Cluster optimized proximity scaling (COPS) refers to multidimensional scaling methods that aim at pronouncing the clustered appearance of the configuration. They achieve this by transforming proximities/distances with power functions and augment the fitting criterion with a clusteredness index, the OPTICS Cordillera. There are two variants: One for finding the configuration directly for given parameters (COPS-C) for ratio, interval and nonmetric MDS, and one for using the augmented fitting criterion to find optimal parameters (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different MDS models in a COPS framework like ratio, interval and nometric MDS for COPS-C and P-COPS with Torgerson scaling, SMACOF, Sammon mapping, elastic scaling, symmetric SMACOF, spherical SMACOF, sstress, rstress, powermds, power elastic scaling, power sammon mapping, powerstress and approximated power stress. All of these models can also solely be fit as MDS with power transformations. The package further contains a function for pattern search optimization (Adaptive LJ Algorithm).
|Author||Thomas Rusch [aut, cre], Jan de Leeuw [aut], Patrick Mair [aut]|
|Date of publication||2018-08-09 21:32:27|
|Maintainer||Thomas Rusch <[email protected]>|
|License||GPL-2 | GPL-3|
|Package repository||View on R-Forge|
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