plot.train

0th

Percentile

Plot Method for the train Class

This function takes the output of a train object and creates a line or level plot using the lattice or ggplot2 libraries.

Keywords
hplot
Usage
## S3 method for class 'train':
plot(x, 
     plotType = "scatter",
     metric = x$metric[1],
     digits = getOption("digits") - 3, 
     xTrans = NULL, 
     ...)

## S3 method for class 'train': ggplot(data = NULL, metric = data$metric[1], plotType = "scatter", output = "layered", ...)

Arguments
x
an object of class train.
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.
plotType
a string describing the type of plot ("scatter", "level" or "line" (plot only))
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.
data
an object of class train.
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 levelplot, xyplot, stripplot<
Details

If there are no tuning parameters, or none were varied, an error is produced. 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/facets 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/facets.

References

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

See Also

train, levelplot, xyplot, stripplot, ggplot

Aliases
  • plot.train
  • ggplot.train
Examples
library(klaR)
rdaFit <- train(Species ~ .,
                data = iris, 
                method = "rda", 
                control = trainControl(method = "cv"))
plot(rdaFit)
plot(rdaFit, plotType = "level")

ggplot(rdaFit) + theme_bw()
Documentation reproduced from package caret, version 6.0-24, License: GPL-2

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