plotROC {Anaquin} | R Documentation |
Create receiver operating characteristic (ROC) plot at various threshold settings.
plotROC(seqs, score, group, label, refGroup, title, legTitle)
seqs |
Sequin names |
score |
How to rank ROC points |
group |
How to group ROC points |
label |
True-positive (TP) or false positive (FP) |
refGroup |
Reference ratio groups |
title |
Label of the plot. Default to |
legTitle |
Title of the legend. Default to |
Create a receiver operating characteristic (ROC) plot at various threshold settings. The true positive rate (TPR) is plotted on the x-axis and false positive rate (FPR) is plotted on the y-axis.
The function requires a scoring threshold function, and illustrates the performance of the data as the threshold is varied. Common scoring threshold include p-value, sequencing depth and allele frequency, etc.
ROC plot is a useful diagnostic performance tool; it provides tools to select possibly optimal models and to discard suboptimal ones. In particularly, the AUC statistics indicate the performance of the model relatively to a random experiment (AUC 0.5).
The function prints ROC plot and return it's AUC statistics.
Ted Wong t.wong@garvan.org.au
library(Anaquin) # # Data set generated by DESeq2 and Anaquin. described in Section 5.6.3.3 of # the user guide. # data(UserGuideData_5.6.3) # Sequin names seqs <- row.names(UserGuideData_5.6.3) # Expected log-fold group <- abs(UserGuideData_5.6.3$ExpLFC) # How the ROC curves are ranked score <- 1-UserGuideData_5.6.3$Pval # Classified labels (TP/FP) label <- UserGuideData_5.6.3$Label plotROC(seqs, score, group, label, title='ROC Plot', refGroup=0)