cond.cop: Flexible Pair-Copula Estimation in R-vines with Penalized...

In penRvine: Flexible R-Vines Estimation Using Bivariate Penalized Splines

Description

Calculation of the conditional paircopulas.

Usage

cond.cop(data,coef,K,diff="u2",Index.basis.D,base,q=2,env,kn1,kn2,int.base1,int.base2,ddb)

Arguments

data	Considered bivariate data, later used as "u1" and "u2".
coef	Considered coefficients of the splines.
K	Number of marginal knots.
diff	Default="u2", alternatively diff="u1". Determining in which direction the pair-copula is differentiated.
Index.basis.D	Vector of indices built in the program before.
base	"B-spline" or "Bernstein".
q	Order of the B-spline, default=2
env	Environment with needed data.
kn1	Marginal knots for the first covariate.
kn2	Marginal knots for the second covariate.
int.base1	Integrated marginal density basis of the first covariate.
int.base2	Integrated marginal density basis of the second covariate.
ddb	Number of coefficients

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Author(s)

Christian Schellhase <[e-mail]>

References

Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines, Kauermann, G. and Schellhase, C. (2014), Statistics and Computing 24(6): 1081-1100).

Nonparametric estimation of simplified vines: comparison of methods, Nagler N., Schellhase, C. and Czado, C. (2017) Dependence Modeling.

penRvine documentation built on May 30, 2017, 2:20 a.m.