gera bn pa

# Clean environment

closeAllConnections()

rm(list=ls())

# Set enviroment

setwd("~/your working directory")

# Load packages

library(bnpa)

# Show data sets

data1<-dataQuantC # Pre-Loaded

data2<-dataQualiN # Pre-Loaded

head(data1)

head(data2)

# Set a data set to work

data.to.work <- data1

# Transform some variables into integer types

data.to.work$A<-as.integer(data.to.work$A)

data.to.work$C<-as.integer(data.to.work$C)

data.to.work$E<-as.integer(data.to.work$E)

data.to.work$G<-as.integer(data.to.work$G)

# Creates a white and black list empty

wl=""

bl=""

# Set what BN learning algorithms will be used

bn.learn.algorithms <- c("hc", "rsmax2")

# Learn a BN structure from data to work and builds a PA model

bn.pa<-gera.bn.pa(data.to.work, bn.learn.algorithms)

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RESULTS:

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The BN structures learned from data:

• HC

• MMHC

The BN parameters:

• HC

• MMHC

The PA model Learned and main fit indexes:

• HC

• MMHC

The PA parameters:

• HC

• RSMAX2