Calculating the considered log likelihood.
pen.log.like(penden.env, cal=FALSE, temp=FALSE)Containing all information, environment of pencopula()
if TRUE, the final weights of one iteration are used for the calculation of the penalized log likelihood.
if TRUE, the iteration for optimal weights is still in progress and the temporary weights are used for calculation.
Penalized log likelihood of the copula density.
Log-Likelihood of the copula density.
The calculation depends on the estimated weights b, the penalized hierarchical B-splines Phi and the penalty paramters lambda. $$l(b,\lambda)=\sum_{i=1}^{n} \left[ \log \{\sum_{i=1}^n \boldsymbol\Phi(u_i)\} b\right]- \frac 12 b^T \boldsymbol{P}(\lambda) b$$ with $$\boldsymbol{P}(\lambda)=\sum_{j=1}{p}\lambda_j\boldsymbol{P}_j$$
The needed values are saved in the environment.
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.