ChowLiu: Determines the Chow-Liu tree for data

In nvihrs14/tcherry: Learning the structure of tcherry trees

Description

Determines the structure and the conditional probability tables for the Chow-Liu tree fitted to data.

Usage

ChowLiu(data, root = NULL, bayes_smooth = 0, CPTs = TRUE, ...)

Arguments

data	The data the tree structure should be based on.
root	An optional argument, choosing a userspecified root for the tree.
bayes_smooth	Additional cell counts for bayesian estimation of probability tables.
CPTs	A logical value indicating whether conditional probability tables should be estimated from data or not.
...	Additional parameters passed to MI2.

Value

A list containing the following components:

• skeleton_adj The adjacency matrix for the skeleton of the Chow-Liu tree.

• adj_DAG The adjacency matrix of the resulting DAG.

• CPTs The estimated conditional probability tables of the bayesian network if estimated. Otherwise the logical value FALSE.

Author(s)

Katrine Kirkeby, [e-mail]

Maria Knudsen, [e-mail]

Ninna Vihrs, [e-mail]

See Also

MI2 for mutual information, CPT for probability tables and is_acyclic for a test for cycles.

Examples

set.seed(43)
var1 <- c(sample(c(1, 2), 50, replace = TRUE))
var2 <- var1 + c(sample(c(1, 2), 50, replace = TRUE))
var3 <- var1 + c(sample(c(0, 1), 50, replace = TRUE,
                        prob = c(0.9, 0.1)))
var4 <- c(sample(c(1, 2), 50, replace = TRUE))

data <- data.frame("var1" = as.character(var1),
                   "var2" = as.character(var2),
                   "var3" = as.character(var3),
                   "var4" = as.character(var4))

CL <- ChowLiu(data, root = 'var1', smooth = 0.1)

nvihrs14/tcherry documentation built on Aug. 1, 2020, 6:25 p.m.