caret (version 6.0-24)

plot.rfe: Plot RFE Performance Profiles

Description

These functions plot the resampling results for the candidate subset sizes evaluated during the recursive feature elimination (RFE) process

Usage

## S3 method for class 'rfe':
plot(x, metric = x$metric,  ...)
## S3 method for class 'rfe':
ggplot(data = NULL, metric = data$metric[1], output = "layered", ...)

Arguments

x

an object of class rfe.

metric

What measure of performance to plot. Examples of possible values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be used depending n what metrics have been calculated.

...

plot only: specifications to be passed to xyplot. The function automatically sets some arguments (e.g. axis labels) but passing in values here will over-ride the defaults.

data

an object of class rfe.

output

either "data", "ggplot" or "layered". The first returns a data frame while the second retruns a simple ggplot object with no layers. The third value returns a plot with a set of layers.

Value

• a lattice or ggplot object

Details

These plots show the average performance versus the subset sizes.

References

Kuhn (2008), ``Building Predictive Models in R Using the caret'' (http://www.jstatsoft.org/v28/i05/)

See Also

rfe, xyplot, ggplot

Examples

data(BloodBrain)

x <- scale(bbbDescr[,-nearZeroVar(bbbDescr)])
x <- x[, -findCorrelation(cor(x), .8)]
x <- as.data.frame(x)

set.seed(1)
lmProfile <- rfe(x, logBBB,
                 sizes = c(2:25, 30, 35, 40, 45, 50, 55, 60, 65),
                 rfeControl = rfeControl(functions = lmFuncs, 
                                         number = 200))
plot(lmProfile)
plot(lmProfile, metric = "Rsquared")
ggplot(lmProfile)