Forward selection with generalised linear regression models {MXM}

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, ini = NULL, threshold = 0.05, wei = NULL, tol = 2, 
ncores = 1) 

gammafsreg(target, dataset, ini = NULL, threshold = 0.05, wei = NULL, tol = 2, 
ncores = 1) 

normlog.fsreg(target, dataset, ini = NULL, threshold = 0.05, wei = NULL, tol = 2, 
ncores = 1) 

Arguments

target

A numerical vector or a factor variable with two levels. The Gamma regression reuqires strictly positive numbers, wehreas the normlog requires positive numbes, zero included.

dataset

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

ini

If you have a set of variables already start with this one. Otherwise leave it NULL.

threshold

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

wei

A vector of weights to be used for weighted regression. An example where weights are used is surveys when stratified sampling has occured.

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 enter the model.

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 make a difference in the overall time (in fact it can be slower). The parallel computation is used in the first step of the algorithm, where univariate associations are examined, those take place in parallel. We have seen a reduction in time of 50% with 4 cores in comparison to 1 core. Note also, that the amount of reduction is not linear in the number of cores.

Value

The output of the algorithm is S3 object including:

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.

mat

A matrix with the variables and their latest test statistics and logged p-values. If you have logistic or Poisson regression with continuous predictor variables in a matrix form and no weights, this will not appear. In this case, a C++ code is called and the output is less.

info

A matrix with the selected variables, their logged 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.

ci_test

The conditional independence test used.

final

The final regression model.

Author(s)

Michail Tsagris

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

See Also

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

Examples

set.seed(123)

#simulate a dataset with continuous data
dataset <- matrix( runif(200 * 30, 1, 100), ncol = 30 )

#define a simulated class variable 
target <- rpois(200, 10)

a <- glm.fsreg(target, dataset, threshold = 0.05, tol = 2, ncores = 1 ) 
b <- MMPC(target, dataset, max_k = 3, threshold = 0.05, test = "testIndPois")