print.summary.mfpl: Summarise information from MFPLM estimation

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

Summarise information from MFPLM estimation

Description

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

Usage

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

Arguments

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

Value

• The matched call.

• The optimal value of the tunning parameter (h.opt or k.opt).

• The optimal initial number of covariates to build the reduced model (w.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 w.opt, 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 number of covariates employed to construct the reduced model, the tuning parameter, the penalisation parameter and vn.

Author(s)

German Aneiros Perez [e-mail]

Silvia Novo Diaz [e-mail]

See Also

PVS.kernel.fit and PVS.kNN.fit.

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