rdop

<code>rdop</code> returns the relative density-based outlier probabilities according to Barroso and Busing (2025).

Multidimensional scaling (MDS) functions for various tasks that are beyond the beta stage and way past the alpha stage.
Currently, options are available for weights, restrictions, classical scaling or principal coordinate analysis, transformations (linear, power, Box-Cox, spline, ordinal), outlier mitigation (rdop), out-of-sample estimation (predict), negative dissimilarities, fast and faster executions with low memory footprints, penalized restrictions, cross-validation-based penalty selection, supplementary variable estimation (explain), additive constant estimation, mixed measurement level distance calculation, restricted classical scaling, etc. More will come in the future.
References. Busing (2024) "A Simple Population Size Estimator for Local Minima Applied to Multidimensional Scaling". Manuscript submitted for publication.
Busing (2025) "Node Localization by Multidimensional Scaling with Iterative Majorization". Manuscript submitted for publication.
Busing (2025) "Faster Multidimensional Scaling". Manuscript in preparation.
Barroso and Busing (2025) "e-RDOP, Relative Density-Based Outlier Probabilities, Extended to Proximity Mapping". Manuscript submitted for publication.

Frank Busing

fmds

Multidimensional Scaling Development Kit

Frank M.T.A. Busing

Juan Claramunt Gonzalez

rdop function

<dl><dt>data</dt>
<dd>a (rectangular, multivariate, n by m) data matrix or a (n by n ) distance matrix,
in either case, the function continues with a full distance matrix</dd>
<dt>k</dt>
<dd>number of neighbors (default: sqrt( 2n ))</dd>
<dt>lambda</dt>
<dd>multiple of standard distance deviations to get probabilistic distances</dd>
<dt>extended</dt>
<dd>extended relative density-based probabilities</dd>
<dt>alpha</dt>
<dd>steepness parameter turning scores into weights</dd>
<dt>beta</dt>
<dd>halfway parameter turning scores into weights</dd></dl>

Arguments

Author

Relative Density-based Outlier Probabilities Function — rdop

<dl>

<dt>data</dt>
<dd>a (rectangular, multivariate, n by m) data matrix or a (n by n ) distance matrix,
in either case, the function continues with a full distance matrix</dd>


<dt>k</dt>
<dd>number of neighbors (default: sqrt( 2n ))</dd>


<dt>lambda</dt>
<dd>multiple of standard distance deviations to get probabilistic distances</dd>


<dt>extended</dt>
<dd>extended relative density-based probabilities</dd>


<dt>alpha</dt>
<dd>steepness parameter turning scores into weights</dd>


<dt>beta</dt>
<dd>halfway parameter turning scores into weights</dd>

</dl>

State of Data and AI Literacy Report 2025

rdop: Relative Density-based Outlier Probabilities Function

Description

Usage

Value

Arguments

Author

References