pcSelect.presel: Estimate Subgraph around a Response Variable using Preselection

Description

This function uses pcSelect to preselect some covariates and then runs pcSelect again on the reduced data set.

Usage

pcSelect.presel(y, dm, alpha, alphapre, corMethod = "standard", verbose = 0, directed=FALSE)

Arguments

Response vector.

Data matrix (rows: samples, cols: nodes; i.e., length(y) == nrow(dm)).

alpha

Significance level of individual partial correlation tests.

alphapre

Significance level for pcSelect in preselection

corMethod

"standard" or "Qn" for standard or robust correlation estimation

verbose

0-no output, 1-small output, 2-details (using 1 and 2 makes the function very much slower)

directed

Logical; should the output graph be directed?

Value

Details

First, pcSelect is run using alphapre. Then, only the important variables are kept and pcSelect is run on them again.

Examples

Run this code

p <- 10
## generate and draw random DAG :
set.seed(101)
myDAG <- randomDAG(p, prob = 0.2)
if(require(Rgraphviz))
   plot(myDAG, main = "randomDAG(10, prob = 0.2)")

## generate 1000 samples of DAG using standard normal error distribution
n <- 1000
d.mat <- rmvDAG(n, myDAG, errDist = "normal")

## let's pretend that the 10th column is the response and the first 9
## columns are explanatory variable. Which of the first 9 variables
## "cause" the tenth variable?
y <- d.mat[,10]
dm <- d.mat[,-10]
res <- pcSelect.presel(d.mat[,10], d.mat[,-10], alpha=0.05, alphapre=0.6)