print.summary.sfplsim: Summarise information from SFPLSIM estimation

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

Summarise information from SFPLSIM estimation

Description

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

Usage

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

Arguments

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

Value

• 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.

Author(s)

German Aneiros Perez [e-mail]

Silvia Novo Diaz [e-mail]

See Also

sfplsim.kernel.fit and sfplsim.kNN.fit.

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