Calibration plot

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

Value

No return values.

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.

Author

Greg Ridgeway gregridgeway@gmail.com

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))