logLik.gcrq {quantregGrowth} | R Documentation |
Log Likelihood, AIC and BIC for gcrq objects
Description
The function returns the log-likelihood value(s) evaluated at the estimated coefficients
Usage
## S3 method for class 'gcrq'
logLik(object, summ=TRUE, ...)
## S3 method for class 'gcrq'
AIC(object, ..., k=2, bondell=FALSE)
Arguments
object |
A |
summ |
If |
k |
Optional numeric specifying the penalty of the edf in the AIC formula. |
bondell |
Logical. If |
... |
optional arguments (nothing in |
Details
The 'logLikelihood' is computed by assuming an asymmetric Laplace distribution for the response as in logLik.rq
, namely n (\log(\tau(1-\tau))-1-\log(\rho_\tau/n))
, where \rho_\tau
is the minimized objective function. When there are multiple quantile curves j=1,2,...,J
(and summ=TRUE
) the formula is
n (\sum_j\log(\tau_j(1-\tau_j))-J-\log(\sum_j\rho_{\tau_j}/(n J)))
AIC.gcrq
simply returns -2*logLik + k*edf
where k
is 2 or log(n)
.
Value
The log likelihood(s) of the model fit object
Author(s)
Vito Muggeo
References
Bondell HD, Reich BJ, Wang H (2010) Non-crossing quantile regression curve estimation, Biometrika, 97: 825-838.
See Also
Examples
## logLik(o) #a unique value (o is the fit object from gcrq)
## logLik(o, summ=FALSE) #vector of the log likelihood values
## AIC(o, k=-1) #BIC