rAverage {rAverage}

Parameter estimation for the averaging model of Information Integration Theory

Description

The R-Average package implements a method to identify the parameters of the Averagingcmodel of Information Integration Theory (Anderson, 1981), following the spirit of the so-called "principle of parsimony".

Name of the parameters:

s0,w0: initial state values of the Averaging Model.
s(k,j): scale value of the j-th level of k-th factor.
w(k,j): weight value of the j-th level of k-th factor.

Details

Package:	rAverage
Type:	Package
Version:	0.5-8
Date:	2017-07-29
License:	GNU (version 2 or later)

Functions of the R-Average package:

rav: estimates the parameters for averaging models.
fitted: extracts the predicted values of the best model from a rav object.
residuals: extracts the residuals from a rav object.
coefficients: extracts the parameters from a rav object.
outlier.replace: given an estimated averaging model with the rav function, it detects and replace outliers from the residual matrix. rav.indices: given a set of parameters s and w and a matrix of observed data, it calculates the fit indices for the averaging model.
datgen: returns the responses R for averaging models given the set of parameters s and w.
pargen: generates pseudorandom parameters for the averaging model.
rav.grid: generates an empty matrix in 'rav' format.
rav.single: single subjects analysis over an aggregated data matrix.
rav2file: store the reesults of rav into a text file.

Author(s)

Supervisor: Prof. Giulio Vidotto [e-mail]

University of Padova, Department of General Psychology
QPLab: Quantitative Psychology Laboratory

version 0.0:
Marco Vicentini [e-mail]

version 0.1 and following:
Stefano Noventa [e-mail]
Davide Massidda [e-mail]

References

Akaike, H. (1976). Canonical correlation analysis of time series and the use of an information criterion. In: R. K. Mehra & D. G. Lainotis (Eds.), System identification: Advances and case studies (pp. 52-107). New York: Academic Press. doi: 10.1016/S0076-5392(08)60869-3

Anderson, N. H. (1981). Foundations of Information Integration Theory. New York: Academic Press. doi: 10.2307/1422202

Anderson, N. H. (1982). Methods of Information Integration Theory. New York: Academic Press.

Anderson, N. H. (1991). Contributions to information integration theory: volume 1: cognition. Lawrence Erlbaum Associates, Hillsdale, New Jersey. doi: 10.2307/1422884

Anderson, N. H. (2007). Comment on article of Vidotto and Vicentini. Teorie & Modelli, Vol. 12 (1-2), 223-224.

Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. Journal Scientific Computing, 16, 1190-1208. doi: 10.1137/0916069

Kuha, J. (2004). AIC and BIC: Comparisons of Assumptions and Performance. Sociological Methods & Research, 33 (2), 188-229.

Nelder, J. A., & Mead, R. (1965). A Simplex Method for Function Minimization. The Computer Journal, 7, 308-313. doi: 10.1093/comjnl/7.4.308

Vidotto, G., Massidda, D., & Noventa, S. (2010). Averaging models: parameters estimation with the R-Average procedure. Psicologica, 31, 461-475. URL https://www.uv.es/psicologica/articulos3FM.10/3Vidotto.pdf

Vidotto, G. & Vicentini, M. (2007). A general method for parameter estimation of averaging models. Teorie & Modelli, Vol. 12 (1-2), 211-221.

See Also

rav, datgen, pargen, rav.indices, fmdata1, pasta, optim