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ERIC Number: EJ856633
Record Type: Journal
Publication Date: 2009
Pages: 2
Abstractor: ERIC
Reference Count: 4
ISBN: N/A
ISSN: ISSN-0146-6216
BMDS: A Collection of R Functions for Bayesian Multidimensional Scaling
Okada, Kensuke; Shigemasu, Kazuo
Applied Psychological Measurement, v33 n7 p570-571 2009
Bayesian multidimensional scaling (MDS) has attracted a great deal of attention because: (1) it provides a better fit than do classical MDS and ALSCAL; (2) it provides estimation errors of the distances; and (3) the Bayesian dimension selection criterion, MDSIC, provides a direct indication of optimal dimensionality. However, Bayesian MDS is not yet widely used as a psychological measurement tool. This can be attributed to the apparent lack of software, because there is none except for Oh and Raftery's (2001) original code, which requires substantial experience in Fortran programming and the IMSL library, which is a commercial library for numerical calculations. It may therefore be difficult for many researchers to acquire such an environment. Considering this situation, a set of R functions is proposed, BMDS, to perform Bayesian MDS and to evaluate the results. R is a free language and environment for statistical computing and graphics. Using BMDS, researchers can: (1) perform Bayesian estimation in MDS; (2) check the convergence of Markov chain Monte Carlo (MCMC) estimation; (3) evaluate the optimal number of dimensions; (4) evaluate the estimation errors; and (5) plot the resultant configurations. Moreover, using BMDS, users can evaluate the results of Bayesian and classical MDS comparatively in terms of the value of stress and a plot of observed and estimated distances. In short, BMDS provides a free and comprehensive solution for Bayesian MDS users. BMDS runs on a Windows-based computer with R and WinBUGS installed, both of which are available at no cost.
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A