print.summary.lm: Summarise information from linear models estimation

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

Summarise information from linear models estimation

Description

summary and print functions for lm.pels.fit and PVS.fit.

Usage

## S3 method for class 'lm.pels'
print(x, ...)
## S3 method for class 'PVS'
print(x, ...)
## S3 method for class 'lm.pels'
summary(object, ...)
## S3 method for class 'PVS'
summary(object, ...)

Arguments

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

Value

• The matched call.

• The estimated intercept of the model.

• 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 and vn.opt (and w.opt in the case of the PVS).

• 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 penalisation parameter and vn.

• The optimal value of vn in the case of the lm.pels object.

In the case of the PVS objects, these functions also return the optimal number of covariates required to construct the reduced model in the first step of the algorithm (w.opt). This value is selected using the same criterion employed for selecting the penalisation parameter.

Author(s)

German Aneiros Perez [e-mail]

Silvia Novo Diaz [e-mail]

See Also

lm.pels.fit and PVS.fit.

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