certificate.of.exclusion: Certificate of exclusion from the selected variables set...

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

Certificate of exclusion from the selected variables set using SES or MMPC

Description

Information on why one ore more variables were not selected.

Usage

certificate.of.exclusion(xIndex, sesObject = NULL, mmpcObject = NULL) 
certificate.of.exclusion2(xIndex, mmpc2object) 

Arguments

xIndex	A numerical vector with the indices of the predictor variables.
sesObject	If you ran SES, wald.ses or perm.ses, give the whole SES object here, otherwise leave it NULL.
mmpcObject	If you ran MMPC, wald.mmpc or prm.mmpc, give the whole MMPC object here, otherwise leave it NULL.
mmpc2object	If you ran mmpc2, give the whole MMPC object here.

Value

A list with the conditioning variables (if any), the test statistic and the logarithm of the p-value. In case a variable has been selected a message appears.

Author(s)

Michail Tsagris

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

See Also

MMPC

Examples

set.seed(123)
#simulate a dataset with continuous data
dataset <- matrix(runif(100 * 100, 1, 100), ncol = 100)
#define a simulated class variable 
target <- 3 * dataset[, 10] + 2 * dataset[, 100] + 3 * dataset[, 20] + rnorm(100, 0, 5)
# define some simulated equivalences
dataset[, 15] <- dataset[, 10] + rnorm(100, 0, 2)
dataset[, 100] <- dataset[, 100] + rnorm(100, 0, 2) 
dataset[, 20] <- dataset[, 100] + rnorm(100, 0, 2)
# run the SES algorithm
mod1 <- SES(target, dataset, max_k = 5, threshold = 0.05, test = "testIndFisher", 
hash = TRUE, hashObject = NULL);
mod2 <- MMPC(target, dataset, max_k = 5, threshold = 0.05, test = "testIndFisher", 
hash = TRUE, hashObject = NULL);
certificate.of.exclusion(c(10, 15, 30, 45, 20), mod1)
certificate.of.exclusion(c(10, 15, 30, 45, 20), NULL, mod2)

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