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Standardized GH distribution fit

dist-sghFit.Rd

Estimates the distributional parameters for a standardized generalized hyperbolic distribution.

Usage

sghFit(x, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE,
    scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, 
    title = NULL, description = NULL, ...)

Arguments

x

a numeric vector.

zeta, rho, lambda

shape parameter zeta is positive, skewness parameter rho is in the range (-1, 1). and index parameter lambda, by default 1.

include.lambda

a logical flag, by default TRUE. Should the index parameter lambda included in the parameter estimate?

scale

a logical flag, by default TRUE. Should the time series be scaled by its standard deviation to achieve a more stable optimization?

doplot

a logical flag. Should a plot be displayed?

span

x-coordinates for the plot, by default 100 values automatically selected and ranging between the 0.001, and 0.999 quantiles. Alternatively, you can specify the range by an expression like span=seq(min, max, times = n), where, min and max are the left and right endpoints of the range, and n gives the number of the intermediate points.

trace

a logical flag. Should the parameter estimation process be traced?

title

a character string which allows for a project title.

description

a character string which allows for a brief description.

...

parameters to be parsed.

Value

an object from class "fDISTFIT". Slot fit is a list, currently with components estimate, minimum, code, param, mean (mean of the original data), var (variance of original data).

Examples

set.seed(1953)
s <- rsgh(n = 2000, zeta = 0.7, rho = 0.5, lambda = 0) 

sghFit(s, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, 
       doplot = TRUE, trace = FALSE)

#> 
#> Title:
#>  SGH Parameter Estimation 
#> 
#> Call:
#>  sghFit(x = s, zeta = 1, rho = 0, lambda = 1, include.lambda = TRUE, 
#>     doplot = TRUE, trace = FALSE)
#> 
#> Model:
#>  Standarized GH Distribution
#> 
#> Estimated Parameter(s):
#>       zeta        rho     lambda 
#>  0.6700175  0.5262540 -0.1766919 
#>