calibrate.plot {gbm}R Documentation

Calibration plot

Description

An experimental diagnostic tool that plots the fitted values versus the actual average values. Currently developed for only 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,
               ...)

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
... other graphics parameters passed on to the plot function

Details

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

Value

calibrate.plot returns no values.

Author(s)

Greg Ridgeway gregr@rand.org

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

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))

[Package gbm version 1.5 Index]