fit.ghypuv {ghyp} | R Documentation |
This function performs a maximum likelihood parameter estimation for univariate generalized hyperbolic distributions.
fit.ghypuv(data, lambda = 1, alpha.bar = 0.1, mu = mean(data), sigma = sqrt(var(data)), gamma = 0, opt.pars = c(lambda = T, alpha.bar = T, mu = T, sigma = T, gamma = !symmetric), symmetric = F, save.data = T, na.rm = T, silent = FALSE, ...) fit.hypuv(data, opt.pars = c(alpha.bar = T, mu = T, sigma = T, gamma = !symmetric), symmetric = F, ...) fit.NIGuv(data, opt.pars = c(alpha.bar = T, mu = T, sigma = T, gamma = !symmetric), symmetric = F, ...) fit.VGuv(data, lambda = 1, opt.pars = c(lambda = T, mu = T, sigma = T, gamma = !symmetric), symmetric = F, ...) fit.tuv(data, nu = 4, opt.pars = c(nu = T, mu = T, sigma = T, gamma = !symmetric), symmetric = F, ...)
data |
A vector or univariate data.frame . |
lambda |
Shape parameter. |
alpha.bar |
Shape parameter. |
nu |
Shape parameter only used in case of a student-t distribution. It determines
the degree of freedom and is defined as -2*lambda . |
mu |
Location parameter. |
sigma |
Dispersion parameter. |
gamma |
Skewness parameter. |
opt.pars |
A named logical vector which states which parameters should be fitted. |
symmetric |
If TRUE the skewness parameter gamma keeps zero. |
save.data |
If TRUE data will be stored within the
mle.ghypuv object. |
na.rm |
If TRUE missing values will be removed from data . |
silent |
If TRUE no prompts will appear in the console. |
... |
Arguments passed to optim and to fit.ghypuv when
fitting special cases of the generalized hyperbolic distribution. |
The general-purpose optimization routine optim
is used to maximize
the loglikelihood function. The default method is that of Nelder and Mead which
uses only function values. Parameters of optim
can be passed via
the ... argument of the fitting routines.
An object of class mle.ghypuv
.
The variance gamma distribution becomes singular when x - m = 0
. This singularity
is catched and the reduced density function is computed. Because the transition is
not smooth in the numerical implementation this can rarely result in nonsensical
fits.
Wolfgang Breymann, David Lüthi
fit.ghypmv
, fit.hypmv
, fit.NIGmv
,
fit.VGmv
, fit.tmv
for multivariate fitting routines.
data(smi.stocks) fit.NIGuv(data=smi.stocks[,"SMI"],opt.pars=c(alpha.bar=FALSE),alpha.bar=1, control=list(abs.tol = 1e-5, maxit = 100))