print.summary.sfpl: Summarise information from SFPLM estimation

In fsemipar: Estimation, Variable Selection and Prediction for Functional Semiparametric Models

Summarise information from SFPLM estimation

Description

summary and print functions for sfpl.kNN.fit and sfpl.kernel.fit.

Usage

## S3 method for class 'sfpl.kernel'
print(x, ...)
## S3 method for class 'sfpl.kNN'
print(x, ...)
## S3 method for class 'sfpl.kernel'
summary(object, ...)
## S3 method for class 'sfpl.kNN'
summary(object, ...)

Arguments

x	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.
object	Output of the sfpl.kernel.fit or sfpl.kNN.fit functions (i.e. an object of the class sfpl.kernel or sfpl.kNN).

Value

• 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.

Author(s)

German Aneiros Perez [e-mail]

Silvia Novo Diaz [e-mail]

See Also

sfpl.kernel.fit and sfpl.kNN.fit.

fsemipar documentation built on May 29, 2024, 1:31 a.m.