A basic example of ROC/AUC analysis with simulated random data.
# Get example data
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## Copyright (C) 2011-2015
## Leo Lahti and Jarkko Salojarvi
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data.directory <- system.file("extdata", package = "microbiome")
l2.log.simulated <- read.profiling(level = "L2",
data.dir = data.directory, log10 = TRUE)## Reading /home/lei/R/x86_64-pc-linux-gnu-library/3.1/microbiome/extdata/L2-frpa.tab
## Logarithmizing the data
hitchip.matrix <- l2.log.simulated
# Define two random groups for demonstration purpose
g1 <- sample(colnames(hitchip.matrix), 10)
g2 <- setdiff(colnames(hitchip.matrix), g1)
# Compare the two groups with t-test
pvalues <- c()
for (tax in rownames(hitchip.matrix)) {
pvalues[[tax]] <- t.test(hitchip.matrix[tax, g1], hitchip.matrix[tax, g2])$p.value
}
# Order the taxa based on the p-values
ordered.results <- names(sort(pvalues))
# Assume there are some known true positives (here just randomly picked for demonstration)
true.positives <- sample(rownames(hitchip.matrix), 10)
# Overall ROC analysis (this will give the cumulative TPR and FPR along the ordered list)
res <- roc(ordered.results, true.positives)
# Calculate ROC/AUC value
auc <- roc.auc(ordered.results, true.positives)
print(auc)## [1] 0.5533333
# Plot ROC curve
roc.plot(ordered.results, true.positives, line = TRUE)