# pen.log.like: Calculating the log likelihood In pencopula: Flexible Copula Density Estimation with Penalized Hierarchical B-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 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.

pencopula documentation built on May 20, 2017, 12:41 a.m.

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