Description Usage Arguments Value Author(s) References See Also Examples

View source: R/pacotestRVine.R

The function can be used to test a single copula in a R-vine copula to be a (j-1)-th order partial copula.
To apply the function one needs to provide the data and a specified/estimated R-vine copula model in form of a RVineMatrix from the VineCopula-package.
Additionally, a pacotest options list, which can be generated with the `pacotestset`

function, needs to be provided.

1 | ```
pacotestRvineSingleCopula(data, RVM, pacotestOptions, tree, copulaNumber)
``` |

`data` |
A (n x d) matrix (or data frame) of [0,1] data (i.e. uniform marigns). |

`RVM` |
An RVineMatrix object (VineCopula-package) which includes the structure, the pair-copula families and parameters of an R-vine copula. |

`pacotestOptions` |
A options list generated by the |

`tree` |
The tree number (j>=2) of the copula which should be tested to be a (j-1)-th order partial copula. |

`copulaNumber` |
The number (1<= copulaNumber <= j-1) of the copula in the normalized RVineMatrix which should be tested to be a (j-1)-th order partial copula. |

A list which can, depending on the chosen test, consist of the following elements:

`pValue` |
The p-value of the test. |

`testStat` |
The value of the test statistic. |

`decisionTree` |
The decision tree used to partition the support Lmabda0 of the conditioning variable W. It is provided as a list conisisting of three nodes ( |

`S` |
The boostrapped values of the test statistic (only for the test type |

Malte S. Kurz

Kurz, M. S. and F. Spanhel (2017), "Testing the simplifying assumption in high-dimensional vine copulas", ArXiv e-prints https://arxiv.org/abs/1706.02338.

Spanhel, F. and M. S. Kurz (2015), " The partial vine copula: A dependence measure and approximation based on the simplifying assumption", ArXiv e-prints https://arxiv.org/abs/1510.06971.

`pacotest-package`

, `pacotest`

, `pacotestset`

, `pacotestRvineSeq`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | ```
# Sample data and R-vine copula selection are taken
# from the documentation of RVineStructureSelect
# of the VineCopula package.
# Obtain sample data
data(daxreturns, package ="VineCopula")
dataSet = daxreturns[1:750,1:4]
# Specify an R-vine copula model
# (can be obtained by calling: RVM = VineCopula::RVineStructureSelect(dataSet))
vineStructure = matrix(c(3,4,1,2,0,2,4,1,0,0,1,4,0,0,0,4),4,4)
families = matrix(c(0,5,2,2,0,0,2,14,0,0,0,14,0,0,0,0),4,4)
par = matrix(c(0,0.8230664,0.1933472,0.6275062,
0,0,0.2350109,1.6619945,
0,0,0,1.599363,
0,0,0,0),4,4)
par2 = matrix(c(0,0,11.757700,4.547847,
0,0,17.15717,0,
0,0,0,0,0,0,0,0),4,4)
RVM = VineCopula::RVineMatrix(vineStructure, families, par, par2)
# Specify a pacotestOptions list:
# For illustrating the functioning of the decision tree,
# grouped scatterplots and a decision tree plot are activated.
pacotestOptions = pacotestset(testType='CCC',
groupedScatterplots = TRUE,
decisionTreePlot = TRUE)
# Test for a 2-nd order partial copula
# corresponding to the variables BAYN.DE,BMW.DE
# and conditioning set ALV.DE,BAS.DE
tree = 3
copulaNumber = 1
pacotestResultList = pacotestRvineSingleCopula(dataSet, RVM,
pacotestOptions, tree, copulaNumber)
``` |

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