bnpa (version 0.3.0)

boot.strap.bn: Executes a bootstrap during the learning of a BN structure

Description

This function receives a list of parameters, executes the bootstrap process and learn the Bayesian Network (BN) from the data set, then executes the process of model averaging to extract the final BN structure and print it.

Usage

boot.strap.bn(bn.algorithm, bn.score.test, data.to.work, black.list,
  white.list, nreplicates = 1000, type.of.algorithm, outcome.var)

Arguments

bn.algorithm

is a list of algorithms to learn the BN structure.

bn.score.test

is list of conditional independence tests and the network scores to be used.

data.to.work

is a data from which the BN structure will be learned.

black.list

is a list of forbiden connections of BN structure to be created.

white.list

is a list of mandatory connections of BN structure to be created.

nreplicates

is the number of replications to be done in the bootstrap process.

type.of.algorithm

is the type of algorithm to learn the BN sctructure, it would be constrained or score based.

outcome.var

is the variable to be used as outcome (dependent) and be highlighted in the BN.

Value

The final BN structure learned.

References

Claeskens N, Hjort N (2009) Model selection and model avaraging. Cambridge University Press, Cambridge, England.

Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge.

Scutari M (2017). Bayesian Network Constraint-Based Structure Learning Algorithms: Parallel and Optimized Implementations in the bnlearn R Package. Journal of Statistical Software, 77(2), 1-20.

Examples

# NOT RUN {
# Clean environment
closeAllConnections()
rm(list=ls())
# Set enviroment
# setwd("to your working directory")
# Load packages
library(bnpa)
# Use working data sets from package
data(dataQualiN)
# Start the cluster
cl <- bnpa::create.cluster()
# Set the number of replications
nreplicates=1000
# Set the algorithm to be used
bn.algorithm="hc"
# Executes a parallel bootstrap process
data.bn.boot.strap=bnlearn::boot.strength(data = dataQualiN, R = nreplicates, algorithm =
bn.algorithm, cluster=cl, algorithm.args=list(score="bic"), cpdag = FALSE)
# Release the cluster
parallel::stopCluster(cl)
head(data.bn.boot.strap)
# }