eBIC {genscore}

eBIC score with or without refitting.

Description

Calculates the eBIC score both with and without refitting an unpenalized model restricted to the estimated support.

Usage

eBIC(res, elts, BIC_refit = TRUE, gammas = c(0, 0.5, 1))

Arguments

res

A list of results returned by get_results().

elts

A list, elements necessary for calculations returned by get_elts().

BIC_refit

A boolean, whether to get the BIC scores by refitting an unpenalized model restricted to the estimated edges, with lambda1=0, lambda2=0 and diagonal_multiplier=1. Default to TRUE.

gammas

Optional. A number of a vector of numbers. The \gamma parameter in eBIC. Default to c(0,0.5,1).

Value

A vector of length 2*length(gammas). The first length(gammas) numbers are the eBIC scores without refitting for each gamma value, and the rest are those with refitting if BIC_refit == TRUE, or Inf if BIC_refit == FALSE.

Examples

# Examples are shown for Gaussian truncated to R+^p only. For other distributions
#   on other types of domains, please refer to \code{gen()} or \code{get_elts()},
#   as the way to call this function (\code{eBIC()}) is exactly the same in those cases.
n <- 50
p <- 30
domain <- make_domain("R+", p=p)
h_hp <- get_h_hp("min_pow", 1, 3)
mu <- rep(0, p)
K <- diag(p)
dm <- 1 + (1-1/(1+4*exp(1)*max(6*log(p)/n, sqrt(6*log(p)/n))))
x <- tmvtnorm::rtmvnorm(n, mean = mu, sigma = solve(K),
       lower = rep(0, p), upper = rep(Inf, p), algorithm = "gibbs",
       burn.in.samples = 100, thinning = 10)

elts_gauss_np <- get_elts(h_hp, x, setting="gaussian", domain=domain,
                centered=FALSE, profiled=FALSE, diag=dm)
res_nc_np <- get_results(elts_gauss_np, symmetric="symmetric",
               lambda1=0.35, lambda2=2, previous_res=NULL,
               is_refit=FALSE)
eBIC(res_nc_np, elts_gauss_np, BIC_refit=TRUE, gammas=c(0,0.5,1))