Integrates information from several fit indices, and selects the best model.
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A matrix or data.frame of fit indices, with colnames
corresponding to the indices named in
A named numeric vector. Names should correspond to
Many fit indices are available for model selection. Following the procedure developed by Akogul and Erisoglu (2017), this function integrates information from several fit indices, and selects the best model, using Saaty's (1990) Analytic Hierarchy Process (AHP). Conceptually, the process consists of the following steps:
For each fit index, calculate the amount of support provided for each model, relative to the other models.
From these comparisons, obtain a "priority vector" of the amount of support for each model.
Compute a weighted average of the priority vectors for all fit indeces, with weights based on a simulation study examining each fit index' ability to recover the correct number of clusters (Akogul & Erisoglu, 2016).
Select the model with the highest weighted average priority.
Caspar J. van Lissa
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