initialize-D2C-method: creation of a D2C object which preprocesses the list of DAGs...

In D2C: Predicting Causal Direction from Dependency Features

Description

creation of a D2C object which preprocesses the list of DAGs and observations contained in sDAG and fits a Random Forest classifier

Usage

## S4 method for signature 'D2C'
initialize(.Object, sDAG, descr = new("D2C.descriptor"),
  verbose = TRUE, ratioMissingNode = 0, ratioEdges = 1,
  max.features = 20, goParallel = FALSE)

Arguments

.Object	: the D2C object
sDAG	: simulateDAG object
descr	: D2C.descriptor object containing the parameters of the descriptor
verbose	: if TRUE it prints the state of progress
ratioMissingNode	: percentage of existing nodes which are not considered. This is used to emulate latent variables.
ratioEdges	: percentage of existing edges which are added to the training set
max.features	: maximum number of features used by the Random Forest classifier randomForest. The features are selected by the importance returned by the function importance.
goParallel	: if TRUE it uses parallelism

References

Gianluca Bontempi, Maxime Flauder (2014) From dependency to causality: a machine learning approach. Under submission

Examples

require(RBGL)
require(gRbase)
 require(foreach)
descr=new("D2C.descriptor")
descr.example<-new("D2C.descriptor",bivariate=FALSE,ns=3,acc=TRUE)
trainDAG<-new("simulatedDAG",NDAG=2, N=50,noNodes=10,
             functionType = "linear", seed=0,sdn=0.5)
example<-new("D2C",sDAG=trainDAG, descr=descr.example)

D2C documentation built on May 29, 2017, 10:44 a.m.