*! version 1.1 25may2016

help pls

Title

pls -- calculates composite variables using the partial least squares path modeling (PLS) algorithm

Syntax

pls (1 = 1)
(2 = 2)
... (N = N) [if] [in]
[, options]

Options
adjacent(1 [, 2, ..., N]) defines which composites are adjacent to each other

modeB(varlist) sets Mode B outer estimation

scheme(scheme) sets innner estimation scheme

by is allowed; see [D] by.

Description

pls calculates composite variables using the partial least squares path
modeling (PLS) algorithm.

The composites are calculated as weighted combinations of existing variables
using the weight algorithm introduced by Wold (see Wold 1982).
The composites produced by pls are identical to the composites produced by commercial
PLS software as well as the open source
matrixpls
R package except for small numerical differences due to different convergence
criterion.

Options

Options

adjacent(1 [, 2, ..., N]) defines which composites are adjacent to each other during inner
estimation. The first variable of varlist is defined as being adjacent to the
other variables. If the path scheme is used for inner estimation, the
directionality of adjacencies is from the other variables toward the first
variable.

modeB(varlist)} sets Mode B outer estimation for the composites in varlist. All other composites are calculated with Mode A outer estimation.

scheme(scheme) sets innner estimation scheme. Valid values for scheme are centroid, factor, and path. The default is the centroid method.

Remarks

This program is provided for educational purposes. It is difficult to recommend
the PLS composites for any serious empirical work
(see Rönkkö, McIntosh, and Antonakis (2015))

pls is not an official Stata command. Please cite it as:

Rönkkö, M. (2016) pls: Stata module to calculate PLS composites
https://github.com/mronkko/StataPLS

Example

The example uses the ECSI dataset (Tenenhaus et al 2005), which
is commonly used to demonstrate PLS. The data are standardized with the user written center command.

Setup the data

. sysuse ecsimobi

. center _all, inplace standardize

Calculate the PLS composites

. pls (Expectation = CUEX1-CUEX3) ///

(Satisfaction = CUSA1-CUSA3) ///

(Complaint = CUSCO) ///

(Loyalty = CUSL1-CUSL3) ///

(Image = IMAG1-IMAG5) ///

(Quality = PERQ1-PERQ7) ///

(Value = PERV1-PERV2), ///

adjacent(Expectation Image, Quality Expectation, Value Expectation Quality, ///

Satisfaction Value Quality Image Expectation, Complaint Satisfaction, Loyalty Complaint Satisfaction Image) ///

scheme("path")

Regression between the composites

. regress Expectation Image

. estimates store Expectation

. regress Quality Expectation

. estimates store Quality

. regress Value Expectation Quality

. estimates store Value

. regress Satisfaction Value Quality Image Expectation

. estimates store Satisfaction

. regress Complaint Satisfaction

. estimates store Complaint

. regress Loyalty Complaint Satisfaction Image

. estimates store Loyalty

. estimates table Expectation Quality Value Satisfaction Complaint Loyalty

References

Rönkkö, M., McIntosh, C. N., & Antonakis, J. (2015). On the adoption of partial least squares in psychological research: Caveat emptor. Personality and Individual Differences, (87), 76-84.
DOI:10.1016/j.paid.2015.07.019

Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y.-M., & Lauro, C. (2005). PLS path modeling. Computational Statistics & Data Analysis, 48(1), 159-205.
DOI:10.1016/j.csda.2004.03.005

Wold, H. (1982). Soft modeling: The basic design and some extensions.
In K. G. Jöreskog & S. Wold (Eds.), Systems under indirect observation?: causality, structure, prediction (pp. 1-54). Amsterdam: North-Holland.