calibrate.plot {gbm} | R Documentation |
An experimental diagnostic tool that plots the fitted values versus the actual average values.
Currently developed for only distribution="bernoulli"
.
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, ...)
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 |
... |
other graphics parameters passed on to the plot function |
Uses gam
to estimate E(y|p). Well-calibrated predictions
imply that E(y|p) = p. The plot also includes a pointwise 95
band.
calibrate.plot
returns no values.
Greg Ridgeway gregr@rand.org
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.
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))