splitopt.reg

matrix or data.frame with the data.

vector indicating the binary partition

splits

fact

string indicating the method: LM or LAD

method

Further arguments passed on to <code><a rd-options="" href="/link/splitopt.reg?package=genpathmox&version=0.6" data-mini-rdoc="genpathmox::splitopt.reg">splitopt.reg</a></code>.

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Defining optimum partition for a specific variable (regression model)

internal

It provides an interesting solution for handling a high number
of segmentation variables in partial least squares structural equation
modeling. The package implements the "Pathmox" algorithm (Lamberti, Sanchez,
and Aluja,(2016)<doi:10.1002/asmb.2168>) including the F-coefficient
test (Lamberti, Sanchez, and Aluja,(2017)<doi:10.1002/asmb.2270>)
to detect the path coefficients responsible for the identified differences),
the hybrid multi-group approach (Lamberti (2021)
<doi:10.1007/s11135-021-01096-9>) and the classical multi-group
approaches (parametric and permutation approaches). The package also
includes an extension of the "Pathmox" algorithm to the case of
linear regression models.

Giuseppe Lamberti

genpathmox

Generalized Pathmox Approach Segmentation Tree Analysis

splitopt.reg function

Further arguments passed on to <code><a rd-options='' href='splitopt.reg'>splitopt.reg</a></code>.

splitopt.reg: Defining optimum partition for a specific variable (regression model)

Description

Usage

Arguments

Value

Details