MXM (version 0.9.7)

Backward selection regression: Variable selection in regression models with backward selection

Description

Variable selection in regression models with backward selection

Usage

bs.reg(target, dataset, threshold = 0.05, wei = NULL, test = NULL, user_test = NULL, 
robust = FALSE)

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. See also Details.

dataset

The dataset; provide either a data frame or a matrix (columns = variables, rows = samples). In either case, only two cases are avaialble, either all data are continuous, or categorical.

threshold

Threshold (suitable values in [0,1]) for assessing p-values significance. Default value is 0.05.

test

The regression model to use. Available options are most of the tests for SES and MMPC. The ones NOT available are "gSquare", "censIndER", "testIndMVreg", "testIndClogit", "testIndSpearman" and "testIndFisher". If you want to use multinomial or ordinal logistic regression, make sure your target is factor. See also SES and CondIndTests for the tests.

wei

A vector of weights to be used for weighted regression. The default value is NULL. It is not suggested when robust is set to TRUE.

user_test

A user-defined conditional independence test (provide a closure type object). Default value is NULL. If this is defined, the "test" argument is ignored.

robust

A boolean variable which indicates whether (TRUE) or not (FALSE) to use a robust version of the statistical test if it is available. It takes more time than a non robust version but it is suggested in case of outliers. Default value is FALSE.

Value

The output of the algorithm is S3 object including: The output of the algorithm is S3 object including:

Details

This functions currently implements only linear, binary logistic and Poisson regression. If the sample size is less than the number of variables a meesage will appear and no backward regression is performed.

See Also

glm.fsreg, lm.fsreg, bic.fsreg, bic.glm.fsreg, CondIndTests, MMPC, SES

Examples

set.seed(123)
#simulate a dataset with continuous data
dataset <- matrix( runif(1000 * 10, 1, 100), ncol = 10 )

#define a simulated class variable 
target <- rnorm(1000)

a <- bs.reg(target, dataset, threshold = 0.05, test = "testIndRQ") 
b <- bs.reg(target, dataset, threshold = 0.05, test = "testIndReg")