initialize-D2C.descriptor-method: creation of a D2C.descriptor

In D2C: Predicting Causal Direction from Dependency Features

Description

creation of a D2C.descriptor

Usage

## S4 method for signature 'D2C.descriptor'
initialize(.Object, lin = TRUE, acc = TRUE,
  struct = TRUE, pq = c(0.1, 0.25, 0.5, 0.75, 0.9), bivariate = FALSE,
  ns = 4)

Arguments

.Object	: the D2C.descriptor object
lin	: TRUE OR FALSE: if TRUE it uses a linear model to assess a dependency, otherwise a local learning algorithm
acc	: TRUE OR FALSE: if TRUE it uses the accuracy of the regression as a descriptor
struct	: TRUE or FALSE to use the ranking in the markov blanket as a descriptor
pq	:a vector of quantiles used to compute the descriptors
bivariate	:TRUE OR FALSE: if TRUE it includes also the descriptors of the bivariate dependence
ns	: size of the Markov Blanket returned by the mIMR algorithm

References

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

Examples

require(RBGL)
require(gRbase)
require(foreach)
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)

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