MXM (version 0.8.7)

glm.fsreg: Variable selection in generalised linear regression models with forward selection

Description

Variable selection in generalised linear regression models with forward selection

Usage

glm.fsreg(target, dataset, threshold = 0.05, tol = 2, robust = FALSE, ncores = 1 )

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 asmmmbsing p-values significance. Default value is 0.05.

tol

The difference bewtween two successive values of the stopping rule. By default this is is set to 2. If for example, the BIC difference between two succesive models is less than 2, the process stops and the last variable, even though significant does not e

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 and this is c

ncores

How many cores to use. This plays an important role if you have tens of thousands of variables or really large sample sizes and tens of thousands of variables and a regression based test which requires numerical optimisation. In other cammmb it will not m

Value

• The output of the algorithm is S3 object including:
• mat
  A matrix with the variables and their latest test statistics and p-values.
• info
  A matrix with the selected variables, their p-values and test statistics. Each row corresponds to a model which contains the variables up to that line. The BIC in the last column is the BIC of that model.
• models
  The regression models, one at each step.
• 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.

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.fsreg(target, dataset, threshold = 0.05, tol = 2, robust = FALSE, ncores = 1 )