invariance

A square (lower triangular) boolean matrix representing the inner
model (i.e. the path relationships between latent variables).

inner

list of vectors with column indices or column names from Data indicating
the sets of manifest variables forming each block (i.e. which manifest variables correspond to each block).

outer

character vector indicating the type of measurement for each
block. Possible values are: <code>"A", "B", "newA", "PLScore", "PLScow"</code>.
The length of <code>mode</code> must be equal to the length of <code>outer</code>.

mode

string indicating the type of inner weighting
scheme. Possible values are <code>"centroid"</code>, <code>"factorial"</code>, or
<code>"path"</code>.

scheme

optional list of string vectors indicating the type of
measurement scale for each manifest variable specified in <code>blocks</code>.
<code>scaling</code> must be specified when working with non-metric variables.
Possible values: <code>"num"</code> (numer

scaling

whether manifest variables should be standardized.
Only used when <code>scaling = NULL</code>. When (<code>TRUE</code>, data is
scaled to standardized values (mean=0 and variance=1).

scaled

internal

genpathmox provides a very interesting solution for handling
    segmentation variables in complex statistical methodology. It contains en
    extended version of the PATHMOX algorithm in the context of partial least
    square path modeling (Sanchez, 2009) 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.

invariance: Calculating the invariance test

Description

Usage

Arguments

Value

Details