R: Clustering of ICE and d-ICE curves by kmeans.

clusterICE {ICEbox}

R Documentation

Clustering of ICE and d-ICE curves by kmeans.

Description

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, ...)

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.
`...`	Additional arguments for plotting.

Value

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

Examples

## Not run: 
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)

## End(Not run)