Summarizing Normal Inverse Gaussian Distribution Fit

Description

summary Method for class "gigFit".

Usage

## S3 method for class 'gigFit'
summary(object, hessian = FALSE,
        hessianMethod = c("tsHessian","exact"), ...)

## S3 method for class 'summary.gigFit'
print(x,
                               digits = max(3, getOption("digits") - 3), ...)

Arguments

Details

If hessian = FALSE no calculations are performed, the class of object is simply changed from gigFit to summary.gigFit so that it can be passed to print.summary.gigFit for printing in a convenient form.

If hessian = TRUE the Hessian is calculated via a call to gigHessian and the standard errors of the parameter estimates are calculated using the Hessian and these are added to the original list object. The class of the object returned is again changed to summary.gigFit.

Value

summary.gigFit returns a list comprised of the original object object and additional elements hessian and sds if hessian = TRUE, otherwise it returns the original object. The class of the object returned is changed to summary.gigFit.

See gigFit for the composition of an object of class gigFit.

If the Hessian and standard errors have not been added to the object x, print.summary.gigFit prints a summary in the same format as print.gigFit. When the Hessian and standard errors are available, the Hessian is printed and the standard errors for the parameter estimates are printed in parentheses beneath the parameter estimates, in the manner of fitdistr in the package MASS.

Author(s)

David Scott d.scott@auckland.ac.nz, Christine Yang Dong c.dong@auckland.ac.nz

See Also

gigFit, summary, gigHessian.

Examples

### Continuing the  gigFit(.) example:
param <- c(1,1,1)
dataVector <- rgig(500, param = param)
fit <- gigFit(dataVector)
print(fit)
summary(fit, hessian = TRUE, hessianMethod = "tsHessian")

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GeneralizedHyperbolic)
Loading required package: DistributionUtils
Loading required package: RUnit
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GeneralizedHyperbolic/summary.gigFit.Rd_%03d_medium.png", width=480, height=480)
> ### Name: summary.gigFit
> ### Title: Summarizing Normal Inverse Gaussian Distribution Fit
> ### Aliases: summary.gigFit print.summary.gigFit
> ### Keywords: distribution
> 
> ### ** Examples
> 
> ### Continuing the  gigFit(.) example:
> param <- c(1,1,1)
> dataVector <- rgig(500, param = param)
> fit <- gigFit(dataVector)
> print(fit)

Data:      dataVector 
Parameter estimates:
   chi     psi  lambda  
0.4305  1.1983  1.3922  
Likelihood:         -571.8416 
Method:             Nelder-Mead 
Convergence code:   0 
Iterations:         280 
> summary(fit, hessian = TRUE, hessianMethod = "tsHessian")

Data:      dataVector 
Hessian:  tsHessian 
            chi        psi    lambda
[1,]  -32.63525   259.3988 -146.0166
[2,]  259.39879 -1892.1068  353.9889
[3,] -146.01664   353.9889 -171.4743
Parameter estimates:
      chi        psi      lambda 
   0.43052    1.19830    1.39225 
  (    NaN)  (0.02910)  (0.01727)
Likelihood:         -571.8416 
Method:             Nelder-Mead 
Convergence code:   0 
Iterations:         280 
Warning message:
In sqrt(diag(varcov)) : NaNs produced
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>