For classification models, this function creates a 'calibration plot' that describes how consistent model probabilities are with observed event rates.
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)
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).
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"."
a character string for the class of interest
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.
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.
A list that could be supplied to lattice.options
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.
calibration.formula returns a list with elements:
the data used for plotting
the number of cuts
the event class
the names of the model probabilities
xyplot.calibration returns a lattice object
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:
The data are split into cuts - 1 roughly equal groups by their class probabilities
the number of samples with true results equal to class are determined
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.
# NOT RUN {
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))
# }
# NOT RUN {
# }