IAMB backward selection phase {MXM}

IAMB backward selection phase

Description

IAMB backward selection phase.

Usage

iamb.bs(target, dataset, threshold = 0.05, wei = NULL, test = NULL, user_test = NULL)

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.

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".

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.

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.

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:

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.

ci_test

The conditional independence test used.

vars

The selected variables.

mat

A matrix with the selected variables and their latest test statistic and logged p-value. If no variable is selected this is NULL.

final

The final regression model.

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(500 * 10, 1, 100), ncol = 10 )
target <- rnorm(500)

a1 <- iamb.bs(target, dataset, threshold = 0.05, test = "testIndRQ") 
a2 <- bs.reg(target, dataset, threshold = 0.05, test = "testIndRQ")