A Simple and Fast Algorithm for Generating Correlation Matrices with a Known Average Correlation Coefficient.
This article describes a simple and fast algorithm for generating correlation matrices ( R) with a known average correlation. The algorithm should be useful for researchers desiring plausible R matrices for substantive domains in which average correlations are known (at least approximately). The met...
| Publicado en: | American Statistician Vol. 79; no. 1; pp. 23 - 30 |
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| Autor principal: | |
| Formato: | Artículo |
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Taylor & Francis Ltd
Feb2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| Sumario: | This article describes a simple and fast algorithm for generating correlation matrices ( R) with a known average correlation. The algorithm should be useful for researchers desiring plausible R matrices for substantive domains in which average correlations are known (at least approximately). The method is non-iterative and it can solve relatively large problems (e.g., generate a 500 × 500 R matrix) in less than a second on a personal computer. It also has didactic value for introducing students to the convex set of feasible R matrices of a fixed dimension. This Euclidean body is called an elliptope. The proposed method exploits the geometry of elliptopes to efficiently generate realistic R matrices with a desired average correlation coefficient. R code for implementing the algorithm (and for reproducing all of the results of this article) is reported in an online supplement. |
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