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.

Thomas Rusch

cops

Cluster Optimized Proximity Scaling

Patrick Mair

Kurt Hornik

spp function

<dl><dt>dhat</dt>
<dd>a dist object or symmetric matrix of dissimilarities</dd>
<dt>confdist</dt>
<dd>a dist object or symmetric matrix of fitted distances</dd>
<dt>weightmat</dt>
<dd>dist objetc or symmetric matrix of weights</dd></dl>

Arguments

Calculating stress per point — spp

<dl>

<dt>dhat</dt>
<dd>a dist object or symmetric matrix of dissimilarities</dd>


<dt>confdist</dt>
<dd>a dist object or symmetric matrix of fitted distances</dd>


<dt>weightmat</dt>
<dd>dist objetc or symmetric matrix of weights</dd>

</dl>

spp: Calculating stress per point

Description

Usage

Value

Arguments