new.weights: Calculating new weights v.

Description Usage Arguments Details Value Author(s) References

View source: R/new.weights.r


Calculating new weights v using quadratic programing.


new.weights(penden.env, lambda.temp=NULL,start=FALSE)



Containing all information, environment of paircopula()


Default=NULL, if optimal lambda is calculated, the lambda are saved temporarily, also the resulted coefficients are saved temporarily until some convergences.


Default=FALSE, indicating if the first calculation of new weights is running.


The new weights are calculated solving a quadratic program. Therefore, the derivates of first and second order are needed, 'Derv1.pen' and 'Derv2.pen'. Moreover, we have to fulfill the side conditions v>=0, sum(v)=1 and that the marginal densities are uniform. All side conditions are saved as '' in the environment.

If the quadratic program does not find a new feasible solution, the whole program terminates. For solving the quadratic program, we use the function 'solve.QP' from the R-package 'quadprog'.



Calculated new values for the weights 'v'. The add on 'temp' means, that there is a check in the next step if the weights 'v' have been converted (in the case of fixed lambda). If converted, the new values 'ck.val.temp' are unnoted. If not converted, 'ck.val.temp' become the ordinary 'ck.val' for the next iteration. This check is done in my.loop. If the optimal value of lambda is calculated, the coefficients 'ck.val.temp' become the ordinary 'ck.val' for the next iteration if lambda is converted. t

'ck.val.temp' is saved in the environment.


Christian Schellhase <>


Flexible Pair-Copula Estimation in D-vines using Bivariate Penalized Splines, Kauermann, G. and Schellhase, C. (2014), Statistics and Computing 24(6): 1081-1100).

Nonparametric estimation of simplified vines: comparison of methods, Nagler N., Schellhase, C. and Czado, C. (2017) Dependence Modeling.

penRvine documentation built on May 30, 2017, 2:20 a.m.