.t")
pheno0 <- scan(fl)
lvls <- c("Lean", "Obese", "Overwt")
pheno <- factor(lvls[pheno0 + 1], levels=lvls)
names(pheno) <- rownames(count)
## count data used for cross-validation, and cross-validation
count <- csubset(c("Lean", "Obese"), count, pheno)
data(bestgrp)
## true, false positives from single-group classifier
bst <- roc(pheno[rownames(count)] == "Obese",
predict(bestgrp, count)[,"Obese"])
head(bst)
## lattice plot
xyplot(TruePostive ~ FalsePositive, bst, type="l",
xlab="False Positive", ylab="True Positive")
Run the code above in your browser using DataLab