# Calculating the log likelihood

### 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 pencopula() |

`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 b, the penalized
hierarchical B-splines Phi and the penalty paramters lambda.

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

with

*\boldsymbol{P}(λ)=∑_{j=1}{p}λ_j\boldsymbol{P}_j*

The needed values are saved in the environment.

### Value

`pen.log.like` |
Penalized log likelihood of the copula density. |

`log.like` |
Log-Likelihood of the copula density. |

The values are saved in the environment.

### Author(s)

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

### References

Flexible Copula Density Estimation with Penalized Hierarchical B-Splines, Kauermann G., Schellhase C. and Ruppert, D. (2013), Scandinavian Journal of Statistics 40(4), 685-705.