Cross-validation of the FBED with LMM {MXM}

Cross-validation of the FBED with LMM

Description

Cross-validation of the FBED with LMM.

Usage

cv.fbed.lmm.reg(target, dataset, id, prior = NULL, kfolds = 10, 
                folds = NULL, alphas = c(0.01, 0.05), ks = 0:2) 

Arguments

target

The class variable. This must be a numerical vector with continuous data.

dataset

The dataset; provide a numerical a matrix (columns = variables, rows = observations).

id

This is a numerical vector of the same size as target denoting the groups or the subjects.

prior

If you have prior knowledge of some variables that must be in the variable selection phase add them here. This an be a vector (if you have one variable) or a matrix (if you more variables). This does not work during the backward phase at the moment.

kfolds

The number of the folds in the k-fold Cross Validation (integer).

folds

The folds of the data to use (a list generated by the function generateCVRuns TunePareto). If NULL the folds are created internally with the same function.

alphas

A vector of significance levels to be tested.

ks

A vector of K values to be tested.

Details

The function performs cross-validation for the FBED agortihm with clustered data using the linear mixed model. The k-folds cross-validation is on clusters. Instead of leaving observations, clusters are left aside each time.

Value

A list including:

list(vars = vars, cv = cv, perf = perf, best = best, runtime = runtime)

vars

An array with the number of selected variables for each combination of significance level and value of K.

cv

An array with the number of selected variables for each combination of significance level and value of K.

perf

A matrix with the average performance each combination of significance level and value of K.

best

The best significance level and value of K.

runtime

The runtime required.

Author(s)

Michail Tsagris

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

References

Fang Y. (2011). Asymptotic equivalence between cross-validations and Akaike information criteria in mixed-effects models. Journal of data science, 9(1), 15-21.

Eugene Demidenko (2013). Mixed Models: Theory and Applications with R, 2nd Edition. New Jersey: Wiley & Sons.

Borboudakis G. and Tsamardinos I. (2019). Forward-backward selection with early dropping. Journal of Machine Learning Research, 20(8): 1-39.

See Also

fbed.glmm.reg, fbed.gee.reg, MMPC.glmm

Examples

## Not run: 
require(lme4)
data(sleepstudy)
reaction <- sleepstudy$Reaction
subject <- sleepstudy$Subject
x1 <- sleepstudy$Days
x <- matrix(rnorm(180 * 200),ncol = 200) ## unrelated predictor variables
x <- cbind(x1, x)
m <- cv.fbed.lmm.reg(reaction, x, subject) 

## End(Not run)