plot.clusterforest {C443}

Plot a clusterforest object

Description

A function that can be used to plot a clusterforest object, either by returning plots with information such as average silhouette width and within cluster siiliarity on the cluster solutions, or plots of the medoid trees of each solution.

Usage

## S3 method for class 'clusterforest'
plot(x, solution = NULL, predictive_plots = FALSE, ...)

Arguments

x

A clusterforest object

solution

The solution to plot the medoid trees from. If NULL, plots with the average silhouette width, within cluster similiarty (and predictive accuracy) per solution are returned. Default = NULL

predictive_plots

Indicating whether predictive plots should be returned: A plot showing the predictive accuracy when making predictions based on the medoid trees, and a plot of the agreement between the class label for each object predicted on the basis of the random forest as a whole versus based on the medoid trees. Default = FALSE.

...

Additional arguments that can be used in generic plot function, or in plot.party.

Details

This function can be used to plot a clusterforest object in two ways. If it's used without specifying a solution, then the average silhouette width, and within cluster similarity measures are plotted for each solution. If additionally, predictive_plots=TRUE, two more plots are returned, namely a plot showing for each solution the predictive accuracy when making predictions based on the medoid trees, and a plot showing for each solution the agreement between the class label for each object predicted on the basis of the random forest as a whole versus based on the medoid trees. These plots may be helpful in deciding how many clusters are needed to summarize the forest (see Sies & Van Mechelen, 2020).

If the function is used with the clusterforest object and the number of the solution, then the medoid tree(s) of that solution are plotted.

References

Sies, A. & Van Mechelen I. (2020). C443: An R-package to see a forest for the trees. Journal of Classification.

Examples

require(MASS)
require(rpart)
#Function to draw a bootstrap sample from a dataset
DrawBoots <- function(dataset, i){
set.seed(2394 + i)
Boot <- dataset[sample(1:nrow(dataset), size = nrow(dataset), replace = TRUE),]
return(Boot)
}

#Function to grow a tree using rpart on a dataset
GrowTree <- function(x,y,BootsSample, minsplit = 40, minbucket = 20, maxdepth =3){
 controlrpart <- rpart.control(minsplit = minsplit, minbucket = minbucket,
 maxdepth = maxdepth, maxsurrogate = 0, maxcompete = 0)
 tree <- rpart(as.formula(paste(noquote(paste(y, "~")),
 noquote(paste(x, collapse="+")))), data = BootsSample,
 control = controlrpart)
 return(tree)
}

#Use functions to draw 20 boostrapsamples and grow a tree on each sample
Boots<- lapply(1:10, function(k) DrawBoots(Pima.tr ,k))
Trees <- lapply(1:10, function (i) GrowTree(x=c("npreg", "glu",  "bp",
 "skin",  "bmi", "ped", "age"), y="type",
Boots[[i]] ))

ClusterForest<- clusterforest(observeddata=Pima.tr,treedata=Boots,trees=Trees,m=1,
fromclus=1, toclus=5, sameobs=FALSE, no_cores=2)
plot(ClusterForest)
plot(ClusterForest,2)