Cluster Optimized Proximity Scaling
Calculating the pairwise phi distance matrix between n vectors
MDS Bootstrap for pcops objects
PCOPS version of approximated power stress model.
S3 plot method for cops objects
PCOPS version of strain
PCOPS versions of elastic scaling models (via smacofSym)
PCOPS version of elastic scaling with powers
PCOPS version of powermds
PCOPS version of sammon with powers
COPS version of powerstress
PCOPS version of restricted powerstress.
PCOPS version of rstress
PCOPS version of Sammon mapping from MASS
Another COPS versions of Sammon mapping models (via smacofSym)
PCOPS versions of smacofSphere models
PCOPS versions of smacofSym models
PCOPS version of sstress
cops: cluster optimized proximity scaling
High Level COPS Function
Calculates copstress for given MDS object
Fitting a COPS-C Model (COPS Variant 1).
Double centering of a matrix
Explicit Normalization Normalizes distances
MDS Jackknife for pcops objects
(Adaptive) Version of Luus-Jakola Optimization
Auxfunction1
Take matrix to a power
Profile COPS Function (aka COPS Variant 2)
Squared p-distances
S3 plot method for p-cops objects
procruster: a procrustes function
Adjusts a configuration
Secular Equation
Calculating stress per point
Squared distances
Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm'' (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.