# summary.hyperbFit: Summarizing Hyperbolic Distribution Fit In HyperbolicDist: The Hyperbolic Distribution

 summary.hyperbFit R 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`.

`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.