Lattice functions for plotting resampling results of recursive feature selection

A set of lattice functions are provided to plot the resampled performance estimates (e.g. classification accuracy, RMSE) over different subset sizes.

# S3 method for rfe
densityplot(x, data = NULL, metric = x$metric, ...)

Arguments

x	An object produced by rfe
data	This argument is not used
metric	A character string specifying the single performance metric that will be plotted
...	arguments to pass to either histogram, densityplot, xyplot or stripplot

Value

A lattice plot object

Details

By default, only the resampling results for the optimal model are saved in the rfe object. The function rfeControl can be used to save all the results using the returnResamp argument.

If leave-one-out or out-of-bag resampling was specified, plots cannot be produced (see the method argument of rfeControl)

See also

rfe, rfeControl, histogram, densityplot, xyplot, stripplot

Examples

if (FALSE) {
library(mlbench)
n <- 100
p <- 40
sigma <- 1
set.seed(1)
sim <- mlbench.friedman1(n, sd = sigma)
x <- cbind(sim$x,  matrix(rnorm(n * p), nrow = n))
y <- sim$y
colnames(x) <- paste("var", 1:ncol(x), sep = "")

normalization <- preProcess(x)
x <- predict(normalization, x)
x <- as.data.frame(x)
subsets <- c(10, 15, 20, 25)

ctrl <- rfeControl(
                   functions = lmFuncs,
                   method = "cv",
                   verbose = FALSE,
                   returnResamp = "all")

lmProfile <- rfe(x, y,
                 sizes = subsets,
                 rfeControl = ctrl)
xyplot(lmProfile)
stripplot(lmProfile)

histogram(lmProfile)
densityplot(lmProfile)
}