hist-methods: Histogram for univariate generalized hyperbolic distributions
In ghyp: A package on the generalized hyperbolic distribution and its special cases

Description Usage Arguments Value Author(s) See Also Examples

The function hist computes a histogram of the given data values and the univariate generalized hyperbolic distribution.

## S4 method for signature 'ghyp'
hist(x, data = ghyp.data(x), gaussian = TRUE,
     log.hist = F, ylim = NULL, ghyp.col = 1, ghyp.lwd = 1,
     ghyp.lty = "solid", col = 1, nclass = 30, plot.legend = TRUE,
     location = if (log.hist) "bottom" else "topright", legend.cex = 1, ...)

`x`	Usually a fitted univariate generalized hyperbolic distribution of class `mle.ghyp`. Alternatively an object of class `ghyp` and a data vector.
`data`	An object coercible to a `vector`.
`gaussian`	If `TRUE` the probability density of the normal distribution is plotted as a reference.
`log.hist`	If `TRUE` the logarithm of the histogramm is plotted.
`ylim`	The “y” limits of the plot.
`ghyp.col`	The color of the density of the generalized hyperbolic distribution.
`ghyp.lwd`	The line width of the density of the generalized hyperbolic distribution.
`ghyp.lty`	The line type of the density of the generalized hyperbolic distribution.
`col`	The color of the histogramm.
`nclass`	A single number giving the number of cells for the histogramm.
`plot.legend`	If `TRUE` a legend is drawn.
`location`	The location of the legend. See `legend` for possible values.
`legend.cex`	The character expansion of the legend.
`...`	Arguments passed to `plot` and `qqghyp`.

No value is returned.

David Luethi

qqghyp, fit.ghypuv, hist, legend, plot, lines.

  data(smi.stocks)
  univariate.fit <- fit.ghypuv(data = smi.stocks[,"SMI"],
                               opt.pars = c(mu = FALSE, sigma = FALSE),
                               symmetric = TRUE)
  hist(univariate.fit)