WVPlots (version 1.3.0)

PRTPlot: Plot Precision-Recall or Enrichment-Recall as a function of threshold.

Description

Plot classifier performance metrics as a function of threshold.

Usage

PRTPlot(
  frame,
  predVar,
  truthVar,
  truthTarget,
  title,
  ...,
  plotvars = c("precision", "recall"),
  thresholdrange = c(-Inf, Inf),
  linecolor = "black"
)

Arguments

frame

data frame to get values from

predVar

name of the column of predicted scores

truthVar

name of the column of actual outcomes 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.

plotvars

variables to plot, must be at least one of the measures listed below. Defaults to c("precision", "recall")

thresholdrange

range of thresholds to plot.

linecolor

line color for the plot

Details

For a classifier, the precision is what fraction of predicted positives are true positives; the recall is what fraction of true positives the classifier finds, and the enrichment is the ratio of classifier precision to the average rate of positives. Plotting precision-recall or enrichment-recall as a function of classifier score helps identify a score threshold that achieves an acceptable tradeoff between precision and recall, or enrichment and recall.

In addition to precision/recall, PRTPlot can plot a number of other metrics:

• precision: fraction of predicted positives that are true positives

• recall: fraction of true positives that were predicted to be true

• enrichment: ratio of classifier precision to prevalence of positive class

• sensitivity: the same as recall (also known as the true positive rate)

• specificity: fraction of true negatives to all negatives (or 1 - false_positive_rate)

• false_positive_rate: fraction of negatives predicted to be true over all negatives

For example, plotting sensitivity/false_positive_rate as functions of threshold will "unroll" an ROC Plot.

Plots are in a single column, in the order specified by plotvars.

See Also

ThresholdPlot, ROCPlot

Examples

# NOT RUN {
df <- iris
df$isVersicolor <- with(df, Species=='versicolor')
model = glm(isVersicolor ~ Petal.Length + Petal.Width + Sepal.Length + Sepal.Width,
            data=df, family=binomial)
df$pred = predict(model, newdata=df, type="response")

WVPlots::PRTPlot(df, "pred", "isVersicolor", TRUE, title="Example Precision-Recall threshold plot")

WVPlots::PRTPlot(df, "pred", "isVersicolor", TRUE,
                 plotvars = c("sensitivity", "specificity", "false_positive_rate"),
                 title="Sensitivity/specificity/FPR as functions of threshold")

# }