gbm (version 1.3-5)

calibrate.plot: 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

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

Value

• calibrate.plot returns no values.

Details

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

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))