calibrate.plot: 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,
  ...
)

Value

No return values.

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

Author

Greg Ridgeway gregridgeway@gmail.com

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

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

Run this code

# 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))

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