dist-snigFit: Fit of a Stndardized NIG Distribution In fBasics: Rmetrics - Markets and Basic Statistics

Description

Estimates the parameters of a standardized normal inverse Gaussian distribution.

Usage

 1 2 3 snigFit(x, zeta = 1, rho = 0, scale = TRUE, doplot = TRUE, span = "auto", trace = TRUE, title = NULL, description = NULL, ...)

Arguments

 zeta, rho shape parameter zeta is positive, skewness parameter rho is in the range (-1, 1). description a character string which allows for a brief description. doplot a logical flag. Should a plot be displayed? scale a logical flag, by default TRUE. Should the time series be scaled by its standard deviation to achieve a more stable optimization? 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. title a character string which allows for a project title. trace a logical flag. Should the parameter estimation process be traced? x a numeric vector. ... parameters to be parsed.

Value

The function snigFit returns a list with the following components:

 estimate the point at which the maximum value of the log liklihood function is obtained. minimum the value of the estimated maximum, i.e. the value of the log liklihood function. code an integer indicating why the optimization process terminated. 1: relative gradient is close to zero, current iterate is probably solution; 2: successive iterates within tolerance, current iterate is probably solution; 3: last global step failed to locate a point lower than estimate. Either estimate is an approximate local minimum of the function or steptol is too small; 4: iteration limit exceeded; 5: maximum step size stepmax exceeded five consecutive times. Either the function is unbounded below, becomes asymptotic to a finite value from above in some direction or stepmax is too small. gradient the gradient at the estimated maximum. steps number of function calls.

Examples

 1 2 3 4 5 6 7 8 9 ## snigFit - # Simulate Random Variates: set.seed(1953) s = rsnig(n = 2000, zeta = 0.7, rho = 0.5) ## snigFit - # Fit Parameters: snigFit(s, zeta = 1, rho = 0, doplot = TRUE)

fBasics documentation built on March 13, 2020, 9:09 a.m.