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Last data update: 2014.03.03

R: creation of a D2C.descriptor

initialize,D2C.descriptor-method

R Documentation

creation of a D2C.descriptor

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)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(D2C)
Loading required package: randomForest
randomForest 4.6-12
Type rfNews() to see new features/changes/bug fixes.
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/D2C/initialize-D2C.descriptor-method.Rd_%03d_medium.png", width=480, height=480)
> ### Name: initialize,D2C.descriptor-method
> ### Title: creation of a D2C.descriptor
> ### Aliases: initialize,D2C.descriptor-method
> 
> ### ** Examples
> 
> require(RBGL)
Loading required package: RBGL
Loading required package: graph
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following object is masked from 'package:randomForest':

    combine

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

> require(gRbase)
Loading required package: gRbase
> require(foreach)
Loading required package: 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)
simulatedDAG: DAG number: 1 generated: #nodes= 10 # edges= 6 # samples= 50 
simulatedDAG: DAG number: 2 generated: #nodes= 10 # edges= 4 # samples= 50 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>