BICc {REMixed} | R Documentation |
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, ...)
Arguments
object |
|
... |
opptional additional arguments. |
Value
BICc.
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
## 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)
BICc(res)
## End(Not run)
[Package REMixed version 0.1.0 Index]