fsemipar (version 1.1.1)

print.summary.mfpl: Summarise information from MFPLM estimation

Description

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

Usage

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

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.

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).

Author

German Aneiros Perez

Silvia Novo Diaz

See Also

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