plot.pencopula: Plot the estimated copula density or copula distribution.
In pencopulaCond: Estimating Non-Simplified Vine Copulas Using Penalized Splines

Description Usage Arguments Details Value Author(s) References

The function plots the estimated copula density or the copula distrubtion for a paircopula, using the R-package 'lattice'.

## S3 method for class 'pencopula'
plot(x, val = NULL, marg = TRUE, plot = TRUE, int = FALSE,
main.txt = NULL, sub.txt = NULL, contour = FALSE, cond = NULL, cuts =
20, cex = 1, cex.axes = 1, cex.contour=1, xlab = NULL, ylab = NULL,
zlab=NULL, zlim=NULL, biv.margin=NULL, show.observ=FALSE,cond.cop=FALSE,
cond.par,margin.normal=FALSE,...)

`x`	object of class 'pencopula'.
`val`	Default val = NULL, one can calculate the estimated density in for p-dimensional vector, e.g. val=c(0.5,1) for the two dimensional case.
`marg`	Default = TRUE, plotting the marginal densities.
`plot`	Default = TRUE, if 'FALSE' no plot is shown, e.g. for calculations with val != NULL.
`int`	Default = FALSE, if TRUE, the integral, i.e. the distribution of the copula density is plotted.
`main.txt`	Default = NULL shows 'd', 'D', the values of lambda, the penalty order and the degree of the B-splines.
`sub.txt`	Default = NULL shows the log-likelihood, the penalized log-likelihood and the AIC-value of the estimation.
`contour`	If TRUE, a contour plot is shown. Default = FALSE.
`cond`	Default = NULL, if the dimension of data 'p' is higher than 2, one can plot a two-dimensional conditional plot. The user specifies p-2 values for the plot, indicating with '-1'. So for a three-dimensional plot, cond=c(0,-1,-1) shows the density/distribution ith fixed first covariate and the second and third covariates vary.
`cuts`	Number of cuts for the contour plots, if contour=TRUE.
`cex`	Default = 1, determing the size of the main of the plot.
`cex.axes`	Default = 1, determing the size of the labels at the axes.
`cex.contour`	Default = 1, determing the size of the labels at the cuts of the contourplot.
`xlab`	Default = NULL and no text is printed at the xlab
`ylab`	Default = NULL and no text is printed at the ylab
`zlab`	Default = NULL and 'density' is printed at the zlab for int=FALSE and 'distribution' for int=TRUE.
`zlim`	For Default = NULL, the range of the estimated values determin zlim. Alternatively, one can suggest the range as a vector.
`biv.margin`	Determines for which parameter the bivariate marginal distribution/density is presented.
`show.observ`	Default = FALSE. If TRUE, plotting the original observation into a contourplot. For multivariate copulas the data corresponding to 'biv.margin' is plotted. Show.observ is not possible in combination with option 'cond'.
`cond.cop`	Default=FALSE. If cond.cop=TRUE, the object x have to be condtional copula - this option will disapper as the object itself contains this information.
`cond.par`	If cond.cop=TRUE, the plot is created for the conditioning argument cond.par
`margin.normal`	Default = FALSE. If TRUE, the plot is presented with margins following standard normal distribution.
`...`	further arguments

For the two dimensional plots, a equidistant grid of 51 values between 0 and 1 is constructed. The plot consists of the density or distribution values in this grid points. For plots of high dimensional data (p>2), one has to fix p-2 covariates (see 'cond').

If 'val' is not NULL, the function returns a matrix with the calculated density or distribution values for the set 'val'.

Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>

Estimating Non-Simplified Vine Copulas Using Penalized Splines, Schellhase, C. and Spanhel, F. (2017), Statistics and Computing.

pencopulaCond documentation built on May 1, 2019, 7:56 p.m.