plotRBPCurve: Plot residual-based predictiveness (RBP) curve.
In RBPcurve: The Residual-Based Predictiveness Curve

Description Usage Arguments Examples

View source: R/plotRBPCurve.R

plots the RBP curve

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, ...)

`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`.

# 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)