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R: creation of a "simulatedDAG" containing a list of DAGs and...

initialize,simulatedDAG-method

R Documentation

creation of a "simulatedDAG" containing a list of DAGs and associated observations

Description

creation of a "simulatedDAG" containing a list of DAGs and associated observations

Usage

## S4 method for signature 'simulatedDAG'
initialize(.Object, NDAG = 1,
  noNodes = sample(10:20, size = 1), functionType = "linear",
  quantize = FALSE, verbose = TRUE, N = sample(100:500, size = 1),
  seed = 1234, sdn = 0.5, goParallel = FALSE)

Arguments

`.Object`	: simulatedDAG object
`NDAG`	: number of DAGs to be created and simulated
`noNodes`	: number of Nodes of the DAGs. If it is a two-valued vector , the value of Nodes is randomly sampled in the interval
`functionType`	: type of the dependency. It is of class "character" and is one of ("linear", "quadratic","sigmoid")
`quantize`	: if TRUE it discretize the observations into two bins. If it is a two-valued vector [a,b], the value of quantize is randomly sampled in the interval [a,b]
`verbose`	: if TRUE it prints out the state of progress
`N`	: number of sampled observations for each DAG. If it is a two-valued vector [a,b], the value of N is randomly sampled in the interval [a,b]
`seed`	: random seed
`sdn`	: standard deviation of aditive noise. If it is a two-valued vector, the value of N is randomly sampled in the interval
`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=10, N=c(50,100),noNodes=c(15,40),
             functionType = "linear", seed=0,sdn=c(0.45,0.75))

Results


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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-simulatedDAG-method.Rd_%03d_medium.png", width=480, height=480)
> ### Name: initialize,simulatedDAG-method
> ### Title: creation of a "simulatedDAG" containing a list of DAGs and
> ###   associated observations
> ### Aliases: initialize,simulatedDAG-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=new("D2C.descriptor")
> descr.example<-new("D2C.descriptor",bivariate=FALSE,ns=3,acc=TRUE)
> trainDAG<-new("simulatedDAG",NDAG=10, N=c(50,100),noNodes=c(15,40),
+              functionType = "linear", seed=0,sdn=c(0.45,0.75))
simulatedDAG: DAG number: 1 generated: #nodes= 24 # edges= 35 # samples= 63 
simulatedDAG: DAG number: 2 generated: #nodes= 33 # edges= 34 # samples= 59 
simulatedDAG: DAG number: 3 generated: #nodes= 35 # edges= 42 # samples= 58 
simulatedDAG: DAG number: 4 generated: #nodes= 15 # edges= 14 # samples= 79 
simulatedDAG: DAG number: 5 generated: #nodes= 32 # edges= 51 # samples= 60 
simulatedDAG: DAG number: 6 generated: #nodes= 39 # edges= 101 # samples= 80 
simulatedDAG: DAG number: 7 generated: #nodes= 25 # edges= 12 # samples= 100 
simulatedDAG: DAG number: 8 generated: #nodes= 20 # edges= 27 # samples= 73 
simulatedDAG: DAG number: 9 generated: #nodes= 15 # edges= 9 # samples= 61 
simulatedDAG: DAG number: 10 generated: #nodes= 22 # edges= 23 # samples= 75 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>