# 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.