Fechnerian scaling is a procedure for constructing a metric on a set of objects (e.g., symbols, X-ray films). The constructed Fechnerian metric represents subjective dissimilarities among the objects as perceived by a system (e.g., person, technical device). The package fechner provides functions and example datasets for performing and illustrating Fechnerian scaling of discrete object sets in R.
|License:||GPL (>= 2)|
Fechnerian scaling of discrete object (or stimulus) sets provides a theoretical framework for deriving, so-called Fechnerian, distances among objects representing subjective dissimilarities. A Fechnerian metric on a set of stimuli is constructed from the probabilities with which the objects are discriminated from each other by a perceiving system. In addition to the oriented and overall Fechnerian distances, the package fechner also computes such related information as the points of subjective equality, the psychometric increments, the geodesic chains and loops with their corresponding lengths, and the generalized Shepardian dissimilarities (or S-index). Moreover, the package fechner provides functions for checking the required data format and the fundamental regular minimality/maximality condition. These concepts are explained in detail in the paper about the fechner package by Uenlue, Kiefer, and Dzhafarov (2009), and in the theoretical papers by Dzhafarov and Colonius (2006, 2007) (see ‘References’).
The package fechner is implemented based on the S3 system. It
comes with a namespace, and consists of three external functions
(functions the package exports):
check.regular, and the main function of this package,
fechner. It also contains six internal functions
(functions not exported by the package), which are
summary methods for objects of the class
summary.fechner, and two functions for computing intermediate
print.summary.fechner, and fechner-internal.
The features of the package fechner are illustrated with
accompanying two real datasets,
wish, and two artificial datasets,
Thomas Kiefer, Ali Uenlue. Based on original MATLAB source by Ehtibar N. Dzhafarov.
Maintainer: Ali Uenlue <[email protected]>
Dzhafarov, E. N. and Colonius, H. (2006) Reconstructing distances among objects from their discriminability. Psychometrika, 71, 365–386.
Dzhafarov, E. N. and Colonius, H. (2007) Dissimilarity cumulation theory and subjective metrics. Journal of Mathematical Psychology, 51, 290–304.
Uenlue, A. and Kiefer, T. and Dzhafarov, E. N. (2009) Fechnerian scaling in R: The package fechner. Journal of Statistical Software, 31(6), 1–24. URL http://www.jstatsoft.org/v31/i06/.
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