PRPlot | R Documentation |
Plot Precision-Recall plot.
PRPlot(frame, xvar, truthVar, truthTarget, title, ..., estimate_sig = FALSE)
frame |
data frame to get values from |
xvar |
name of the independent (input or model) column in frame |
truthVar |
name of the dependent (output or result to be modeled) column in frame |
truthTarget |
value we consider to be positive |
title |
title to place on plot |
... |
no unnamed argument, added to force named binding of later arguments. |
estimate_sig |
logical, if TRUE compute significance |
See https://www.nature.com/articles/nmeth.3945 for a discussion of precision and recall, and how the precision/recall plot relates to the ROC plot.
In addition to plotting precision versus recall, PRPlot
reports the best
achieved F1 score, and plots an isoline corresponding to that F1 score.
ROCPlot
if (requireNamespace('data.table', quietly = TRUE)) {
# don't multi-thread during CRAN checks
data.table::setDTthreads(1)
}
set.seed(34903490)
x = rnorm(50)
y = 0.5*x^2 + 2*x + rnorm(length(x))
frm = data.frame(x=x,y=y,yC=y>=as.numeric(quantile(y,probs=0.8)))
frm$absY <- abs(frm$y)
frm$posY = frm$y > 0
frm$costX = 1
WVPlots::PRPlot(frm, "x", "yC", TRUE, title="Example Precision-Recall plot")
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