cnDiscretize: Data Categorization

In catnet: Categorical Bayesian Network Inference

Data Categorization

Description

Numerical data discretization using empirical quantiles.

Usage

cnDiscretize(data, numCategories, mode="uniform", qlevels=NULL)

Arguments

data	a numerical matrix or data.frame
numCategories	an integer, the number of categories per node
mode	a character, the discretization method to be used, "quantile" or "uniform"
qlevels	a list of integer vectors, the node discretization parameters

Details

The numerical data is discretized into given number of categories, numCategories, using the empirical node quantiles. As in all functions of catnet package that accept data, if the data parameter is a matrix then it is organized in the row-node format. If it is a data.frame, the column-node format is assumed.

The mode specifies the discretization model. Currantly, two discretization methods are supported - "quantile" and "uniform", which is the default choice.

The quantile-based discretization method is applied as follows. For each node, the sample node distribution is constructed, which is then represented by a sum of non-intersecting classes separated by the quantile points of the sample distribution. Each node value is assigned the class index in which it falls into.

The uniform discretization breaks the range of values of each node into numCategories equal intervals or of lengths proportional to the corresponding qlevels values.

Currently, the function assigns equal number of categories for each node of the data.

Value

A matrix or data.frame of indices.

Author(s)

N. Balov, P. Salzman

See Also

cnSamples

Examples

  ps <- t(sapply(1:10, function(i) rnorm(20, i, 0.1)))
  dps1 <- cnDiscretize(ps, 3, mode="quantile")
  hist(dps1[1,])
  qlevels <- lapply(1:10, function(i) rep(1, 3))
  qlevels[[1]] <- c(1,2,1)
  dps2 <- cnDiscretize(ps, 3, mode="uniform", qlevels)
  hist(dps2[1,])

catnet documentation built on Nov. 10, 2022, 5:08 p.m.