iamb: IAMB variable selection

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

IAMB variable selection

Description

IAMB variable selection.

Usage

iamb(target, dataset, threshold = 0.05, wei = NULL, test = NULL, user_test = NULL, 
stopping = "BIC", tol = 2, ncores = 1, back = "iambbs")

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.
dataset	The dataset; provide either a data frame or a matrix (columns = variables, rows = observations). In either case, only two cases are avaialble, either all data are continuous, or categorical.
threshold	Threshold (suitable values in (0,1)) for assessing 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.
test	The regression model to use. Available options are most of the tests for SES and MMPC. The ones NOT available are "gSquare", "censIndER", "testIndMVreg", "testIndClogit", "testIndSpearman" and "testIndFisher" and "testIndIGreg".
user_test	A user-defined conditional independence test (provide a closure type object). Default value is NULL. If this is defined, the "test" argument is ignored.
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.
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 cases 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.
back	The backward phase. If this "iambbs" (default value) the IAMB backward phase is performed and hence the IAMB algorithm is completed. If "bs", a simple backward selection phase is performed. This way, the IAMB algorithm is slightly more general.

Details

IAMB stands for Incremental Association Markov Blanket. The algorithm comprises of a forward selection and a modified backward selection process. This functions does the modified backward selection process. In the usual backward selection, among the non singificant variabels, the one with the maximum p-value is dropped. So, one variable is removed at every step. In the IAMB backward phase, at aevery step, all non significant variables are removed. This makes it a lot faster.

Value

The output of the algorithm is a list of an S3 object including:

vars	A vector with the selected variables.
mod	The output of the backward phase. In the case of no backward procedure this is the output of the forward phase.
mess	If the forward regression returned at most one variable, no backward procedure takes place and a message appears informing the user about this.

Author(s)

Michail Tsagris

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

References

Tsamardinos, I., Aliferis, C. F., Statnikov, A. R., & Statnikov, E. (2003). Algorithms for Large Scale Markov Blanket Discovery. In FLAIRS conference, pp. 376-380.

See Also

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

Examples

set.seed(123)
dataset <- matrix( runif(100 * 50, 1, 100), ncol = 50 )

target <- rpois(100, 10)
a1 <- iamb(target, dataset, threshold = 0.05, stopping = "BIC", tol = 0, back = "iambbs")
a2 <- iamb(target, dataset, threshold = 0.05, stopping = "BIC", tol = 0, back = "bs")

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