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.
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).
See Also
ice, dice
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)