pcSelect_parallel {ParallelPC}

Estimate subgraph around a response variable using pcSelect_parallel.

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)