eBIC {REMixed}R Documentation

eBIC

Description

Computes extended bayesian information criterion as

eBIC = -2\mathcal{LL}_{y}(\hat\theta,\hat\alpha)+P\log(N)+2\gamma\log(\binom(k,K))

where P is the total number of parameters estimated, N the number of subject, \mathcal{LL}_{y}(\hat\theta,\hat\alpha) the log-likelihood of the model, K the number of submodel to explore (here the numbre of biomarkers tested) and k the numbre of biomarkers selected in the model.

Usage

eBIC(object, ...)

Arguments

object

output of remix or cv.remix.

...

opptional additional arguments.

Value

eBIC.

References

Chen, J. and Z. Chen. 2008. Extended Bayesian information criteria for model selection with large model spaces. Biometrika 95 (3): 759-771.

Examples

## Not run: 
project <- getMLXdir()

ObsModel.transfo = list(S=list(AB=log10),
                        linkS="yAB",
                        R=rep(list(S=function(x){x}),5),
                        linkR = paste0("yG",1:5))

alpha=list(alpha0=NULL,
           alpha1=setNames(paste0("alpha_1",1:5),paste0("yG",1:5)))

y = c(S=5,AB=1000)
lambda = 1440

res = remix(project = project,
            dynFUN = dynFUN_demo,
            y = y,
            ObsModel.transfo = ObsModel.transfo,
            alpha = alpha,
            selfInit = TRUE,
            eps1=10**(-2),
            eps2=1,
            lambda=lambda)

eBIC(res)

## End(Not run)

[Package REMixed version 0.1.0 Index]