likelihood_stats {unitquantreg}

Likelihood-based statistics of fit for unitquantreg objects.

Description

Computes the likelihood-based statistics (Neg2LogLike, AIC, BIC and HQIC) from unitquantreg objects.

Usage

likelihood_stats(..., lt = NULL)

## S3 method for class 'likelihood_stats'
print(x, ...)

Arguments

...

unitquantreg objects separated by commas. Not use in print method.

lt

a list with one or more unitquantreg objects.

x

object of class likelihood_stats obtained from likelihood_stats function.

Details

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.

Value

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.

Author(s)

André F. B. Menezes

Josmar Mazucheli

References

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.

Examples

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