diversityForest (version 0.5.0)

plot.multifor: Plot method for multifor objects

Description

Plot function for multifor objects that allows to obtain a first overview of the result of the multi-class VIM analysis. This function visualises the distribution of the multi-class VIM values together with that of the corresponding discriminatory VIM values and the estimated dependency structures of the multi-class outcome on the variables with largest multi-class VIM values. These estimated dependency structures are visualised using density plots and/or boxplots.

Usage

# S3 method for multifor
plot(x, plot_type = c("both", "density", "boxplot")[1], num_best = 5, ...)

Value

A ggplot2 plot.

Arguments

x

Object of class multifor.

plot_type

Plot type, one of the following: "both" (the default), "density", "boxplot". If "density", "density" plots are produced, if "boxplot", "boxplot" plots are produced, and if "both", both "density" plots and "boxplot" plots are produced. See the 'Details' section of plotMcl for details.

num_best

The number of variables with largest multi-class VIM values to plot. Default is 5.

...

Further arguments passed to or from other methods.

Author

Roman Hornung

Details

In the plot showing the distribution of the multi-class VIM values along with that of the discriminatory VIM values, the discriminatory VIM values are normalized to make them comparable to the multi-class VIM values. This is achieved by dividing the discriminatory VIM values by their mean and multiplying it by that of the multi-class VIM values. Although the discriminatory VIM values are computed for all variables, only those variables for which the multi-class VIM values were computed are included in this analysis (i.e., all variables that have at least as many unique values as there are classes in the outcome variable).
For details on the plots of the estimated dependency structures of the multi-class outcome on the variables, see plotMcl. The latter function allows to visualise these estimated dependency structures for arbitrary variables in the data.

References

• Hornung, R., Hapfelmeier, A. (2024). Multi forests: Variable importance for multi-class outcomes. arXiv:2409.08925, <tools:::Rd_expr_doi("10.48550/arXiv.2409.08925")>.

• Hornung, R. (2022). Diversity forests: Using split sampling to enable innovative complex split procedures in random forests. SN Computer Science 3(2):1, <tools:::Rd_expr_doi("10.1007/s42979-021-00920-1")>.

See Also

plotMcl

Examples

if (FALSE) {

## Load package:

library("diversityForest")



## Set seed to make results reproducible:

set.seed(1234)



## Construct multi forest and calculate multi-class and discriminatory VIM values:

data(hars)
model <- multifor(dependent.variable.name = "Activity", data = hars, 
                  num.trees = 100, probability=TRUE)

# NOTE: num.trees = 100 (in the above) would be likely too small for practical 
# purposes. This small number of trees was simply used to keep the
# runtime of the example short.
# The default number of trees is num.trees = 5000 for datasets with a maximum of
# 5000 observations and num.trees = 1000 for datasets larger than that.



## By default the estimated class-specific distributions of the num_best=5
## variables with the largest multi-class VIM values are plotted:

plot(model)

## Consider only the 2 variables with the largest multi-class VIM values:

plot(model, num_best = 2)

## Show only the density plots or only the boxplots:

plot(model, plot_type = "density", num_best = 2)
plot(model, plot_type = "boxplot", num_best = 2)

## Show only the plot of the distributions of the multi-class and
## discriminatory VIM values:

plot(model, num_best = 0)

}