R: Calibration plot

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

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 band.

Value

calibrate.plot returns no values.

Author(s)

Greg Ridgeway gregridgeway@gmail.com

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))

[Package gbm version 2.1 Index]