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.
pen.log.like(penden.env,lambda0,f.hat.val=NULL,beta.val=NULL)Containing all information, environment of pendensity()
penalty parameter lambda
matrix contains the fitted values of the response, if NULL the matrix is caught in the environment
actual parameter set beta, if NULL the vector is caught in the environment
Returns the log likelihood depending on the penalty parameter lambda.
The calculation depends on the fitted values of the response as well as on the penalty parameter lambda and the penalty matrix Dm. $$l(\beta)=\sum_{i=1}^{n} \left[ \log \{\sum_{k=-K}^K c_k(x_i,\beta) \boldsymbol\phi_k(y_i)\} \right]- \frac 12 \lambda \beta^T D_m \beta$$.
The needed values are saved in the environment.
Density Estimation with a Penalized Mixture Approach, Schellhase C. and Kauermann G. (2012), Computational Statistics 27 (4), p. 757-777.