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 |
|
... |
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]