summary and print functions for sfpl.kNN.fit and sfpl.kernel.fit.
# S3 method for sfpl.kernel
print(x, ...)
# S3 method for sfpl.kNN
print(x, ...)
# S3 method for sfpl.kernel
summary(object, ...)
# S3 method for sfpl.kNN
summary(object, ...)The matched call.
The optimal value of the tunning parameter (h.opt or k.opt).
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 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 sfpl.kernel.fit or sfpl.kNN.fit functions (i.e. an object of the class sfpl.kernel or sfpl.kNN).
Further arguments.
Output of the sfpl.kernel.fit or sfpl.kNN.fit functions (i.e. an object of the class sfpl.kernel or sfpl.kNN).
German Aneiros Perez german.aneiros@udc.es
Silvia Novo Diaz snovo@est-econ.uc3m.es
sfpl.kernel.fit and sfpl.kNN.fit.