bnlearn (version 4.7.1)

data preprocessing: Pre-process data to better learn Bayesian networks

Description

Screen and transform the data to make them more suitable for structure and parameter learning.

Usage

# discretize continuous data into factors.
  discretize(data, method, breaks = 3, ordered = FALSE, ..., debug = FALSE)
  # screen continuous data for highly correlated pairs of variables.
  dedup(data, threshold, debug = FALSE)

Value

discretize() returns a data frame with the same structure (number of columns, column names, etc.) as data, containing the discretized variables.

dedup() returns a data frame with a subset of the columns of data.

Arguments

data

a data frame containing numeric columns (for dedup()) or a combination of numeric or factor columns (for discretize()).

threshold

a numeric value between zero and one, the absolute correlation used a threshold in screening highly correlated pairs.

method

a character string, either interval for interval discretization, quantile for quantile discretization (the default) or hartemink for Hartemink's pairwise mutual information method.

breaks

an integer number, the number of levels the variables will be discretized into; or a vector of integer numbers, one for each column of the data set, specifying the number of levels for each variable.

ordered

a boolean value. If TRUE the discretized variables are returned as ordered factors instead of unordered ones.

...

additional tuning parameters, see below.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Author

Marco Scutari

Details

discretize() takes a data frame as its first argument and returns a secdond data frame of discrete variables, transformed using of three methods: interval, quantile or hartemink. Discrete variables are left unchanged.

The hartemink method has two additional tuning parameters:

• idisc: the method used for the initial marginal discretization of the variables, either interval or quantile.

• ibreaks: the number of levels the variables are initially discretized into, in the same format as in the breaks argument.

dedup() screens the data for pairs of highly correlated variables, and discards one in each pair.

Both discretize() and dedup() accept data with missing values.

References

Hartemink A (2001). Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks. Ph.D. thesis, School of Electrical Engineering and Computer Science, Massachusetts Institute of Technology.

Examples

data(gaussian.test)
d = discretize(gaussian.test, method = 'hartemink', breaks = 4, ibreaks = 10)
plot(hc(d))
d2 = dedup(gaussian.test)