bayesEst: Bayesian Network Estimator

In bd2kccd/r-causal: R-Causal Library

Bayesian Network Estimator

Description

bayesEst is the revised Greedy Equivalence Search (GES) algorithm developed by Joseph D. Ramsey, Director of Research Computing, Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA.

Usage

bayesEst(df, depth = 3, significance = 0.05, verbose = FALSE, 
	java.parameters = NULL, priorKnowledge = NULL)

Arguments

df	Data Frame of the dataset.
depth	integer indicating a number of nodes conditioned on in the search. It ranges from -1 for unlimited nodes, otherwise a number >= 0. By default, depth=3.
significance	floating point indicating the statistical value (between zero and one) for the conditional independence test. By default, significance=0.05.
verbose	boolean indicating whether or not if verbose output should be printed. verbose is FALSE by default.
java.parameters	string indicating an optional parameters for JVM. For example, java.parameters = "-Xmx1024M". By default, java.parameters=NULL.
priorKnowledge	object indicating a prior knowledge of the graph. By default, priorKnowledge=NULL.

Details

The Bayes estimator uses the maximum likelihood estimation in order to estimate causal graph.

More detail about bayesEst implementation, please visit the java code.

Value

A list containing the bayesEst's parameters, the input dataset, the result's nodes, and the result's edges.

Examples

data("audiology")
#Compute bayes Estimation
bayesEst <- bayesEst(df=audiology, depth = -1, verbose = TRUE)
bayesEst$parameters #Show the bayesEst's parameters
bayesEst$datasets #Show the dataset
bayesEst$nodes #Show the result's nodes
bayesEst$edges #Show the result's edges

bd2kccd/r-causal documentation built on Aug. 29, 2023, 9:17 a.m.