RBPcurve (version 1.3)

plotRBPCurve: 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")

if (FALSE) {
# 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)
}