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 2.1.3 Index]