my.loop: Iterative loop for calculating the optimal coefficients 'v'.
In penRvine: Flexible R-Vines Estimation Using Bivariate Penalized Splines

Description Usage Arguments Details Value Author(s) References

Calculating the optimal coefficients 'v' iteratively, using quadratic programing.

1	my.loop(penden.env)

penden.env

Containing all information, environment of pencopula()

'my.loop' optimates the log-likelihhod iteratively. Therefore, the routine checks a) the relative chance in the optimal lambda and stops the iteration, if the relative change of lambda is less than one percent. During the calculations of new weights 'v' in the routine 'new.weights', most of the values are called '.temp'. This add on underlines the temporarily values. Alternatively b) for fixed lambda, 'my.loop' checks the relative change in the weights. If the change of a) the optimal lambda or b) of the basis coefficients 'v' are greater than one percent, the the real values are overwritten with the '.temp' values.

liste

The results of each iteration are written in a matrix called 'liste', saved in the environment. 'liste' contains the penalized log-likelihood, the log-likelihood, 'lambda' and the weights 'v'.

Christian Schellhase <cschellhase@wiwi.uni-bielefeld.de>

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.