dgmb-package: Simulating Data for PLS Mode B Structural Models
In dgmb: Simulating Data for PLS Mode B Structural Models

Description Details Author(s) References See Also Examples

A set of functions have been implemented to generate random data to perform Monte Carlo simulations on structural models with formative constructs (PLS-Mode-B models). The setup of the true model considers a simple structure with three formative exogenous constructs related to one formative endogenous construct. The routines take into account the interaction and nonlinear effects of the exogenous constructs on the endogenous construct.

A graphical user interface allows to fix the simulation parameters such as the number of replications, the sample size of each data set and the number of indicators for each block of variables. Using binary matrices, it is possible to set the structural relationships between constructs as well as nonlinear and interaction effects. A set of permissible outer weights and path coefficients is available to run the procedure and to obtain the data sets.

Type:	Package
Title:	Simulating Data for PLS Mode B Structural Models
Version:	1.2
Date:	2015-10-02
Depends:	abind, tcltk, MASS
License:	GPL <>=2
LazyLoad:	yes

Alba Martinez-Ruiz <amartine@ucsc.cl>, Claudia Martinez-Araneda <cmartinez@ucsc.cl> Maintainer: Alba Martinez-Ruiz <amartine@ucsc.cl>

Chin, W. W., Marcolin, B. L., and Newsted, P. R. 2003. A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217.

Dalgaard's Peter (2002) Rnews: The Newsletter of the R Project, Vol. 3.

Hanafi, M. 2007. PLS path modeling: Computation of latent variables with the estimation mode B. Computational Statistics, 22, 275-292.

Martinez-Ruiz, A., Aluja-Banet, T. (2013) Two-step PLS path modeling mode B: Nonlinear and interaction effects between formative constructs. In New Perspectives in Partial Least Squares and Related Methods, eds H. Abdi, W. Chin, V. Esposito Vinzi, G. Russolillo, and L. Trinchera, Springer Proceedings in Mathematics and Statistics, volume 56, pp. 187-199.

R Development Core Team (2011).R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.r-project.org/

Tenenhaus, M., Esposito Vinzi, V., Chatelin, Y. M., and Lauro, C. 2005. PLS path modeling. Computational Statistics & Data Analysis, 48, 159-205.

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