Vinecop.__init__

Vinecop.__init__(*args, **kwargs)

Overloaded function.

  1. __init__(self: pyvinecopulib.Vinecop, d: int) -> None

Instantiates a D-vine with all pair-copulas set to independence.

Parameter d:

The dimension (= number of variables) of the model.

  1. __init__(self: pyvinecopulib.Vinecop, structure: pyvinecopulib.RVineStructure, pair_copulas: List[List[pyvinecopulib.Bicop]] = [], var_types: List[str] = []) -> None

Instantiates an arbitrary vine copula model.

Parameter structure:

An RVineStructure object specifying the vine structure.

Parameter pair_copulas:

Bicop objects specifying the pair-copulas, namely a nested list such that pc_store[t][e] contains a Bicop object for the pair copula corresponding to tree t and edge e.

Parameter var_types:

Strings specifying the types of the variables, e.g., ("c", "d") means first variable continuous, second discrete. If empty, then all variables are set as continuous.

  1. __init__(self: pyvinecopulib.Vinecop, matrix: numpy.ndarray[numpy.uint64[m, n]], pair_copulas: List[List[pyvinecopulib.Bicop]] = [], var_types: List[str] = []) -> None

Instantiates an arbitrary vine copula model.

Parameter matrix:

An R-vine matrix specifying the vine structure.

Parameter pair_copulas:

Bicop objects specifying the pair-copulas, namely a nested list such that pc_store[t][e] contains a Bicop object for the pair copula corresponding to tree t and edge e.

Parameter var_types:

Strings specifying the types of the variables, e.g., ("c", "d") means first variable continuous, second discrete. If empty, then all variables are set as continuous.

4. __init__(self: pyvinecopulib.Vinecop, data: numpy.ndarray[numpy.float64[m, n]], structure: pyvinecopulib.RVineStructure = <pyvinecopulib.RVineStructure> 1 , var_types: List[str] = [], controls: pyvinecopulib.FitControlsVinecop = FitControlsVinecop()) -> None

Instantiates from data.

Equivalent to creating a default Vinecop() and then selecting the model using select().

Parameter data:

An \(n \times d\) matrix of observations.

Parameter structure:

An RVineStructure object specifying the vine structure. If empty, then it is selected as part of the fit.

Parameter var_types:

Strings specifying the types of the variables, e.g., ("c", "d") means first variable continuous, second discrete. If empty, then all variables are set as continuous.

Parameter controls:

See FitControlsVinecop().

  1. __init__(self: pyvinecopulib.Vinecop, data: numpy.ndarray[numpy.float64[m, n]], matrix: numpy.ndarray[numpy.uint64[m, n]] = array([], shape=(0, 0), dtype=uint64), var_types: List[str] = [], controls: pyvinecopulib.FitControlsVinecop = FitControlsVinecop()) -> None

Instantiates from data.

Equivalent to creating a default Vinecop() and then selecting the model using select().

Parameter data:

An \(n \times d\) matrix of observations.

Parameter matrix:

Either an empty matrix (default) or an R-vine structure matrix, see select(). If empty, then it is selected as part of the fit.

Parameter var_types:

Strings specifying the types of the variables, e.g., ("c", "d") means first variable continuous, second discrete. If empty, then all variables are set as continuous.

Parameter controls:

See FitControlsVinecop().

  1. __init__(self: pyvinecopulib.Vinecop, filename: str, check: bool = True) -> None

Instantiates from a JSON file.

The input file contains 2 attributes : "structure" for the vine structure, which itself contains attributes "array" for the structure triangular array and "order" for the order vector, and "pair copulas". "pair copulas" contains a list of attributes for the trees ("tree1", "tree2", etc), each containing a list of attributes for the edges ("pc1", "pc2", etc). See the corresponding method of Bicop objects for the encoding of pair-copulas.

Parameter filename:

The name of the JSON file to read.

Parameter check:

Whether to check if the "structure" node of the input represents a valid R-vine structure.