gbm: calibrate.plot – R documentation

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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,
  ...
)

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))

gbm

Generalized Boosted Regression Models

v2.1.8

GPL (>= 2) | file LICENSE

Authors

Brandon Greenwell [aut, cre] (<https://orcid.org/0000-0002-8120-0084>), Bradley Boehmke [aut] (<https://orcid.org/0000-0002-3611-8516>), Jay Cunningham [aut], GBM Developers [aut] (https://github.com/gbm-developers)

Initial release