caret (version 6.0-93)

calibration: Probability Calibration Plot

Description

For classification models, this function creates a 'calibration plot' that describes how consistent model probabilities are with observed event rates.

Usage

calibration(x, ...)
# S3 method for default
calibration(x, ...)
# S3 method for formula
calibration(
  x,
  data = NULL,
  class = NULL,
  cuts = 11,
  subset = TRUE,
  lattice.options = NULL,
  ...
)
# S3 method for calibration
print(x, ...)
# S3 method for calibration
xyplot(x, data = NULL, ...)
# S3 method for calibration
ggplot(data, ..., bwidth = 2, dwidth = 3)

Value

calibration.formula returns a list with elements:

data

the data used for plotting

cuts

the number of cuts

class

the event class

probNames

the names of the model probabilities

xyplot.calibration returns a lattice object

Arguments

x

a lattice formula (see xyplot for syntax) where the left -hand side of the formula is a factor class variable of the observed outcome and the right-hand side specifies one or model columns corresponding to a numeric ranking variable for a model (e.g. class probabilities). The classification variable should have two levels.

...

options to pass through to xyplot or the panel function (not used in calibration.formula).

data

For calibration.formula, a data frame (or more precisely, anything that is a valid envir argument in eval, e.g., a list or an environment) containing values for any variables in the formula, as well as groups and subset if applicable. If not found in data, or if data is unspecified, the variables are looked for in the environment of the formula. This argument is not used for xyplot.calibration. For ggplot.calibration, data should be an object of class "calibration"."

class

a character string for the class of interest

cuts

If a single number this indicates the number of splits of the data are used to create the plot. By default, it uses as many cuts as there are rows in data. If a vector, these are the actual cuts that will be used.

subset

An expression that evaluates to a logical or integer indexing vector. It is evaluated in data. Only the resulting rows of data are used for the plot.

lattice.options

A list that could be supplied to lattice.options

bwidth, dwidth

a numeric value for the confidence interval bar width and dodge width, respectively. In the latter case, a dodge is only used when multiple models are specified in the formula.

Author

Max Kuhn, some lattice code and documentation by Deepayan Sarkar

Details

calibration.formula is used to process the data and xyplot.calibration is used to create the plot.

To construct the calibration plot, the following steps are used for each model:

1. The data are split into cuts - 1 roughly equal groups by their class probabilities

2. the number of samples with true results equal to class are determined

3. the event rate is determined for each bin

xyplot.calibration produces a plot of the observed event rate by the mid-point of the bins.

This implementation uses the lattice function xyplot, so plot elements can be changed via panel functions, trellis.par.set or other means. calibration uses the panel function panel.calibration by default, but it can be changed by passing that argument into xyplot.calibration.

The following elements are set by default in the plot but can be changed by passing new values into xyplot.calibration: xlab = "Bin Midpoint", ylab = "Observed Event Percentage", type = "o", ylim = extendrange(c(0, 100)),xlim = extendrange(c(0, 100)) and panel = panel.calibration

For the ggplot method, confidence intervals on the estimated proportions (from binom.test) are also shown.

See Also

xyplot, trellis.par.set

Examples

if (FALSE) {
data(mdrr)
mdrrDescr <- mdrrDescr[, -nearZeroVar(mdrrDescr)]
mdrrDescr <- mdrrDescr[, -findCorrelation(cor(mdrrDescr), .5)]


inTrain <- createDataPartition(mdrrClass)
trainX <- mdrrDescr[inTrain[[1]], ]
trainY <- mdrrClass[inTrain[[1]]]
testX <- mdrrDescr[-inTrain[[1]], ]
testY <- mdrrClass[-inTrain[[1]]]

library(MASS)

ldaFit <- lda(trainX, trainY)
qdaFit <- qda(trainX, trainY)

testProbs <- data.frame(obs = testY,
                        lda = predict(ldaFit, testX)$posterior[,1],
                        qda = predict(qdaFit, testX)$posterior[,1])

calibration(obs ~ lda + qda, data = testProbs)

calPlotData <- calibration(obs ~ lda + qda, data = testProbs)
calPlotData

xyplot(calPlotData, auto.key = list(columns = 2))
}