dgmbGui: Graphical user interface (GUI) for random data generation
In dgmb: Simulating Data for PLS Mode B Structural Models
Description
Usage
Details
Value
Author(s)
References
See Also
Examples
Description
A function to implement a graphical user interface (GUI) to fix simulation parameters.
Usage
1
dgmbGui()
Details
The following main window is displayed in R:
Figure 1 Graphical user interface for random data generation
Functions are implemented to generate data for a specific model (Figure 2). The true model considers three formative exogenous constructs and one formative endogenous construct. Linear effects are specified in a binary matrix as well as nonlinear and interaction effects. The interface allows one to set the number of data sets or Monte Carlo replications and the sample size of each data set; it is advisable to consider a sample size equal to or higher than 100 observations. All the measurement models have to have the same number of manifest variables. Four options can be used: 2, 4, 6, and 8 indicators per construct.
For each true model, two sets of true outer weights are available. The value of the outer effects may be equal in all measurement models or a set of different permissible values may be considered. Manifest variables are generated as random normal data.
Figure 2 True structural and measurement models
Value
Two files:
text file
A .txt file in ASCII format readable from R-project with dget.
csv file
A .csv file.
Author(s)
Alba Martinez-Ruiz <amartine@ucsc.cl>, Claudia Martinez-Araneda <cmartinez@ucsc.cl>
References
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.
See Also
dgmb, IntPar, ViewParameters, ToProcess, <tcltk> package
Examples
1
2
dgmb documentation built on May 2, 2019, 7:55 a.m.
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Description Usage Details Value Author(s) References See Also Examples
Description
A function to implement a graphical user interface (GUI) to fix simulation parameters.
Usage
1 | dgmbGui()
|
Details
The following main window is displayed in R:
Figure 1 Graphical user interface for random data generation
Functions are implemented to generate data for a specific model (Figure 2). The true model considers three formative exogenous constructs and one formative endogenous construct. Linear effects are specified in a binary matrix as well as nonlinear and interaction effects. The interface allows one to set the number of data sets or Monte Carlo replications and the sample size of each data set; it is advisable to consider a sample size equal to or higher than 100 observations. All the measurement models have to have the same number of manifest variables. Four options can be used: 2, 4, 6, and 8 indicators per construct.
For each true model, two sets of true outer weights are available. The value of the outer effects may be equal in all measurement models or a set of different permissible values may be considered. Manifest variables are generated as random normal data.
Figure 2 True structural and measurement models
Value
Two files:
text file |
A |
csv file |
A |
Author(s)
Alba Martinez-Ruiz <amartine@ucsc.cl>, Claudia Martinez-Araneda <cmartinez@ucsc.cl>
References
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.
See Also
dgmb, IntPar, ViewParameters, ToProcess, <tcltk> package
Examples
1 2 |
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