Variable selection for continuous data using the PC-simple algorithm {pchc}

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. https://arxiv.org/pdf/0906.3204.pdf

See Also

mmpc, cor.fbed

Examples

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