View source: R/likelihood_stats.R
likelihood_stats | R Documentation |
unitquantreg
objects.Computes the likelihood-based statistics (Neg2LogLike, AIC, BIC and HQIC)
from unitquantreg
objects.
likelihood_stats(..., lt = NULL)
## S3 method for class 'likelihood_stats'
print(x, ...)
... |
|
lt |
a list with one or more |
x |
object of class |
Neg2LogLike: The log-likelihood is reported as
Neg2LogLike= -2\log(L)
AIC: The Akaike's information criterion (AIC) is defined as
AIC = -2\log(L)+2p
BIC: The Schwarz Bayesian information criterion (BIC) is defined as
BIC = -2\log(L) + p\log(n)
HQIC: The Hannan and Quinn information criterion (HQIC) is defined as
HQIC = -2\log(L) + 2p\log[\log(n)]
where L
is the likelihood function.
A list with class "likelihood_stats"
containing the following
components:
call |
the matched call. |
stats |
ordered matrix according AIC value containg the likelihood based statistics. |
André F. B. Menezes
Josmar Mazucheli
Akaike, H. (1974). A new look at the statistical model identification. IEEE Transaction on Automatic Control, 19(6), 716–723.
Hannan, E. J. and Quinn, B. G. (1979). The determination of the order of an autoregression. Journal of the Royal Statistical Society, Series B, 41(2), 190–195.
Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6(2), 461–464.
data(sim_bounded, package = "unitquantreg")
sim_bounded_curr <- sim_bounded[sim_bounded$family == "uweibull", ]
models <- c("uweibull", "kum", "ulogistic")
lt_fits <- lapply(models, function(fam) {
unitquantreg(formula = y1 ~ x, tau = 0.5, data = sim_bounded_curr,
family = fam)
})
ans <- likelihood_stats(lt = lt_fits)
ans
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