lm.fsreg: Variable selection in linear regression models with forward...

In MXM: Feature Selection (Including Multiple Solutions) and Bayesian Networks

Variable selection in linear regression models with forward selection

Description

Variable selection in linear regression models with forward selection

Usage

lm.fsreg(target, dataset, ini = NULL, threshold = 0.05, wei = NULL, stopping = "BIC", 
tol = 2, 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.
ini	If you have a set of variables already start with this one. Currently this can only be a matrix with continuous variables. In such cases, the dataset must also contain continuous variables only. 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. The default value is NULL. An example where weights are used is surveys when stratified sampling has occured.
stopping	The stopping rule. The BIC ("BIC") or the adjusted R^2 ("adjrsq") can be 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. If the adjusted R^2 is used, the tol should be something like 0.01 or 0.02.
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.

Details

Only linear regression (robust and non robust) is supported from this function.

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.
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.
models	The regression models, one at each step.
ci_test	The conditional independence test used, "testIndReg".
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 * 20, 1, 100), ncol = 20 )

#define a simulated class variable 
target <- 3 * dataset[, 10] + 2 * dataset[, 20] + rnorm(200, 0, 5)
a1 <- lm.fsreg(target, dataset, threshold = 0.05, stopping = "BIC", tol = 2) 

MXM documentation built on Aug. 25, 2022, 9:05 a.m.