Averaging Models: Parameters Estimation with the R-Average Procedure
PIJMEP Volume 31, Number 3, ISSN 0211-2159
The Functional Measurement approach, proposed within the theoretical framework of Information Integration Theory (Anderson, 1981, 1982), can be a useful multi-attribute analysis tool. Compared to the majority of statistical models, the averaging model can account for interaction effects without adding complexity. The R-Average method (Vidotto & Vicentini, 2007) can be used to estimate the parameters of these models. By the use of multiple information criteria in the model selection procedure, R-Average allows for the identification of the best subset of parameters that account for the data. After a review of the general method, we present an implementation of the procedure in the framework of R-project, followed by some experiments using a Monte Carlo method. (Contains 1 figure and 6 tables.)
Vidotto, G., Massidda, D. & Noventa, S. (2010). Averaging Models: Parameters Estimation with the R-Average Procedure. Psicologica: International Journal of Methodology and Experimental Psychology, 31(3), 461-475.