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Note 2023--4-1: This version of RCausal uses an older version of Tetrad from at least 5 years ago. We have updated our Python integration to the most recent version of Tetrad in a separste project--see rpy-tetrad.

R-causal has not been maintained for some time now, as the tireless maintainer has since moved on to different work :-)... but going back through some of the issues posted for r-causal gives some hints as to additional functionality that pytetrad/R should have. We'll try to get caught up.

NEWS 2024-09-05: We are working on a new wrapping of Tetrad using rJava; please see r-tetrad. This will be a standalong implementation that requires only R to be installed; the JDK and Tetrad jars used will be downloaded programmatically by the R scripts.

r-causal

R Wrapper for Tetrad Library

R Library Requirement

R >= 3.3.0, stringr, rJava

Installation

  • Install the R library requirements:
install.packages("stringr")
install.packages("rJava")
  • Install r-causal from github:
library(devtools)
install_github("bd2kccd/r-causal")

Example

Continuous Dataset

library(rcausal)
data("charity")   #Load the charity dataset

tetradrunner.getAlgorithmDescription(algoId = 'fges')
#Compute FGES search
tetradrunner <- tetradrunner(algoId = 'fges',df = charity,scoreId = 'sem-bic',
dataType = 'continuous',faithfulnessAssumed=TRUE,maxDegree=-1,verbose=TRUE)

tetradrunner$nodes #Show the result's nodes
tetradrunner$edges #Show the result's edges

graph <- tetradrunner$graph
graph$getAttribute('BIC')

nodes <- graph$getNodes()
for(i in 0:as.integer(nodes$size()-1)){
    node <- nodes$get(i)
    cat(node$getName(),": ",node$getAttribute('BIC'),"\n")
}

Discrete Dataset

library(rcausal)
data("audiology")    #Load the charity dataset

#Compute FGES search
tetradrunner <- tetradrunner(algoId = 'fges',df = audiology,scoreId = 'cg-bic-score',dataType = 'discrete',
faithfulnessAssumed=TRUE,maxDegree=-1,verbose=TRUE)

tetradrunner$nodes #Show the result's nodes
tetradrunner$edges #Show the result's edges

graph <- tetradrunner$graph
graph$getAttribute('BIC')

nodes <- graph$getNodes()
for(i in 0:as.integer(nodes$size()-1)){
    node <- nodes$get(i)
    cat(node$getName(),": ",node$getAttribute('BIC'),"\n")
}

Prior Knowledge

Create PriorKnowledge Object

forbid <- list(c('TangibilityCondition','Impact')) # List of forbidden directed edges
require <- list(c('Sympathy','TangibilityCondition')) # List of required directed edges
forbiddenWithin <- c('TangibilityCondition','Imaginability')
class(forbiddenWithin) <- 'forbiddenWithin' # Make this tier forbidden within
temporal <- list(forbiddenWithin, c('Sympathy','AmountDonated'),c('Impact')) # List of temporal node tiers
prior <- priorKnowledge(forbiddirect = forbid, requiredirect = require, addtemporal = temporal)
tetradrunner <- tetradrunner(algoId = 'fges',df = charity,scoreId = 'fisher-z',
dataType = 'continuous',alpha=0.1,faithfulnessAssumed=TRUE,maxDegree=-1,verbose=TRUE, 
priorKnowledge = prior)

Load Knowledge File

# knowledge file: audiology.prior
# /knowledge
# forbiddirect
# class tymp
# class age_gt_60
# class notch_at_4k
# 
# requiredirect
# history_noise class
#
# addtemporal
# 0* bser late_wave_poor tymp notch_at_4k o_ar_c ar_c airBoneGap air bone o_ar_u airBoneGap
# 1 history_noise history_dizziness history_buzzing history_roaring history_recruitment history_fluctuating history_heredity history_nausea
# 2 class

prior <- priorKnowledgeFromFile('audiology.prior')
tetradrunner <- tetradrunner(algoId = 'fges',df = audiology,scoreId = 'bdeu',dataType = 'discrete',
alpha=0.1,faithfulnessAssumed=TRUE,maxDegree=-1,verbose=TRUE, priorKnowledge = prior)

Plot a DOT graph

library(DOT)
graph_dot <- tetradrunner.tetradGraphToDot(tetradrunner$graph)
dot(graph_dot)

Useful rJava Trouble-shooting Installation in Mac OS X Links

  1. http://stackoverflow.com/questions/26948777/how-can-i-make-rjava-use-the-newer-version-of-java-on-osx/32544358#32544358
  2. http://andrewgoldstone.com/blog/2015/02/03/rjava/
  3. https://stackoverflow.com/questions/7019912/using-the-rjava-package-on-win7-64-bit-with-r/7604469#7604469

Citation

DOI