summary and print functions for sfplsim.kNN.fit and sfplsim.kernel.fit.
# S3 method for sfplsim.kernel
print(x, ...)
# S3 method for sfplsim.kNN
print(x, ...)
# S3 method for sfplsim.kernel
summary(object, ...)
# S3 method for sfplsim.kNN
summary(object, ...)The matched call.
The optimal value of the tunning parameter (h.opt or k.opt).
Coefficients of \(\hat{\theta}\) in the B-spline basis (theta.est): a vector of length(order.Bspline+nknot.theta).
The estimated vector of linear coefficients (beta.est).
The number of non-zero components in beta.est.
The indexes of the non-zero components in beta.est.
The optimal value of the penalisation parameter (lambda.opt).
The optimal value of the criterion function, i.e. the value obtained with lambda.opt, vn.opt and h.opt/k.opt
Minimum value of the penalised least-squares function. That is, the value obtained using theta.est, beta.est and lambda.opt.
The penalty function used.
The criterion used to select the tuning parameter, the penalisation parameter and vn.
The optimal value of vn.
Output of the sfplsim.kernel.fit or sfplsim.kNN.fit functions (i.e. an object of the class sfplsim.kernel or sfplsim.kNN).
Further arguments.
Output of the sfplsim.kernel.fit or sfplsim.kNN.fit functions (i.e. an object of the class sfplsim.kernel or sfplsim.kNN).
German Aneiros Perez german.aneiros@udc.es
Silvia Novo Diaz snovo@est-econ.uc3m.es
sfplsim.kernel.fit and sfplsim.kNN.fit.