MXM (version 0.9.4)

Forward selection: Variable selection in regression models with forward selection

Description

Variable selection in regression models with forward selection

Usage

fs.reg(target, dataset, threshold = 0.05, test = NULL, stopping = "BIC", 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.

test

The regression model to use. Available options are "gaussian" for normal linear regression, "beta" for beta regression, "Cox" for Cox proportional hazards, "Weibull" for Weibull regression, "binary" for binomial regression, "multinomial" for multinomial regression, "ordinal" for ordinal regression, "poisson" for poisson regression, "nb" for negative binomial regression, "zip" for zero inflated poisson regression and "speedglm" for linear, binary or poisson regression with large datasets (tens of thousands of observations).

stopping

The stopping rule. The BIC is always used for all methods. If you have linear regression though you can change this to "adjrsq" and in this case the adjusted R qaured is used.

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.

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.

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: The output of the algorithm is S3 object including:

Details

If the current 'test' argument is defined as NULL or "auto" and the user_test argument is NULL then the algorithm automatically selects the best test based on the type of the data. Particularly:

• if target is a factor, the multinomial or the binary logistic regression is used. If the target has two values only, binary logistic regression will be used.
• if target is a ordered factor, the ordered logit regression is used.
• if target is a numerical vector or a matrix with at least two columns (multivariate) linear regression is used.
• if target is discrete numerical (counts), the poisson regression conditional independence test is used. If there are only two values, the binary logistic regression is to be used.
• if target is a Surv object, the Survival conditional independence test is used.

See Also

glm.fsreg, 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 * 20, 1, 100), ncol = 20 )

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

a1 <- fs.reg(target, dataset, threshold = 0.05, test = NULL, stopping = "BIC", tol = 2, 
robust = FALSE, ncores = 1 ) 
a2 <- MMPC(target, dataset)

a3 <- fs.reg(target, dataset, threshold = 0.05, test = NULL, stopping = "BIC", tol = 2, 
robust = TRUE, ncores = 1 ) 
a4 <- MMPC(target, dataset, test="testIndReg", robust= TRUE)

target <- rbinom(1000, 1,0.6)
b1 <- fs.reg(target, dataset, threshold = 0.05, test = NULL, stopping = "BIC", tol = 2, 
robust = FALSE, ncores = 1 ) 
b2 <- MMPC(target, dataset)