plot.train: Plot Method for the train Class

Description

This function takes the output of a train object and creates a line or level plot using the lattice library.

Usage

## S3 method for class 'train':
plot(x, 
     plotType = "scatter",
     metric = x$perfNames[1],
     digits = getOption("digits") - 5, 
     xTrans = NULL, 
     ...)

Arguments

an object of class train.

metric

What measure of performance to plot. Possible values are "RMSE", "Rsquared", "Accuracy" or "Kappa"

plotType

a string describing the type of plot ("scatter", "level" or "line")

digits

an integer specifying the number of significant digits used to label the parameter value.

xTrans

a fuction that will be used to scale the x-axis in scatter plots.

...

specifications to be passed to levelplot or bwplot (for line plots). These values should not be axis labels, panel functions, or ti

Details

If there are no tuning parameters, or none were varied, a plot of the resampling distirbution is produced via resampleHist. If the model has one tuning parameter with multiple candidate values, a plot is produced showing the profile of the results over the parameter. Also, a plot can be produced if there are multiple tuning parameters but only one is varied.

If there are two tuning parameters with different values, a plot can be produced where a different line is shown for each value of of the other parameter. For three parameters, the same line plot is created within conditioning panels of the other parameter.

Also, with two tuning parameters (with different values), a levelplot (i.e. un-clustered heatmap) can be created. For more than two parameters, this plot is created inside conditioning panels.

References

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

Examples

Run this code

data(iris)
TrainData <- iris[,1:4]
TrainClasses <- iris[,5]

library(e1071)
rpartFit <- train(TrainData, TrainClasses, "rpart", 
                  tuneLength=15)
plot(rpartFit, scales = list(x = list(rot = 90)))

library(klaR)
rdaFit <- train(TrainData, TrainClasses, "rda", 
                control = trainControl(method = "cv"))
plot(rdaFit, plotType = "line", auto.key = TRUE)