Clustering of ICE and d-ICE curves by kmeans.

Clustering if ICE and d-ICE curves by kmeans. All curves are centered to have mean 0 and then kmeans is applied to the curves with the specified number of clusters.

Usage

clusterICE(
  ice_obj,
  nClusters,
  plot = TRUE,
  plot_margin = 0.05,
  colorvec,
  plot_pdp = FALSE,
  x_quantile = FALSE,
  avg_lwd = 3,
  centered = FALSE,
  plot_legend = FALSE,
  main = NULL,
  num_cores = 1,
  ...
)

Arguments

ice_obj

Object of class ice or dice to cluster.

nClusters

Number of clusters to find.

plot

If TRUE, plots the clusters.

plot_margin

Extra margin to pass to ylim as a fraction of the range of cluster centers.

colorvec

Optional vector of colors to use for each cluster.

plot_pdp

If TRUE, the PDP (ice object) or d-PDP (dice object) is plotted with a dotted black line and highlighted in yellow.

x_quantile

If TRUE, the plot is drawn with the x-axis taken to be quantile(gridpts). If FALSE, the predictor's original scale is used.

avg_lwd

Average line width to use when plotting the cluster means. Line width is proportional to the cluster's size.

centered

If TRUE, all cluster means are shifted to be to be 0 at the minimum value of the predictor. If FALSE, the original cluster means are used.

plot_legend

If TRUE a legend mapping line colors to the proportion of the data in each cluster is added to the plot.

main

Optional title for the plot.

num_cores

Integer number of cores to use for parallel operations. Default is 1.

...

Additional arguments for plotting.

Value

A list with the following elements:

cl

The output of the kmeans call (a list of class kmeans).

plot

The ggplot object used for plotting (if plot = TRUE).

See also

ice, dice

Examples

if (FALSE) { # \dontrun{
require(ICEbox)
require(randomForest)
require(MASS) #has Boston Housing data, Pima

data(Boston) #Boston Housing data
X = Boston
y = X$medv
X$medv = NULL

## build a RF:
bh_rf = randomForest(X, y)

## Create an 'ice' object for the predictor "age":
bh.ice = ice(object = bh_rf, X = X, y = y, predictor = "age",
            frac_to_build = .1) 

## cluster the curves into 2 groups.
clusterICE(bh.ice, nClusters = 2, plot_legend = TRUE)

## cluster the curves into 3 groups, start all at 0.
clusterICE(bh.ice, nClusters = 3, plot_legend = TRUE, center = TRUE)
} # }