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 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 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 2.1.8 Index]