fitted.matrixpls

matrixpls estimation result object produced by the <code><a href="/link/matrixpls?package=matrixpls&version=0.6.0" rd-options="" data-mini-rdoc="matrixpls::matrixpls">matrixpls</a></code> function.

object

The <code>matrixpls</code> method for generic function <code>fitted</code> computes the model implied
covariance matrix by combining  <code>inner</code>, <code>reflective</code>, and <code>formative</code> as a 
simultaneous equations system. The error terms are constrained to be uncorrelated and 
covariances between exogenous variables are fixed at their sample values. Defining a
composite as dependent variable in both inner and formative creates an impossible model
and results in an errors

Partial Least Squares Path Modeling
    algorithm and related algorithms. The algorithm implementations aim for
    computational efficiency using matrix algebra and covariance data. The
    package is designed toward Monte Carlo simulations and includes functions
    to perform simple Monte Carlo simulations.

fitted.matrixpls: Model implied covariance matrix baesd on matrixpls results

Description

Usage

Arguments

Value

See Also