partopt.reg

matrix or data.frame with the data.

matrix or data.frame of the segmentation variables.

string indicating the method: LM or LAD

method

Further arguments passed on to <code><a rd-options="" href="/link/all.part.reg?package=genpathmox&version=0.5" data-mini-rdoc="genpathmox::all.part.reg">all.part.reg</a></code>.

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Defining the optimum partition given a set of segmentation variables (regression model)

internal

Provides a very interesting solution for
handling segmentation variables in complex statistical methodology. It
contains en extended version of the "Pathmox" algorithm (Lamberti, Sanchez
and Aluja,(2016)<doi:10.1002/asmb.2168>) in the context of
Partial Least Squares Path Modeling including the F-block
test (to detect the responsible latent endogenous equations of the
difference), the F-coefficient (to detect the path coefficients responsible
of the difference) and the "invariance" test (to realize a comparison
between the sub-models' latent variables). Furthermore, the package
contains a generalized version of the "Pathmox" algorithm to approach
different methodologies: linear regression and least absolute regression
models.

Giuseppe Lamberti

genpathmox

Generalized Pathmox Approach Segmentation Tree Analysis

partopt.reg function

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

partopt.reg: Defining the optimum partition given a set of segmentation variables (regression model)

Description

Usage

Arguments

Value

Details