cnDiscretize {catnet} | R Documentation |
Numerical data discretization using empirical quantiles.
cnDiscretize(data, numCategories, mode="uniform", qlevels=NULL)
data |
a numerical |
numCategories |
an |
mode |
a |
qlevels |
a list of |
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.
A matrix
or data.frame
of indices.
N. Balov, P. Salzman
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,])