Description Usage Arguments Details Value Author(s) References Examples
View source: R/calibrate.plot.R
An experimental diagnostic tool that plots the fitted values versus the actual average values.
Currently developed for only distribution="bernoulli"
.
1 2 3 4 5 6 7 8 9 10 11 12 |
y |
the outcome 0-1 variable |
p |
the predictions estimating E(y|x) |
distribution |
the loss function used in creating |
replace |
determines whether this plot will replace or overlay the current plot.
|
line.par |
graphics parameters for the line |
shade.col |
color for shading the 2 SE region. |
shade.density |
the |
rug.par |
graphics parameters passed to |
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 the function will select limits |
knots,df |
these parameters are passed directly to
|
... |
other graphics parameters passed on to the plot function |
Uses natural splines 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 gregridgeway@gmail.com
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.
1 2 3 4 5 6 7 8 9 | # 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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