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