Plot Method for the train Class
This function takes the output of a
train object and creates
a line or level plot using the
## S3 method for class 'train': plot(x, plotType = "scatter", metric = x$metric, digits = getOption("digits") - 3, xTrans = NULL, nameInStrip = FALSE, ...)
## S3 method for class 'train': ggplot(data = NULL, metric = data$metric, plotType = "scatter", output = "layered", nameInStrip = FALSE, ...)
- an object of class
- 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.
- a string describing the type of plot (
- an integer specifying the number of significant digits used to label the parameter value.
- a function that will be used to scale the x-axis in scatter plots.
- an object of class
- either "data", "ggplot" or "layered". The first returns a data frame while the second returns a simple
ggplotobject with no layers. The third value returns a plot with a set of layers.
- a logical: if there are more than 2 tuning parameters, should the name and value be included in the panel title?
plotonly: specifications to be passed to
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.
Kuhn (2008), ``Building Predictive Models in R Using the caret'' (
library(klaR) rdaFit <- train(Species ~ ., data = iris, method = "rda", control = trainControl(method = "cv")) plot(rdaFit) plot(rdaFit, plotType = "level") ggplot(rdaFit) + theme_bw()