R_code_bn

#Install dagitty

install.packages("devtools") # unless you have it already
library(devtools)
devtools::install_github("jtextor/dagitty/r")
#install_github("jtextor/dagitty/r")

#Optie unlooped
library( dagitty )
g <- dagitty('
dag {
Absences -> Failures
Absences -> Grades
Activities -> Freetime
Age -> Activities
Age -> Romantic
Age -> Studytime
Failures -> Grades
Failures -> Paid
Failures -> Schoolsup
Failures -> Studytime
Famrel -> Famsup
Famrel -> Paid
Famsup -> Grades
Famsup -> Paid
Famsup -> Studytime
Fedu -> Fjob
Fedu -> Higher
Fjob -> Famrel
Freetime -> Grades
Freetime -> Studytime
Goout -> Freetime
Goout -> Romantic
Health -> Absences
Health -> Grades
Health -> Studytime
Higher -> Famsup
Higher -> School
Higher -> Studytime
Medu -> Higher
Medu -> Mjob
Mjob -> Famrel
Paid -> Freetime
Paid -> Grades
Paid -> Studytime
Romantic -> Freetime
School -> Schoolsup
School -> Traveltime
Schoolsup -> Freetime
Schoolsup -> Grades
Schoolsup -> Studytime
Studytime -> Grades
Subject -> Absences
Subject -> Failures
Subject -> Famsup
Subject -> Grades
Subject -> Paid
Subject -> Schoolsup
Subject -> Studytime
Traveltime -> Absences
Traveltime -> Freetime
}
')

# Print out all d-separation statements implied by the DAG.
impliedConditionalIndependencies(g)

# This single command performs a chi-square test for all implied conditional
# independencies from g on the dataset g, and reports the statistic, p-value
# and the RMSEA for each test.
localTests(g,d,type="cis.chisq")

install.packages('bnlearn’)
library(bnlearn)

#Import data

d <- read.csv("/home/mdegrauw/Downloads/bn-master/matpor_concat.csv")

#Remove duplicate rownumber row

d <- d[,-1]

# Let's take a quick look at the levels and distributions of each variable.
for( i in colnames(d) ){
	print(i)
	print(table(d[,i]))
}

g2 <- pc.stable(d)
plot(g2)
amat(g2)

g3 <- pc.stable(diabetes,alpha=0.01)
plot(g3)
amat(g3)

d$G1 <- cut(d$G1, c(-1,5,10,15,20))
d$G2 <- cut(d$G2, c(-1,5,10,15,20))
d$G3 <- cut(d$G3, c(-1,5,10,15,20))
d$absences <- cut(d$absences, c(-1,0,5,10,20,75))