smacofxDeleteOne

Function deletes every object row and columns once and fits the MDS in object and returns the configuration. The deleted row is set to 0 in the configuration. Is meant for smacofx functions, but should also work for every smacof models.

Flexible multidimensional scaling (MDS) methods and extensions to the package 'smacof'. This package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different flexible MDS models (some as of yet unpublished). These are: Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459) with powers, Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>) with ratio and interval optimal scaling, Multiscale MDS (Ramsay, 1977, <doi:10.1007/BF02294052>) with ratio and interval optimal scaling, S-stress MDS (ALSCAL; Takane, Young & De Leeuw, 1977, <doi:10.1007/BF02293745>) with ratio and interval optimal scaling, elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>) with ratio and interval optimal scaling, r-stress MDS (De Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>) with ratio, interval and non-metric optimal scaling, power-stress MDS (POST-MDS; Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>) with ratio and interval optimal scaling, restricted power-stress (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>) with ratio and interval optimal scaling, approximate power-stress with ratio optimal scaling (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>), Box-Cox MDS (Chen & Buja, 2013, <https://jmlr.org/papers/v14/chen13a.html>), local MDS (Chen & Buja, 2009, <doi:10.1198/jasa.2009.0111>), curvilinear component analysis (Demartines & Herault, 1997, <doi:10.1109/72.554199>) and curvilinear distance analysis (Lee, Lendasse & Verleysen, 2004, <doi:10.1016/j.neucom.2004.01.007>). There also are experimental models (e.g., sparsified MDS and sparsified POST-MDS). Some functions are suitably flexible to allow any other sensible combination of explicit power transformations for weights, distances and input proximities with implicit ratio, interval or non-metric optimal scaling of the input proximities. Most functions use a Majorization-Minimization algorithm. Currently the methods are only available for one-mode data (symmetric dissimilarity matrices).

Thomas Rusch

smacofx

Flexible Multidimensional Scaling and 'smacof' Extensions

Jan de Leeuw

Lisha Chen

Patrick Mair

smacofxDeleteOne function

<dl><dt>object</dt>
<dd>Object of class smacofP if used as method or another object inheriting from smacofB. Note we assume the MDS model was fitted on a symmetric matrix/data frame or dist object</dd>
<dt>delta</dt>
<dd>the data (symmetric matrix, data frame or dist object)</dd>
<dt>weightmat</dt>
<dd>weighting matrix</dd>
<dt>init</dt>
<dd>starting configuration</dd>
<dt>ndim</dt>
<dd>target dimension of the mds</dd>
<dt>type</dt>
<dd>type of MDS</dd>
<dt>verbose</dt>
<dd>print progress</dd>
<dt>itmaxi</dt>
<dd>maximum iterations of the MDS procedure</dd></dl>

Arguments

Helper function to conduct jackknife MDS — smacofxDeleteOne

<dl>

<dt>object</dt>
<dd>Object of class smacofP if used as method or another object inheriting from smacofB. Note we assume the MDS model was fitted on a symmetric matrix/data frame or dist object</dd>


<dt>delta</dt>
<dd>the data (symmetric matrix, data frame or dist object)</dd>


<dt>weightmat</dt>
<dd>weighting matrix</dd>


<dt>init</dt>
<dd>starting configuration</dd>


<dt>ndim</dt>
<dd>target dimension of the mds</dd>


<dt>type</dt>
<dd>type of MDS</dd>


<dt>verbose</dt>
<dd>print progress</dd>


<dt>itmaxi</dt>
<dd>maximum iterations of the MDS procedure</dd>

</dl>

smacofxDeleteOne: Helper function to conduct jackknife MDS

Description

Usage

Value

Arguments