R: GH Distribution Fit

gldFit {fBasics}

R Documentation

GH Distribution Fit

Description

Estimates the distrinbutional parameters for a generalized lambda distribution.

Usage

gldFit(x, lambda1 = 0, lambda2 = -1, lambda3 = -1/8, lambda4 = -1/8, 
    method = c("mle", "mps", "gof", "hist", "rob"),
    scale = NA, doplot = TRUE, add = FALSE, span = "auto", trace = TRUE, 
    title = NULL, description = NULL, ...)

Arguments

`x`	a numeric vector.
`lambda1, lambda2, lambda3, lambda4`	are numeric values where `lambda1` is the location parameter, `lambda2` is the location parameter, `lambda3` is the first shape parameter, and `lambda4` is the second shape parameter.
`method`	a character string, the estimation approach to fit the distributional parameters, see details.
`scale`	not used.
`doplot`	a logical flag. Should a plot be displayed?
`add`	a logical flag. Should a new fit added to an existing plot?
`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 rigldt 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.

Details

The function nlminb is used to minimize the objective function. The following approaches have been implemented:

"mle", maximimum log likelihoo estimation.

"mps", maximum product spacing estimation.

"gof", goodness of fit approaches, type="ad" Anderson-Darling, type="cvm" Cramer-vonMise, type="ks" Kolmogorov-Smirnov.

"hist", histogram binning approaches, "fd" Freedman-Diaconis binning, "scott", Scott histogram binning, "sturges", Sturges histogram binning.

"rob", robust moment matching.

Value

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

    
## gldFit -
   # Simulate Random Variates:
   set.seed(1953)
   s = rgld(n = 1000, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8) 

## gldFit -  
   # Fit Parameters:
   gldFit(s, lambda1=0, lambda2=-1, lambda3=-1/8, lambda4=-1/8, 
     doplot = TRUE, trace = TRUE)

[Package fBasics version 2160.81 Index]