pc.sel: Variable selection for continuous data using the PC-simple...

In pchc: Bayesian Network Learning with the PCHC and Related Algorithms

Variable selection for continuous data using the PC-simple algorithm

Description

Variable selection for continuous data using the PC-simple algorithm.

Usage

pc.sel(y, x, ystand = TRUE, xstand = TRUE, alpha = 0.05)

Arguments

y	A numerical vector with continuous data.
x	A matrix with numerical data; the independent variables, of which some will probably be selected.
ystand	If this is TRUE the response variable is centered. The mean is subtracted from every value.
xstand	If this is TRUE the independent variables are standardised.
alpha	The significance level.

Details

Variable selection for continuous data only is performed using the PC-simple algorithm (Buhlmann, Kalisch and Maathuis, 2010). The PC algorithm used to infer the skeleton of a Bayesian Network has been adopted in the context of variable selection. In other words, the PC algorithm is used for a single node.

Value

A list including:

vars	A vector with the selected variables.
n.tests	The number of tests performed.
runtime	The runtime of the algorithm.

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Buhlmann P., Kalisch M. and Maathuis M. H. (2010). Variable selection in high-dimensional linear models: partially faithful distributions and the PC-simple algorithm. Biometrika, 97(2): 261–278.

See Also

mmpc, cor.fbed

Examples

x <- matrix( rnorm(50 * 50), ncol = 50 )
y <- rnorm(50)
a <- pchc::pc.sel(y, x)

pchc documentation built on April 4, 2025, 1:11 a.m.