MXM (version 0.9.3)

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

Description

Variable selection in generalised linear regression models with backward selection

Usage

glm.bsreg(target, dataset, threshold = 0.05)

Arguments

target

The class variable. Provide either an integer, a numeric value, or a factor. 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 asmmmbsing p-values significance. Default value is 0.05.

Value

The output of the algorithm is S3 object including:

mat

A matrix with the variables and their latest test statistics and p-values.

final

The final regression model.

runtime

The run time of the algorithm. A numeric vector. The first element is the user time, the second element is the system time and the third element is the elapsed time.

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

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

Examples

set.seed(123)
#require(gRbase) #for faster computations in the internal functions
require(hash)

#simulate a dataset with continuous data
dataset <- matrix( runif(1000 * 50, 1, 100), ncol = 50 )

#define a simulated class variable 
target <- rpois(1000, 10)

a <- glm.bsreg(target, dataset, threshold = 0.05)