Example: pcalg

Example_pcalg.R

library(pcalg)

df <- read.csv("Data/reduce_final_R.csv") # Load CSV file as dataframe format. 

# re-encode into matrix format. 
matrix_df = data.matrix(df[,c(2:20)])

# PC Algorithm 
## Parameters 
SuffStat = list(C=cor(matrix_df),n=nrow(matrix_df))
## Fitting 
result.pc = pc(suffStat = SuffStat, indepTest = gaussCItest, p=ncol(matrix_df),alpha=0.01 )
## plot 
plot(result.pc)




# GES Algorithm 
dev.off() # clear the last plot
## Compute score
colnames(matrix_df) = c(1:ncol(matrix_df)) # If colume names are too long, then the node in the plot become empty. 
score.ges = new("GaussL0penObsScore", matrix_df)
## Fitting 
result.ges = ges(score.ges)
## Plot 
plot(result.ges$essgraph)


# RFCI algorithm (RFCI is a fast version of FCI algorithm) 
dev.off() # clear the last plot
## Compute score 
result.rfci = rfci(SuffStat,indepTest = gaussCItest, p = ncol(matrix_df), alpha=0.01 )
## plot 
plot(result.rfci)