params.plsregression: Parameter estimation with PLS regression

Description

Estimates the model parameters with weighted composites using separate OLS regressions for outer model and separate PLS regressions for inner model.

Usage

params.plsregression(S, W, model)

Arguments

Covariance matrix of the data.

Weight matrix, where the indicators are on colums and composites are on the rows.

model

There are two options for this argument: 1. lavaan script or lavaan parameter table, or 2. a list containing three matrices inner, reflective, and formative defining the free regression paths in the model.

Value

A named vector of parameter estimates.

Details

params.plsregression estimates the model parameters similarly to params.regression with the exception that instead of separate OLS regressions the inner part of the model is estimated with separate PLS regressions using the PLS1 algorithm with two rounds of estimation.

The implementation of PLS regression is ported from the raw data version implemented in get_plsr1 funtion of the plspm package.

References

Sanchez, G. (2013). PLS Path Modeling with R. Retrieved from http://www.gastonsanchez.com/PLS Path Modeling with R.pdf

Bjørn-Helge Mevik, & Ron Wehrens. (2007). The pls Package: Principal Component and Partial Least Squares Regression in R. Journal of Statistical Software, 18. Retrieved from http://www.jstatsoft.org/v18/i02/paper