pcSelect_stable: Estimate subgraph around a response variable using pcSelect

In ParallelPC: Paralellised Versions of Constraint Based Causal Discovery Algorithms

Description

This is the stable version (order independent version) of the pcSelect function (pc-Simple algorithm) in the pcalg package.

Usage

pcSelect_stable(y, dm, alpha, corMethod = "standard", method = "stable",
  verbose = FALSE, directed = FALSE)

Arguments

y	The target (response) variable.
dm	Data matrix with rows are samples and columns are variables.
alpha	Significance level of individual partial correlation tests.
corMethod	"standard" or "Qn" for standard or robust correlation estimation
method	Character string specifying method; the default, "stable" provides an Order-independent version.
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_stable
##########################################
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_stable(d.mat[,10],d.mat[,-10], alpha=0.05)

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