MXM (version 0.9.5)

Forward selection with linear regression models: 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, heavy = FALSE, 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.

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. It is not suggested when robust is set to TRUE.

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.

heavy

A boolean variable specifying whether heavy computations are required or not. If for exmaple the dataset contains tens of thousands of rows, it is advised to used memory efficient GLMs and hence set this to TRUE.

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

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

References

Tsamardinos I., Aliferis C. F. and Statnikov, A. (2003). Time and sample efficient discovery of Markov blankets and direct causal relations. In Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 673-678).

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(1000 * 50, 1, 100), ncol = 50 )

#define a simulated class variable 
target <- 3 * dataset[, 10] + 2 * dataset[, 20] + 3 * dataset[, 30] + rnorm(1000, 0, 5)
a <- lm.fsreg(target, dataset, threshold = 0.05, stopping = "BIC", tol = 2, 
robust = FALSE, ncores = 1 ) 
b <- fs.reg(target, dataset, threshold = 0.05, test = NULL, stopping = "BIC", tol = 2, 
robust = TRUE, ncores = 1 )