ggplot.train
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 train
ggplot(
data = NULL,
mapping = NULL,
metric = data$metric[1],
plotType = "scatter",
output = "layered",
nameInStrip = FALSE,
highlight = FALSE,
...,
environment = NULL
)# S3 method for train
plot(
x,
plotType = "scatter",
metric = x$metric[1],
digits = getOption("digits") - 3,
xTrans = NULL,
nameInStrip = FALSE,
...
)
Arguments
- data
an object of class
train
.- 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.
- plotType
a string describing the type of plot (
"scatter"
,"level"
or"line"
(plot
only))- 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.- nameInStrip
a logical: if there are more than 2 tuning parameters, should the name and value be included in the panel title?
- highlight
a logical: if
TRUE
, a diamond is placed around the optimal parameter setting for models using grid search.- …
plot
only: specifications to be passed tolevelplot
,xyplot
,stripplot
(for line plots). 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
train
.- digits
an integer specifying the number of significant digits used to label the parameter value.
- xTrans
a function that will be used to scale the x-axis in scatter plots.
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/article/view/v028i05/v28i05.pdf)
See Also
Examples
# NOT RUN {
# }
# NOT RUN {
library(klaR)
rdaFit <- train(Species ~ .,
data = iris,
method = "rda",
control = trainControl(method = "cv"))
plot(rdaFit)
plot(rdaFit, plotType = "level")
ggplot(rdaFit) + theme_bw()
# }
# NOT RUN {
# }