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

In pcalg: Methods for Graphical Models and Causal Inference

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

y	Response vector.
dm	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?

Details

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

Value

pcs	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.
Xnew	Preselected Variables.

Author(s)

Philipp Ruetimann

See Also

pcSelect

Examples

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)

pcalg documentation built on Sept. 26, 2023, 9:06 a.m.