gofco: Interface with copula class

Description

gofco is an interface with the copula package. It reads out the information from a copula class object and hands them over to a specified gof test or set of tests.

Usage

gofco(
  copulaobject,
  x,
  tests = c("gofPIOSRn", "gofKernel"),
  customTests = NULL,
  margins = "ranks",
  flip = 0,
  M = 1000,
  MJ = 100,
  dispstr = "ex",
  m = 1,
  delta.J = 0.5,
  nodes.Integration = 12,
  lower = NULL,
  upper = NULL,
  seed.active = NULL,
  processes = 1
)

Value

An object of the class gofCOP with the components

method: a character which informs about the performed analysis
copula: the copula tested for
margins: the method used to estimate the margin distribution.
param.margins: the parameters of the estimated margin distributions. Only applicable if the margins were not specified as "ranks" or NULL.
theta: dependence parameters of the copulae
df: the degrees of freedem of the copula. Only applicable for t-copula.
res.tests: a matrix with the p-values and test statistics of the hybrid and the individual tests

Arguments

copulaobject: An object with of class copula from the copula package.
x: A matrix containing the data with rows being observations and columns being variables.
tests: A character vector which indicates the tests to use. Possible choices are the individual tests implemented in this package.
customTests: A character vector which indicates the customized test to use, if any. The test has to be loaded into the workspace. Currently the function containing the test has to have 2 arguments, the first one for the dataset and the second one for the copula to test for. The arguments have to be named "x" and "copula" respectively.
margins: Specifies which estimation method for the margins shall be used. The default is "ranks", which is the standard approach to convert data in such a case. Alternatively can the following distributions be specified: "beta", "cauchy", Chi-squared ("chisq"), "f", "gamma", Log normal ("lnorm"), Normal ("norm"), "t", "weibull", Exponential ("exp"). Input can be either one method, e.g. "ranks", which will be used for estimation of all data sequences. Also an individual method for each margin can be specified, e.g. c("ranks", "norm", "t") for 3 data sequences. If one does not want to estimate the margins, set it to NULL.
flip: The control parameter to flip the copula by 90, 180, 270 degrees clockwise. Only applicable for bivariate copula. Default is 0 and possible inputs are 0, 90, 180, 270 and NULL.
M: Number of bootstrapping samples in the single tests.
MJ: Size of bootstrapping sample. Only necessary if the test gofKernel is part of testset.
dispstr: A character string specifying the type of the symmetric positive definite matrix characterizing the elliptical copula. Implemented structures are "ex" for exchangeable and "un" for unstructured, see package copula.
m: Length of blocks. Only necessary if the test gofPIOSTn is part of testset.
delta.J: Scaling parameter for the matrix of smoothing parameters. Only necessary if the test gofKernel is part of testset.
nodes.Integration: Number of knots of the bivariate Gauss-Legendre quadrature. Only necessary if the test gofKernel is part of testset.
lower: Lower bound for the maximum likelihood estimation of the copula parameter. The constraint is also active in the bootstrapping procedure. The constraint is not active when a switch to inversion of Kendall's tau is necessary. Default NULL.
upper: Upper bound for the maximum likelihood estimation of the copula parameter. The constraint is also active in the bootstrapping procedure. The constraint is not active when a switch to inversion of Kendall's tau is necessary. Default NULL.
seed.active: Has to be either an integer or a vector of M+1 integers. If an integer, then the seeds for the bootstrapping procedure will be simulated. If M+1 seeds are provided, then these seeds are used in the bootstrapping procedure. Defaults to NULL, then R generates the seeds from the computer runtime. Controlling the seeds is useful for reproducibility of a simulation study to compare the power of the tests or for reproducibility of an empirical study.
processes: The number of parallel processes which are performed to speed up the bootstrapping. Shouldn't be higher than the number of logical processors.

Details

The function reads out the arguments in the copula class object. If the dependence parameter is not specified in the object, it is estimated. In case that the object describes a "t"-copula, then the same holds for the degrees of freedom. The dimension is not extracted from the object. It is obtained from the inserted dataset.

When more than one test shall be performed, the hybrid test is computed too.

For small values of M, initializing the parallelisation via processes does not make sense. The registration of the parallel processes increases the computation time. Please consider to enable parallelisation just for high values of M.

References

Yan, Jun. Enjoy the joy of copulas: with a package copula. Journal of Statistical Software 21.4 (2007): 1-21.

Examples

Run this code


data(IndexReturns2D)
copObject = normalCopula(param = 0.5)

gofco(copObject, x = IndexReturns2D, tests = c("gofPIOSRn", "gofKernel"), 
      M = 20)

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