Description Usage Arguments Details Value Author(s) References

Calculating the considered log likelihood. If one chooses lambda0=0, one gets the (actual) unpenalized log likelihood. Otherwise, one gets the penalized log likelihood for the used fitted values of the response y and the actual parameter set beta.

1 | ```
pen.log.like(penden.env,lambda0,f.hat.val=NULL,beta.val=NULL)
``` |

`penden.env` |
Containing all information, environment of pendensity() |

`lambda0` |
penalty parameter lambda |

`f.hat.val` |
matrix contains the fitted values of the response, if NULL the matrix is caught in the environment |

`beta.val` |
actual parameter set beta, if NULL the vector is caught in the environment |

The calculation depends on the fitted values of the response as well as on the penalty parameter lambda and the penalty matrix Dm.

*
\eqn{l(β)=sum(log(sum(c_k(x_i,β) φ_k(y_i))))-0.5*λ β^T D_m β}*

.

The needed values are saved in the environment.

Returns the log likelihood depending on the penalty parameter lambda.

Christian Schellhase <[email protected]>

Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.

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