selection.joint: Joint method for edge selection

In haodongucsb/edgeSelection: Edge selection

Joint method for edge selection

Description

Apply joint selection method by considering joint distribution of all variables.

Usage

selection.joint(data, ...)

Arguments

data

Data frame

...

Any options can be defined. type List specifying the type of spline for each variable. alpha Parameter defining cross-validation score for smoothing parameter selection. subset Optional vector specifying a subset of observations to be used in the fitting process. na.action Function which indicates what should happen when the data contain NAs. seed Seed to be used for the random generation of "knots." prec Precision requirement for internal iterations. maxiter Maximum number of iterations allowed for internal iterations. id.basis Index of observations to be used as "knots." nbasis Number of "knots" to be used. domain Data frame specifying marginal support of density in the joint method. quad Quadrature for calculating integral in the joint method. Mandatory if variables other than factors or numerical vectors are involved. w Optional vector to specify weights for two-way interactions in the joint method.

Examples

library(gss)
data(NO2)
edge.selection(data = NO2, family = "joint", nbasis = 100)

haodongucsb/edgeSelection documentation built on May 8, 2022, 4:40 p.m.