REMixed (version 1.1.2)

BICc: BICc

Description

Computes corrected bayesian information criterion as $$ BICc = -2\mathcal{LL}_{y}(\hat\theta,\hat\alpha)+P_R\log(N)+P_F\log(n_{tot})$$ where \(P_F\) is the total number of parameters linked to fixed effects, \(P_R\) to random effects, \(N\) the number of subject, \(n_tot\) the total number of observations and \(\mathcal{LL}_{y}(\hat\theta,\hat\alpha)\) the log-likelihood of the model.

Usage

BICc(object, ...)

Value

BICc.

Arguments

object

output of remix or cv.remix

...

opptional additional arguments.

References

Delattre M, Lavielle M, Poursat M-A. A note on BIC in mixed-effects models. Elect J Stat. 2014; 8(1): 456-475.

Examples

if (FALSE) {
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)

BICc(res)
}