discretize.dens: discretize.dens

In GroupBN: Inferring Group Bayesian Networks using Hierarchical Feature Clustering

Description

density approximative discretization. Significant peaks in the density are determined and used as starting points for k-means based discretization. If only one peak is present, distribution quartiles are used for binning.

Usage

discretize.dens(data, graph=F, title="Density-approxmative Discretization",
rename.level=F, return.all=T, cluster=F, seed=NULL)

Arguments

data	a vector containing the data that may be discretized
graph	a boolean value, if TRUE, the density and the determined binning are plotted
title	a title for the plot
rename.level	a boolean value, if TRUE, factor levels are replaced by integers 1:n
return.all	a boolean value, if FALSE, only the discretized data are returned.
cluster	a boolean value, if data is a cluster variable and may already be discrete or not
seed	a random seed number

Value

discretized	the discretized data
levels	the factor levels
optima	the x and y coordinates of the determined peaks

Author(s)

Ann-Kristin Becker

Examples

testdata = c(rnorm(100,-3,1), rnorm(100,3,1))
d<-discretize.dens(testdata, graph=TRUE)
summary(d$discretized)

GroupBN documentation built on March 7, 2021, 5:06 p.m.