ggrplsfit: Variable selection in Partial Linear Bivariate penalized...

In funstatpackages/GgAM: Generalized geoAdditive Models

Description

This is an internal function of package GgAM. Bivariate penalized least squares problem is solved with penalty parameter chosen by GCV or CV. Variable selection by using adaptive LASSO or group SCAD is applied in parametric coefficients.

Usage

ggrplsfit(G, criterion, method, family, ind_c, VS,
  control = plbpsm.control(), MI, ...)

Arguments

G	An object of the type returned by plbpsm when fit=FALSE.
criterion	The criterion to choose the penalty parameter lambda. "GCV" to use generalized cross validation method and "CV" for cross validation
method	'ALASSO' or 'SCAD' to penalize the coefficients for parametric part.
family	The family object, specifying the distribution and link to use.
ind_c	The given index of covariates that are selected.
VS	'TRUE' for using ALASSO/SCAD to select linear variables.
control	A list of fit control parameters to replace defaults returned by plbpsm.control. Any control parameters not supplied stay at their default values.
MI	whether model identification is conducted or not.
...	other arguments.

Details

This is an internal function of package GgAM. We propose Iteratively Reweighted Least square based algorithm to get the poilot estimation and then use it to get a a spline-backfitted local polynomial estimation. The smoothing penalty parameter could be chosen by GCV or CV using the routines: gplsfitGCV.

Value

A list of fit information.

funstatpackages/GgAM documentation built on Nov. 4, 2019, 12:59 p.m.