stepAIC.ghyp {ghyp} | R Documentation |
This function performs a model selection in the scope of the
generalized hyperbolic distribution class based on the Akaike
information criterion. stepAIC.ghyp
can be used for the
univariate as well as for the multivariate case.
stepAIC.ghyp(data, dist = c("ghyp", "hyp", "NIG", "VG", "t", "gauss"), symmetric = NULL, ...)
data |
A vector , matrix or data.frame . |
dist |
A character vector of distributions from where the best fit will be identified. |
symmetric |
Either NULL , TRUE or FALSE . NULL means that
both symmetric and asymmetric models will be fitted. For symmetric models
select TRUE and for asymmetric models select FALSE . |
... |
Arguments passed to fit.ghypuv or fit.ghypmv . |
A list with components:
best.model |
The model minimizing the AIC. |
all.models |
All fitted models. |
fit.table |
A data.frame with columns model , symmetric ,
lambda , alpha.bar , aic , llh (log-Likelihood),
converged , n.iter (number of iterations) sorted according to the
aic. In the univariate case three additional columns containing
the parameters mu , sigma and gamma are added. |
David Luethi
lik.ratio.test
, fit.ghypuv
and fit.ghypmv
.
data(indices) # Multivariate case: aic.mv <- stepAIC.ghyp(indices, dist = c("ghyp", "hyp", "t", "gauss"), symmetric = NULL, control = list(maxit = 500), silent = TRUE, nit = 500) summary(aic.mv$best.model) # Univariate case: aic.uv <- stepAIC.ghyp(indices[, "stock"], dist = c("ghyp", "NIG", "VG", "gauss"), symmetric = TRUE, control = list(maxit = 500), silent = TRUE) # Test whether the ghyp-model provides a significant improvement with # respect to the VG-model: lik.ratio.test(aic.uv$all.models[[1]], aic.uv$all.models[[3]])