fitted.matrixpls

matrixpls estimation result object produced by the <code><a rd-options="" href="/link/matrixpls?package=matrixpls&version=1.0.4" 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 error.


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.

Mikko Rönkkö

matrixpls

Matrix-Based Partial Least Squares Estimation

fitted.matrixpls function

matrixpls estimation result object produced by the <code><a rd-options='' href='matrixpls'>matrixpls</a></code> function.

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

Description

Usage

Arguments

Value

See Also