outerEstim: PLS outer estimation

In matrixpls: Matrix-Based Partial Least Squares Estimation

Description

Calculates a set of unstandardized outer weights.

Mode A outer weights are correlations between indicators and composites. Mode B outer weights are regression coefficients of composites on indicators.

For information about GSCA weights, see GSCA.

Usage

outerEstim.modeA(S, W, E, W.model, ...)

outerEstim.modeB(S, W, E, W.model, ...)

outerEstim.gsca(S, W, E, W.model, model, ...)

outerEstim.fixed(S, W, E, W.model, ...)

Arguments

S	Covariance matrix of the data.
W	Weight matrix, where the indicators are on colums and composites are on the rows.
E	Inner weight matrix. A square matrix of inner estimates between the composites.
W.model	A matrix specifying the weight relationships and their starting values.
...	All other arguments are ignored.
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 matrix of unscaled outer weights W with the same dimensions as W.model.

Functions

• outerEstim.modeA: Mode A outer estimation.

• outerEstim.modeB: Mode B outerestimation.

• outerEstim.gsca: outer estimation with generalized structured component analysis.

• outerEstim.fixed: Fixed weights. Returns the starting weights specified in W.model

References

Lohmöller J.-B. (1989) Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.

matrixpls documentation built on April 28, 2021, 5:07 p.m.