Description Usage Arguments Value Author(s) Examples
View source: R/adaptive_refinement_help.R
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.
1 2 | discretize.dens(data, graph=F, title="Density-approxmative Discretization",
rename.level=F, return.all=T, cluster=F, seed=NULL)
|
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 |
discretized |
the discretized data |
levels |
the factor levels |
optima |
the x and y coordinates of the determined peaks |
Ann-Kristin Becker
1 2 3 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.