ebic.bsreg: Backward selection regression using the eBIC

In MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

Backward selection regression using the eBIC

Description

Backward selection regression using the eBIC

Usage

ebic.bsreg(target, dataset, test = NULL, wei = NULL, gam = NULL) 

Arguments

target	The class variable. Provide either a string, an integer, a numeric value, a vector, a factor, an ordered factor or a Surv object.
dataset	The dataset; provide either a data frame or a matrix (columns = variables, rows = samples).
test	The available tests: "testIndReg", "testIndPois", "testIndNB", "testIndLogistic", "testIndMMReg", "testIndBinom", "censIndCR", "censIndWR", "testIndBeta", "testIndZIP", "testIndGamma", "testIndNormLog" and "testIndTobit".
wei	A vector of weights to be used for weighted regression. The default value is NULL. It is not suggested when testIndMMReg is used. An example where weights are used is surveys when stratified sampling has occured.
gam	In case the method is chosen to be "eBIC" one can also specify the gamma parameter. The default value is "NULL", so that the value is automatically calculated.

Details

The algorithm is a variation of the usual forward selection. At every step, the most significant variable enters the selected variables set. In addition, only the significant variables stay and are further examined. The non signifcant ones are dropped. This goes until no variable can enter the set. The user has the option to redo this step 1 or more times (the argument K). In the end, a backward selection is performed to remove falsely selected variables.

Value

A list including:

runtime	The runtime required.
info	A matrix with the number of variables and the number of tests performed (or models fitted) at each round (value of K).
mat	A matrix with the selected variables and their eBIC.
back.rem	The variables removed in the backward phase.
back.n.tests	The number of models fitted in the backward phase.

Author(s)

Michail Tsagris

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

References

Borboudakis G. and Tsamardinos I. (2019). Forward-backward selection with early dropping. Journal of Machine Learning Research, 20(8): 1-39.

See Also

fs.reg, bic.fsreg, MMPC

Examples

dataset <- matrix( runif(100 * 15, 1, 100), ncol = 15 )
target <- rt(100, 10)
a1 <- ebic.bsreg(target, dataset, test = "testIndReg") 
target <- rpois(100, 10)
a2 <- ebic.bsreg(target, dataset, test = "testIndPois") 

MXM documentation built on Aug. 25, 2022, 9:05 a.m.