eBIC: eBIC score with or without refitting.

In genscore: Generalized Score Matching Estimators

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))

genscore documentation built on May 31, 2023, 6:28 p.m.