BiDimRegression : Bidimensional regression modeling using R.




Faculty/Professorship: General Psychology and Methodology  
Author(s): Carbon, Claus-Christian  
Title of the Journal: Journal of statistical software
Corporate Body: UCLA, Dept. of Statistics
Publisher Information: Los Angeles, Calif.
Year of publication: 2013
Volume: 52
Issue: Code Snippet 1
Pages: 1-11
Language(s): English
URL: https://10.18637/jss.v052.c01
http://www.jstatsoft.org/v52/c01
Abstract: 
Tobler (1965) introduced bidimensional regression to the research eld of geography in
1965 to provide a method for estimating mapping relations between two planes on the basis
of regression modeling. The bidimensional regression method has been widely used within
geographical research. However, the applicability in assessing the degree of similarity of
two-dimensional patterns has not much explored in the area of psychological research,
particularly in the domains of cognitive maps, face research and comparison of 2D-data
patterns. Describing Tobler's method in detail, Friedman and Kohler (2003) made an
attempt to bridge the gulf between geographical methodological knowledge and psychological
research practice. Still, the method has not been incorporated into psychologists'
standard methodical repertoire to date. The present paper aims to make bidimensional
regression applicable also for researchers and users unfamiliar with its theoretical basis.
The BiDimRegression function provides a manageable computing option for bidimensional
regression models with a ne and Euclidean transformation, which makes it easy
to assess the similarity of any planar con guration of points. Typical applications are,
for instance, assessments of the similarity of facial images de ned by discrete features
or of (cognitive) maps characterized by landmarks. BiDimRegression can be a valuable
tool since it provides estimation, statistical inference, and goodness-of- t measures for
bidimensional regression.
Keywords: bidimensional regression, R, calculation, probability, similarity, Euclidean, a ne, projective, nonlinear, inference statistics, psychology, geography, cognitive cartography
Type: Article
URI: https://fis.uni-bamberg.de/handle/uniba/2269
Year of publication: 4. November 2013