matrixpls (version 0.4.0)

params.regression: Parameter estimation with separate regression analyses

Description

Estimates the model parameters with weighted composites using separate OLS regressions.

Usage

params.regression(S, model, W, ...)

Arguments

S

Covariance matrix of the data.

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.

W

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

...

All other arguments are passed through to weightFunction and parameterEstimator.

Value

• A named vector of parameter estimates.

Details

params.regression estimates the statistical model described by model with the following steps. If model is not in the native format, it is converted to the native format containing matrices inner, reflective, and formative. The weights W and the data covariance matrix S are used to calculate the composite covariance matrix C and the indicator-composite covariance matrix IC. These are used to estimate multiple OLS regression models.

The OLS regressions are estimated separately for each of the three model parts inner, reflective, and formative. These matrices are analyzed one row at a time so that the row specifies the index of the dependent variable in the OLS regression and the non-zero elements on the row specify the indices of the independent variables.

This approach of estimating the inner and outer models separately with separate OLS regression analyses is the standard way of estimation in the PLS literature.

See Also

Other parameter estimators: params.plsc; params.plsregression; params.tsls