pcSelect_parallel: Estimate subgraph around a response variable using...

In ParallelPC: Paralellised Versions of Constraint Based Causal Discovery Algorithms

Description

This is the parallelised version of the pcSelect (stable) function in the pcalg package. Assume that we have a fixed target variable, the algorithm will test the dependency between each variable and the target variable conditioning on combinations of other variables.

Usage

pcSelect_parallel(y, dm, method = c("parallel"), mem.efficient = FALSE,
  num_workers, alpha, corMethod = "standard", verbose = FALSE,
  directed = FALSE)

Arguments

y	The target (response) variable.
dm	Data matrix with rows are samples and columns are variables.
method	Character string specifying method; the default, "parallel" provides an parallelised method to implement all the conditional independence tests.
mem.efficient	If TRUE, uses less amount of memory at any time point while running the algorithm
num_workers	The numbers of cores CPU to run the algorithm
alpha	Significance level of individual partial correlation tests.
corMethod	"standard" or "Qn" for standard or robust correlation estimation
verbose	Logical or in {0,1,2}; FALSE, 0: No output, TRUE, 1: Little output, 2: Detailed output. Note that such output makes the function very much slower.
directed	Logical; should the output graph be directed?

Value

G A logical vector indicating which column of dm is associated with y.

zMin The minimal z-values when testing partial correlations between y and each column of dm. The larger the number, the more consistent is the edge with the data.

Examples

##########################################
## Using pcSelect_parallel without mem.efficeient
##########################################
library(pcalg)
library(parallel)
p <- 10
set.seed(101)
myDAG <- randomDAG(p, prob = 0.2)
n <- 1000
d.mat <- rmvDAG(n, myDAG, errDist = "normal")
pcSelect_parallel(d.mat[,10],d.mat[,-10], alpha=0.05,num_workers=2)

##########################################
## Using pcSelelct_parallel with mem.efficeient
##########################################
library(pcalg)
library(parallel)
p <- 10
set.seed(101)
myDAG <- randomDAG(p, prob = 0.2)
n <- 1000
d.mat <- rmvDAG(n, myDAG, errDist = "normal")
pcSelect_parallel(d.mat[,10],d.mat[,-10], alpha=0.05,mem.efficient=TRUE,num_workers=2)

ParallelPC documentation built on May 2, 2019, 9:14 a.m.