pcSelect.presel: Estimate Subgraph around a Response Variable using... In pcalg: Methods for Graphical Models and Causal Inference

Description

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

Usage

 ```1 2``` ```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

`pcSelect`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```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) ```