D2C (version 1.2.1)

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

Description

creation of a D2C.descriptor

Usage

"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)