c_mine

wrapper for getting the mine coefficients

Methods that use flexible variants of multidimensional scaling (MDS) which incorporate parametric nonlinear distance transformations and trade-off the goodness-of-fit fit with structure considerations to find optimal hyperparameters, also known as structure optimized proximity scaling (STOPS) (Rusch, Mair & Hornik, 2023,<doi:10.1007/s11222-022-10197-w>). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying different 1-way MDS models with ratio, interval, ordinal optimal scaling in a STOPS framework. These cover essentially the functionality of the package smacofx, including Torgerson (classical) scaling with power transformations of dissimilarities, SMACOF MDS with powers of dissimilarities, Sammon mapping with powers of dissimilarities, elastic scaling with powers of dissimilarities, spherical SMACOF with powers of dissimilarities, (ALSCAL) s-stress MDS with powers of dissimilarities, r-stress MDS, MDS with powers of dissimilarities and configuration distances, elastic scaling powers of dissimilarities and configuration distances, Sammon mapping powers of dissimilarities and configuration distances, power stress MDS (POST-MDS), approximate power stress, Box-Cox MDS, local MDS, Isomap, curvilinear component analysis (CLCA), curvilinear distance analysis (CLDA) and sparsified (power) multidimensional scaling and (power) multidimensional distance analysis (experimental models from smacofx influenced by CLCA). All of these models can also be fit by optimizing over hyperparameters based on goodness-of-fit fit only (i.e., no structure considerations). The package further contains functions for optimization, specifically the adaptive Luus-Jaakola algorithm and a wrapper for Bayesian optimization with treed Gaussian process with jumps to linear models, and functions for various c-structuredness indices. Hyperparameter optimization can be done with a number of techniques but we recommend either Bayesian optimization or particle swarm. For using "Kriging", users need to install a version of the archived 'DiceOptim' R package.

Thomas Rusch

stops

Structure Optimized Proximity Scaling

Patrick Mair

Kurt Hornik

c_mine function

<dl><dt>confs</dt>
<dd>a numeric matrix or data frame with two columns</dd>
<dt>master</dt>
<dd>the master column</dd>
<dt>alpha</dt>
<dd>an optional number of cells allowed in the X-by-Y search-grid. Default value is 0.6</dd>
<dt>C</dt>
<dd>an optional number determining the starting point of the X-by-Y search-grid. When trying to partition the x-axis into X columns, the algorithm will start with at most C X clumps. Default value is 15.</dd>
<dt>var.thr</dt>
<dd>minimum value allowed for the variance of the input variables, since mine can not be computed in case of variance close to 0. Default value is 1e-5.</dd>
<dt>zeta</dt>
<dd>integer in [0,1] (?). If NULL (default) it is set to 1-MIC. It can be set to zero for noiseless functions, but the default choice is the most appropriate parametrization for general cases (as stated in Reshef et al. SOM; they call it epsilon in the paper). It provides robustness.</dd></dl>

Arguments

wrapper for getting the mine coefficients — c_mine

<dl>

<dt>confs</dt>
<dd>a numeric matrix or data frame with two columns</dd>


<dt>master</dt>
<dd>the master column</dd>


<dt>alpha</dt>
<dd>an optional number of cells allowed in the X-by-Y search-grid. Default value is 0.6</dd>


<dt>C</dt>
<dd>an optional number determining the starting point of the X-by-Y search-grid. When trying to partition the x-axis into X columns, the algorithm will start with at most C X clumps. Default value is 15.</dd>


<dt>var.thr</dt>
<dd>minimum value allowed for the variance of the input variables, since mine can not be computed in case of variance close to 0. Default value is 1e-5.</dd>


<dt>zeta</dt>
<dd>integer in [0,1] (?). If NULL (default) it is set to 1-MIC. It can be set to zero for noiseless functions, but the default choice is the most appropriate parametrization for general cases (as stated in Reshef et al. SOM; they call it epsilon in the paper). It provides robustness.</dd>

</dl>

c_mine: wrapper for getting the mine coefficients

Description

Usage

Arguments