caret: plot.rfe – R documentation

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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'
ggplot(
  data = NULL,
  mapping = NULL,
  metric = data$metric[1],
  output = "layered",
  ...,
  environment = NULL
)

## S3 method for class 'rfe'
plot(x, metric = x$metric, ...)

Arguments

`data`	an object of class `rfe`.
`mapping, environment`	unused arguments to make consistent with ggplot2 generic method
`metric`	What measure of performance to plot. Examples of possible values are "RMSE", "Rsquared", "Accuracy" or "Kappa". Other values can be used depending on what metrics have been calculated.
`output`	either "data", "ggplot" or "layered". The first returns a data frame while the second returns a simple `ggplot` object with no layers. The third value returns a plot with a set of layers.
`...`	`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.
`x`	an object of class `rfe`.

Details

These plots show the average performance versus the subset sizes.

Value

a lattice or ggplot object

Note

We using a recipe as an input, there may be some subset sizes that are not well-replicated over resamples. The 'ggplot' method will only show subset sizes where at least half of the resamples have associated results.

Author(s)

Max Kuhn

References

Kuhn (2008), “Building Predictive Models in R Using the caret” (http://www.jstatsoft.org/article/view/v028i05/v28i05.pdf)

Examples

## Not run: 
data(BloodBrain)

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

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)

## End(Not run)

caret

Classification and Regression Training

v6.0-86

GPL (>= 2)

Authors

Max Kuhn [aut, cre], Jed Wing [ctb], Steve Weston [ctb], Andre Williams [ctb], Chris Keefer [ctb], Allan Engelhardt [ctb], Tony Cooper [ctb], Zachary Mayer [ctb], Brenton Kenkel [ctb], R Core Team [ctb], Michael Benesty [ctb], Reynald Lescarbeau [ctb], Andrew Ziem [ctb], Luca Scrucca [ctb], Yuan Tang [ctb], Can Candan [ctb], Tyler Hunt [ctb]

Initial release