pairs-methods: Pairs plot for multivariate generalized hyperbolic...

In ghyp: A package on the generalized hyperbolic distribution and its special cases

Description

This function is intended to be used as a graphical diagnostic tool for fitted multivariate generalized hyperbolic distributions. An array of graphics is created and qq-plots are drawn into the diagonal part of the graphics array. The upper part of the graphics matrix shows scatter plots whereas the lower part shows 2-dimensional histogramms.

Usage

## S4 method for signature 'ghyp'
pairs(x, data = ghyp.data(x), main = "'ghyp' pairwise plot",
      nbins = 30, qq = TRUE, gaussian = TRUE,
      hist.col = c("white", topo.colors(40)),
      spline.points = 150, root.tol = .Machine$double.eps^0.5,
      rel.tol = root.tol, abs.tol = root.tol^1.5, ...)

Arguments

x	Usually a fitted multivariate generalized hyperbolic distribution of class mle.ghyp. Alternatively an object of class ghyp and a data matrix.
data	An object coercible to a matrix.
main	The title of the plot.
nbins	The number of bins passed to hist2d.
qq	If TRUE qq-plots are drawn.
gaussian	If TRUE qq-plots with the normal distribution are plotted.
hist.col	A vector of colors passed to hist2d.
spline.points	The number of support points when computing the quantiles used by the qq-plot. Passed to qqghyp.
root.tol	The tolerance of the quantiles. Passed to uniroot via qqghyp.
rel.tol	The tolerance of the quantiles. Passed to integrate via qqghyp.
abs.tol	The tolerance of the quantiles. Passed to integrate via qqghyp.
...	Arguments passed to plot and axis.

Author(s)

David Luethi

See Also

pairs, fit.ghypmv, qqghyp, hist2d

Examples

  data(smi.stocks)
  fitted.smi.stocks <- fit.NIGmv(data = smi.stocks[1:200, ])
  pairs(fitted.smi.stocks)

Example output

Loading required package: numDeriv
Loading required package: MASS
[1] "iter: 1; rel.closeness:  2.648171E-03; log-likelihood:  3.743345E+03; alpha.bar:  1.173236E+00; lambda: -5.000000E-01"
[1] "iter: 2; rel.closeness:  3.324621E-04; log-likelihood:  3.744589E+03; alpha.bar:  1.279237E+00; lambda: -5.000000E-01"
[1] "iter: 3; rel.closeness:  9.192469E-05; log-likelihood:  3.744934E+03; alpha.bar:  1.351859E+00; lambda: -5.000000E-01"
[1] "iter: 4; rel.closeness:  4.042908E-05; log-likelihood:  3.745085E+03; alpha.bar:  1.405265E+00; lambda: -5.000000E-01"
[1] "iter: 5; rel.closeness:  2.337866E-05; log-likelihood:  3.745173E+03; alpha.bar:  1.446574E+00; lambda: -5.000000E-01"
[1] "iter: 6; rel.closeness:  1.539191E-05; log-likelihood:  3.745230E+03; alpha.bar:  1.479595E+00; lambda: -5.000000E-01"
[1] "iter: 7; rel.closeness:  1.062125E-05; log-likelihood:  3.745270E+03; alpha.bar:  1.506556E+00; lambda: -5.000000E-01"
[1] "iter: 8; rel.closeness:  7.428102E-06; log-likelihood:  3.745298E+03; alpha.bar:  1.528909E+00; lambda: -5.000000E-01"
[1] "iter: 9; rel.closeness:  5.169490E-06; log-likelihood:  3.745317E+03; alpha.bar:  1.547403E+00; lambda: -5.000000E-01"
[1] "iter: 10; rel.closeness:  3.589353E-06; log-likelihood:  3.745331E+03; alpha.bar:  1.562784E+00; lambda: -5.000000E-01"
[1] "iter: 11; rel.closeness:  2.489505E-06; log-likelihood:  3.745340E+03; alpha.bar:  1.575647E+00; lambda: -5.000000E-01"
[1] "iter: 12; rel.closeness:  1.726858E-06; log-likelihood:  3.745346E+03; alpha.bar:  1.586458E+00; lambda: -5.000000E-01"
[1] "iter: 13; rel.closeness:  1.192424E-06; log-likelihood:  3.745351E+03; alpha.bar:  1.595492E+00; lambda: -5.000000E-01"
[1] "iter: 14; rel.closeness:  8.245572E-07; log-likelihood:  3.745354E+03; alpha.bar:  1.603081E+00; lambda: -5.000000E-01"
[1] "iter: 15; rel.closeness:  5.625525E-07; log-likelihood:  3.745356E+03; alpha.bar:  1.609299E+00; lambda: -5.000000E-01"
[1] "iter: 16; rel.closeness:  3.894250E-07; log-likelihood:  3.745358E+03; alpha.bar:  1.614541E+00; lambda: -5.000000E-01"
[1] "iter: 17; rel.closeness:  2.687870E-07; log-likelihood:  3.745359E+03; alpha.bar:  1.618928E+00; lambda: -5.000000E-01"
[1] "iter: 18; rel.closeness:  1.852681E-07; log-likelihood:  3.745359E+03; alpha.bar:  1.622588E+00; lambda: -5.000000E-01"
[1] "iter: 19; rel.closeness:  1.280813E-07; log-likelihood:  3.745360E+03; alpha.bar:  1.625659E+00; lambda: -5.000000E-01"
[1] "iter: 20; rel.closeness:  8.941958E-08; log-likelihood:  3.745360E+03; alpha.bar:  1.628284E+00; lambda: -5.000000E-01"
[1] "iter: 21; rel.closeness:  6.126518E-08; log-likelihood:  3.745360E+03; alpha.bar:  1.630456E+00; lambda: -5.000000E-01"
[1] "iter: 22; rel.closeness:  4.273453E-08; log-likelihood:  3.745360E+03; alpha.bar:  1.632325E+00; lambda: -5.000000E-01"
[1] "iter: 23; rel.closeness:  2.611407E-08; log-likelihood:  3.745361E+03; alpha.bar:  1.633576E+00; lambda: -5.000000E-01"
[1] "iter: 24; rel.closeness:  1.964907E-08; log-likelihood:  3.745361E+03; alpha.bar:  1.634829E+00; lambda: -5.000000E-01"
[1] "iter: 25; rel.closeness:  1.282245E-08; log-likelihood:  3.745361E+03; alpha.bar:  1.635771E+00; lambda: -5.000000E-01"
[1] "iter: 26; rel.closeness:  8.826964E-09; log-likelihood:  3.745361E+03; alpha.bar:  1.636557E+00; lambda: -5.000000E-01"

ghyp documentation built on May 2, 2019, 6:09 p.m.