calibrate.plot: Calibration plot

In gbm: Generalized Boosted Regression Models

Calibration plot

Description

An experimental diagnostic tool that plots the fitted values versus the actual average values. Currently only available when distribution = "bernoulli".

Usage

calibrate.plot(
  y,
  p,
  distribution = "bernoulli",
  replace = TRUE,
  line.par = list(col = "black"),
  shade.col = "lightyellow",
  shade.density = NULL,
  rug.par = list(side = 1),
  xlab = "Predicted value",
  ylab = "Observed average",
  xlim = NULL,
  ylim = NULL,
  knots = NULL,
  df = 6,
  ...
)

Arguments

y	The outcome 0-1 variable.
p	The predictions estimating E(y|x).
distribution	The loss function used in creating p. bernoulli and poisson are currently the only special options. All others default to squared error assuming gaussian.
replace	Determines whether this plot will replace or overlay the current plot. replace=FALSE is useful for comparing the calibration of several methods.
line.par	Graphics parameters for the line.
shade.col	Color for shading the 2 SE region. shade.col=NA implies no 2 SE region.
shade.density	The density parameter for polygon.
rug.par	Graphics parameters passed to rug.
xlab	x-axis label corresponding to the predicted values.
ylab	y-axis label corresponding to the observed average.
xlim, ylim	x- and y-axis limits. If not specified te function will select limits.
knots, df	These parameters are passed directly to ns for constructing a natural spline smoother for the calibration curve.
...	Additional optional arguments to be passed onto plot

Details

Uses natural splines to estimate E(y|p). Well-calibrated predictions imply that E(y|p) = p. The plot also includes a pointwise 95

Value

No return values.

Author(s)

Greg Ridgeway [e-mail]

References

J.F. Yates (1982). "External correspondence: decomposition of the mean probability score," Organisational Behaviour and Human Performance 30:132-156.

D.J. Spiegelhalter (1986). "Probabilistic Prediction in Patient Management and Clinical Trials," Statistics in Medicine 5:421-433.

Examples

# Don't want R CMD check to think there is a dependency on rpart
# so comment out the example
#library(rpart)
#data(kyphosis)
#y <- as.numeric(kyphosis$Kyphosis)-1
#x <- kyphosis$Age
#glm1 <- glm(y~poly(x,2),family=binomial)
#p <- predict(glm1,type="response")
#calibrate.plot(y, p, xlim=c(0,0.6), ylim=c(0,0.6))

gbm documentation built on June 28, 2024, 9:07 a.m.