pcalg (version 2.0-3)

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

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?

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.

Details

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

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)