fechnerian scaling in r: the package fechner

Clicks: 185
ID: 219705
2009
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #46 of 98 articles by views in open geospatial data, software and standards

Most read Least read

Bar heights use a square-root scale.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
Fechnerian scaling is a procedure for constructing a metric on a set of objects (e.g., colors, symbols, X-ray films, or even statistical models) to represent dissimilarities among the objects "from the point of view" of a system (e.g., person, technical device, or even computational algorithm) "perceiving" these objects. This metric, called Fechnerian, is computed from a data matrix of pairwise discrimination probabilities or any other pairwise measure which can be interpreted as the degree with which two objects within the set are discriminated from each other. This paper presents the package fechner for performing Fechnerian scaling of object sets in R. We describe the functions of the package. Fechnerian scaling then is demonstrated on real and artificial data sets accompanying the package.
Reference Key
dzhafarov2009journalfechnerian Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Ehtibar N. Dzhafarov;Thomas Kiefer;Ali Ünlü
Journal open geospatial data, software and standards
Year 2009
DOI
DOI not found
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.