# pen.log.like: Calculating the log likelihood In penDvine: Flexible Pair-Copula Estimation in D-Vines using Bivariate Penalized Splines

## Description

Calculating the considered log likelihood.

## Usage

 1 pen.log.like(penden.env, cal=FALSE, temp=FALSE) 

## Arguments

 penden.env Containing all information, environment of paircopula() cal if TRUE, the final weights of one iteration are used for the calculation of the penalized log likelihood. temp if TRUE, the iteration for optimal weights is still in progress and the temporary weights are used for calculation.

## Details

The calculation depends on the estimated weights v, the penalized splines Phi and the penalty paramters lambda.

\eqn{l(v,lambda)=sum(log(Φ(u_i)v))-0.5*b^T \tilde{P} v}

The needed values are saved in the environment.

## Value

 pen.log.like Penalized log likelihood of the paircopula density. log.like Log-Likelihood of the paircopula density.

The values are saved in the environment.

## Author(s)

Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>

## References

Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines, Kauermann G. and Schellhase C. (2014+), Statistics and Computing (to appear).

penDvine documentation built on May 2, 2019, 1:06 p.m.