plotRBPCurve: Plot residual-based predictiveness (RBP) curve.

In RBPcurve: The Residual-Based Predictiveness Curve

Plot residual-based predictiveness (RBP) curve.

Description

plots the RBP curve

Usage

plotRBPCurve(
  obj,
  main = "RBP Curve",
  xlab = "Cumulative Percentage",
  ylab = "Estimated Residuals",
  type = "l",
  ylim = c(-1, 1.2),
  x.adj = c(NA, -0.5),
  y.adj = c(NA, NA),
  cond.axis = FALSE,
  title.line = ifelse(cond.axis, 3, 2),
  add = FALSE,
  ...
)

Arguments

obj	[RBPObj] Data container for RBP curve.
main	[character(1)] An overall title for the plot.
xlab	[character(1)] Label for X-axis. Default is “Cumulative Percentage”.
ylab	[character(1)] Label for Y-axis. Default is “Estimated Residuals”.
type	[character(1)] The plot type that should be drawn, see plot for all possible types. Default is type = "l" for lines.
ylim	[numeric(2)] Limits for Y-axis. Default is c(-1, 1.1).
x.adj	[numeric(2)] Adjustment for the X-axis.
y.adj	[numeric(2)] Adjustment for the Y-axis.
cond.axis	[logical(1)] Should an additional axis be plotted reflecting residuals conditional on y? Default is FALSE.
title.line	[integer(1)] Where to plot the title, see title.
add	[logical(1)] Should RBP plot be added to current plot? Default is FALSE.
...	[any] Passed to plot or lines, depending on add.

Examples

# Download data
mydata = getTaskData(pid.task)
head(mydata)

# Build logit model and plot RBP curve
mylogit <- glm(diabetes ~ ., data = mydata, family = "binomial")
y = mydata$diabetes
pred1 = predict(mylogit, type="response")
obj1 = makeRBPObj(pred1, y)
plotRBPCurve(obj1, cond.axis = TRUE, type = "b")

## Not run: 
# Build logit model using mlr and plot RBP curve
task = pid.task
lrn = makeLearner("classif.logreg", predict.type = "prob")
tr = train(lrn, task)
pred2 = getPredictionProbabilities(predict(tr, task))
obj2 = makeRBPObj(pred2, y)
plotRBPCurve(obj2, cond.axis = TRUE, type = "b", col = 2)

## End(Not run)

RBPcurve documentation built on April 12, 2025, 1:27 a.m.