Description Usage Arguments Details Value Examples

Analytic solution of the minimized loss for an unpenalized asymmetric model restricted to a given support. Does not work if `symmetric == "symmetric"`

.

1 2 3 4 5 6 | ```
get_crit_nopenalty(
elts,
exclude = NULL,
exclude_eta = NULL,
previous_res = NULL
)
``` |

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

`exclude` |
Optional. A p*p binary matrix or a p^2 binary vector, where |

`exclude_eta` |
Optional. A p-binary vector, similar to |

`previous_res` |
Optional. A list, the returned list by |

If `previous_res`

is provided, `exclude`

and `exclude_eta`

must be `NULL`

or be consistent with the estimated support in `previous_res`

. If `previous_res`

and `exclude`

are both `NULL`

, assume all edges are present. The same applies to the non-profiled non-centered case when `previous_res`

and `exclude_eta`

are both `NULL`

.

A number, the refitted loss.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
# 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{get_crit_nopenalty()}) 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)
get_crit_nopenalty(elts_gauss_np, previous_res=res_nc_np)
``` |

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