summary.hyperbFit: Summarizing Hyperbolic Distribution Fit

View source: R/summary.hyperbFit.R

summary.hyperbFitR Documentation

Summarizing Hyperbolic Distribution Fit

Description

summary Method for class "hyperbFit".

Usage

## S3 method for class 'hyperbFit'
summary(object, ...)

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

Arguments

object

An object of class "hyperbFit", resulting from a call to hyperbFit.

x

An object of class "summary.hyperbFit", resulting from a call to summary.hyperbFit.

digits

The number of significant digits to use when printing.

...

Further arguments passed to or from other methods.

Details

summary.hyperbFit calculates standard errors for the estimates of pi, zeta, delta, and mu of the hyperbolic distribution parameter vector Theta if the Hessian from the call to optim or nlm is available. Because the parameters in the call to the optimiser are pi, log(zeta), log(delta), and mu, the delta method is used to obtain the standard errors for zeta and delta.

Value

If the Hessian is available, summary.hyperbFit computes standard errors for the estimates of pi, zeta, delta, and mu, and adds them to object as object$sds. Otherwise, no calculations are performed and the composition of object is unaltered.

summary.hyperbFit invisibly returns x with class changed to summary.hyperbFit.

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

print.summary.hyperbFit prints a summary in the same format as print.hyperbFit when the Hessian is not available from the fit. When the Hessian is available, the standard errors for the parameter estimates are printed in parentheses beneath the parameter estimates, in the manner of fitdistr in the package MASS.

See Also

hyperbFit, summary.

Examples

### Continuing the  hyperbFit(.) example:
Theta <- c(2,2,2,2)
dataVector <- rhyperb(500, Theta)
fit <- hyperbFit(dataVector, method = "BFGS", hessian = TRUE)
print(fit)
summary(fit)


HyperbolicDist documentation built on March 18, 2022, 6:23 p.m.