This function is intended to be used as a graphical diagnostic tool for fitted univariate generalized hyperbolic distributions. Optionally a qq-plot of the normal distribution can be added.
qqghyp(object, data = ghyp.data(object), gaussian = TRUE, line = TRUE,
main = "Generalized Hyperbolic Q-Q Plot",
xlab = "Theoretical Quantiles", ylab = "Sample Quantiles",
ghyp.pch = 1, gauss.pch = 6, ghyp.lty = "solid",
gauss.lty = "dashed", ghyp.col = "black", gauss.col = "black",
plot.legend = TRUE, location = "topleft", legend.cex = 0.8,
spline.points = 150, root.tol = .Machine$double.eps^0.5,
rel.tol = root.tol, abs.tol = root.tol^1.5, add = FALSE, ...)
Usually a fitted univariate generalized hyperbolic distribution
of class mle.ghyp
. Alternatively
an object of class ghyp
and a data vector.
An object coercible to a vector
.
If TRUE
a qq-plot of the normal distribution is plotted as a reference.
If TRUE
a line is fitted and drawn.
An overall title for the plot.
A title for the x axis.
A title for the y axis.
A plotting character, i.e., symbol to use for quantiles of the generalized hyperbolic distribution.
A plotting character, i.e., symbol to use for quantiles of the normal distribution.
The line type of the fitted line to the quantiles of the generalized hyperbolic distribution.
The line type of the fitted line to the quantiles of the normal distribution.
A color of the quantiles of the generalized hyperbolic distribution.
A color of the quantiles of the normal distribution.
If TRUE
a legend is drawn.
The location of the legend. See legend
for possible values.
The character expansion of the legend.
The number of support points when computing the quantiles.
Passed to qghyp
.
The tolerance of the quantiles. Passed to uniroot
.
The tolerance of the quantiles. Passed to integrate
.
The tolerance of the quantiles. Passed to integrate
.
If TRUE
the points are added to an existing plot window. The legend
argument then becomes deactivated.
Arguments passed to plot
.
David Luethi
hist
, fit.ghypuv
, qghyp
,
plot
,
lines
data(smi.stocks)
smi <- fit.ghypuv(data = smi.stocks[, "Swiss.Re"])
qqghyp(smi, spline.points = 100)
qqghyp(fit.tuv(smi.stocks[, "Swiss.Re"], symmetric = TRUE),
add = TRUE, ghyp.col = "red", line = FALSE)
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