sammon

Wrapper to <code>sammon</code> for S3 class

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 power functions and augment 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) for ratio, power, interval and non-metric MDS (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), 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 non-metric MDS for COPS-C and P-COPS with Torgerson scaling (Torgerson, 1958, ISBN:978-0471879459), scaling by majorizing a complex function (SMACOF; de Leeuw, 1977, <https://escholarship.org/uc/item/4ps3b5mj>), Sammon mapping (Sammon, 1969, <doi:10.1109/T-C.1969.222678>), elastic scaling (McGee, 1966, <doi:10.1111/j.2044-8317.1966.tb00367.x>), s-stress (Takane, Young & de Leeuw, 1977, <doi:10.1007/BF02293745>), r-stress (de Leeuw, Groenen & Mair, 2016, <https://rpubs.com/deleeuw/142619>), power stress (Buja & Swayne, 2002 <doi:10.1007/s00357-001-0031-0>), restricted power stress, approximate power stress, power elastic scaling, power Sammon mapping (for all Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). All of these models can also solely be fit as MDS with power transformations. 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>).

Thomas Rusch

cops

Cluster Optimized Proximity Scaling

Jan de Leeuw

Patrick Mair

sammon function

<dl><dt>d</dt>
<dd>a distance structure such as that returned by 'dist' or a full symmetric matrix. Data are assumed to be dissimilarities or relative distances, but must be positive except for self-distance. This can contain missing values.</dd>
<dt>y</dt>
<dd>An initial configuration. If NULL, 'cmdscale' is used to provide the classical solution. (If there are missing values in 'd', an initial configuration must be provided.) This must not have duplicates.</dd>
<dt>k</dt>
<dd>The dimension of the configuration</dd>
<dt>...</dt>
<dd>Additional parameters passed to <code>sammon</code>, see <code>sammon</code></dd></dl>

Arguments

Wrapper to sammon for S3 class — sammon

<dl>

<dt>d</dt>
<dd>a distance structure such as that returned by 'dist' or a full symmetric matrix. Data are assumed to be dissimilarities or relative distances, but must be positive except for self-distance. This can contain missing values.</dd>


<dt>y</dt>
<dd>An initial configuration. If NULL, 'cmdscale' is used to provide the classical solution. (If there are missing values in 'd', an initial configuration must be provided.) This must not have duplicates.</dd>


<dt>k</dt>
<dd>The dimension of the configuration</dd>


<dt>...</dt>
<dd>Additional parameters passed to <code>sammon</code>, see <code>sammon</code></dd>

</dl>

sammon: Wrapper to `sammon` for S3 class

Description

Usage

Value

Arguments

Details

Examples