You are here:

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. Retrieved August 19, 2019 from .

This record was imported from ERIC on April 19, 2013. [Original Record]

ERIC is sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education.

Copyright for this record is held by the content creator. For more details see ERIC's copyright policy.