Usage
paircopula(data,K=8,base="Bernstein",max.iter=30,lambda=100, data.frame=parent.frame(),m=2,fix.lambda=FALSE,pen=1,q=2)
Arguments
data
'data' contains the data. 'data' has to be a matrix or a
data.frame with two columns.
K
K is the degree of the Bernstein polynomials. In the case of
linear B-spline basis functions, K+1 nodes are used for the basis functions.
base
Type of basis function, default is
"Bernstein". An alternative is base="B-spline".
max.iter
maximum number of iteration, the default is max.iter=30.
lambda
Starting value for lambda, default is lambda=100.
data.frame
reference to the data. Default reference is the
parent.frame().
m
Indicating the order of differences to be penalised. Default is "m=2".
fix.lambda
Determining if lambda is fixed or if the
iteration for an optimal lambda is used, default 'fix.lambda=FALSE'.
pen
'pen' indicates the used penalty. 'pen=1' for the
difference penalty of m-th order. 'pen=2' is only implemented for
Bernstein polynomials, it is the penalty based
on the integrated squared second order derivatives of the Bernstein
polynomials. Due to numerical difficulties handling the integral of Bernstein polynomials (that is the beta function), this approach works only for K
q
Order of B-spline basis, i.e. default q=2 means linear B-spline basis.