BiCopMetaContour: Contour Plot of Bivariate Meta Distribution

In VineCopula: Statistical Inference of Vine Copulas

Contour Plot of Bivariate Meta Distribution

Description

Note: This function is deprecated and only available for backwards compatibility. See contour.BiCop() for contour plots of parametric copulas, and BiCopKDE() for kernel estimates.

Usage

BiCopMetaContour(
  u1 = NULL,
  u2 = NULL,
  bw = 1,
  size = 100,
  levels = c(0.01, 0.05, 0.1, 0.15, 0.2),
  family = "emp",
  par = 0,
  par2 = 0,
  PLOT = TRUE,
  margins = "norm",
  margins.par = 0,
  xylim = NA,
  obj = NULL,
  ...
)

Arguments

u1, u2	Data vectors of equal length with values in [0,1] (default: u1 and u2 = NULL).
bw	Bandwidth (smoothing factor; default: bw = 1).
size	Number of grid points; default: size = 100.
levels	Vector of contour levels. For Gaussian, Student-t or exponential margins the default value (levels = c(0.01, 0.05, 0.1, 0.15, 0.2)) typically is a good choice. For uniform margins we recommend levels = c(0.1, 0.3, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5) and for Gamma margins levels = c(0.005, 0.01, 0.03, 0.05, 0.07, 0.09).
family	An integer defining the bivariate copula family or indicating an empirical contour plot: "emp" = empirical contour plot (default; margins can be specified by margins) 0 = independence copula 1 = Gaussian copula 2 = Student t copula (t-copula) 3 = Clayton copula 4 = Gumbel copula 5 = Frank copula 6 = Joe copula 7 = BB1 copula 8 = BB6 copula 9 = BB7 copula 10 = BB8 copula 13 = rotated Clayton copula (180 degrees; ⁠survival Clayton'') \cr `14` = rotated Gumbel copula (180 degrees; ⁠survival Gumbel”) 16 = rotated Joe copula (180 degrees; ⁠survival Joe'') \cr `17` = rotated BB1 copula (180 degrees; ⁠survival BB1”) 18 = rotated BB6 copula (180 degrees; ⁠survival BB6'')\cr `19` = rotated BB7 copula (180 degrees; ⁠survival BB7”) 20 = rotated BB8 copula (180 degrees; “survival BB8”) 23 = rotated Clayton copula (90 degrees) '24' = rotated Gumbel copula (90 degrees) '26' = rotated Joe copula (90 degrees) '27' = rotated BB1 copula (90 degrees) '28' = rotated BB6 copula (90 degrees) '29' = rotated BB7 copula (90 degrees) '30' = rotated BB8 copula (90 degrees) '33' = rotated Clayton copula (270 degrees) '34' = rotated Gumbel copula (270 degrees) '36' = rotated Joe copula (270 degrees) '37' = rotated BB1 copula (270 degrees) '38' = rotated BB6 copula (270 degrees) '39' = rotated BB7 copula (270 degrees) '40' = rotated BB8 copula (270 degrees) '104' = Tawn type 1 copula '114' = rotated Tawn type 1 copula (180 degrees) '124' = rotated Tawn type 1 copula (90 degrees) '134' = rotated Tawn type 1 copula (270 degrees) '204' = Tawn type 2 copula '214' = rotated Tawn type 2 copula (180 degrees) '224' = rotated Tawn type 2 copula (90 degrees) '234' = rotated Tawn type 2 copula (270 degrees)
par	Copula parameter; if empirical contour plot, par = NULL or 0 (default).
par2	Second copula parameter for t-, BB1, BB6, BB7, BB8, Tawn type 1 and type 2 copulas (default: par2 = 0).
PLOT	Logical; whether the results are plotted. If PLOT = FALSE, the values x, y and z are returned (see below; default: PLOT = TRUE).
margins	Character; margins for the bivariate copula contour plot. Possible margins are: "norm" = standard normal margins (default) "t" = Student t margins with degrees of freedom as specified by margins.par "gamma" = Gamma margins with shape and scale as specified by margins.par "exp" = Exponential margins with rate as specified by margins.par "unif" = uniform margins
margins.par	Parameter(s) of the distribution of the margins if necessary (default: margins.par = 0), i.e., a positive real number for the degrees of freedom of Student t margins (see dt()), a 2-dimensional vector of positive real numbers for the shape and scale parameters of Gamma margins (see dgamma()), a positive real number for the rate parameter of exponential margins (see dexp()).
xylim	A 2-dimensional vector of the x- and y-limits. By default (xylim = NA) standard limits for the selected margins are used.
obj	BiCop object containing the family and parameter specification.
...	Additional plot arguments.

Value

x	A vector of length size with the x-values of the kernel density estimator with Gaussian kernel if the empirical contour plot is chosen and a sequence of values in xylim if the theoretical contour plot is chosen.
y	A vector of length size with the y-values of the kernel density estimator with Gaussian kernel if the empirical contour plot is chosen and a sequence of values in xylim if the theoretical contour plot is chosen.
z	A matrix of dimension size with the values of the density of the meta distribution with chosen margins (see margins and margins.par) evaluated at the grid points given by x and y.

Note

The combination family = 0 (independence copula) and margins = "unif" (uniform margins) is not possible because all z-values are equal.

Author(s)

Ulf Schepsmeier, Alexander Bauer

See Also

BiCopChiPlot(), BiCopKPlot(), BiCopLambda()

Examples

## meta Clayton distribution  with Gaussian margins
cop <- BiCop(family = 1, tau = 0.5)
BiCopMetaContour(obj = cop, main = "Clayton - normal margins")
# better:
contour(cop, main = "Clayton - normal margins")

## empirical contour plot with standard normal margins
dat <- BiCopSim(1000, cop)
BiCopMetaContour(dat[, 1], dat[, 2], bw = 2, family = "emp",
                 main = "empirical - normal margins")
# better:
BiCopKDE(dat[, 1], dat[, 2],
        main = "empirical - normal margins")

## empirical contour plot with exponential margins
BiCopMetaContour(dat[, 1], dat[, 2], bw = 2,
                 main = "empirical - exponential margins",
                 margins = "exp", margins.par = 1)
# better:
BiCopKDE(dat[, 1], dat[, 2],
         main = "empirical - exponential margins",
         margins = "exp")

VineCopula documentation built on Sept. 11, 2024, 5:26 p.m.