GeneralizedHyperbolic (version 0.8-4)

nigPlots: Normal inverse Gaussian Quantile-Quantile and Percent-Percent Plots

Description

qqnig produces a normal inverse Gaussian Q-Q plot of the values in y.

ppnig produces a normal inverse Gaussian P-P (percent-percent) or probability plot of the values in y.

Graphical parameters may be given as arguments to qqnig, and ppnig.

Usage

qqnig(y, mu = 0, delta = 1, alpha = 1, beta = 0,
         param = c(mu, delta, alpha, beta),
         main = "Normal inverse Gaussian Q-Q Plot",
         xlab = "Theoretical Quantiles",
         ylab = "Sample Quantiles",
         plot.it = TRUE, line = TRUE, …)
ppnig(y, mu = 0, delta = 1, alpha = 1, beta = 0,
         param = c(mu, delta, alpha, beta),
         main = "Normal inverse Gaussian P-P Plot",
         xlab = "Uniform Quantiles",
         ylab = "Probability-integral-transformed Data",
         plot.it = TRUE, line = TRUE, …)

Arguments

y

The data sample.

mu

\(\mu\) is the location parameter. By default this is set to 0.

delta

\(\delta\) is the scale parameter of the distribution. A default value of 1 has been set.

alpha

\(\alpha\) is the tail parameter, with a default value of 1.

beta

\(\beta\) is the skewness parameter, by default this is 0.

param

Parameters of the normal inverse Gaussian distribution.

xlab, ylab, main

Plot labels.

plot.it

Logical. Should the result be plotted?

line

Add line through origin with unit slope.

…

Further graphical parameters.

Value

For qqnig and ppnig, a list with components:

x

The x coordinates of the points that are to be plotted.

y

The y coordinates of the points that are to be plotted.

References

Wilk, M. B. and Gnanadesikan, R. (1968) Probability plotting methods for the analysis of data. Biometrika. 55, 1--17.

See Also

ppoints, dnig, nigFit

Examples

# NOT RUN {
par(mfrow = c(1, 2))
param <- c(2, 2, 2, 1.5)
y <- rnig(200, param = param)
qqnig(y, param = param, line = FALSE)
abline(0, 1, col = 2)
ppnig(y, param = param)
# }