tidy_mds_smacof

Performs MDS using SMACOF algorithm from the smacof package

Provides a unified tidyverse-compatible interface to R's machine
learning packages. Wraps established implementations from 'glmnet',
'randomForest', 'xgboost', 'e1071', 'rpart', 'gbm', 'nnet', 'cluster',
'dbscan', and others - providing consistent function signatures, tidy tibble
output, and unified 'ggplot2'-based visualization. The underlying algorithms
are unchanged; 'tidylearn' simply makes them easier to use together. Access
raw model objects via the $fit slot for package-specific functionality.
Methods include random forests Breiman (2001) <doi:10.1023/A:1010933404324>,
LASSO regression Tibshirani (1996) <doi:10.1111/j.2517-6161.1996.tb02080.x>,
elastic net Zou and Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>,
support vector machines Cortes and Vapnik (1995) <doi:10.1007/BF00994018>,
and gradient boosting Friedman (2001) <doi:10.1214/aos/1013203451>.

Cesaire Tobias

tidylearn

A Unified Tidy Interface to R's Machine Learning Ecosystem

tidy_mds_smacof function

<dl><dt>dist_mat</dt>
<dd>A distance matrix (dist object)</dd>
<dt>ndim</dt>
<dd>Number of dimensions (default: 2)</dd>
<dt>type</dt>
<dd>Character; "ratio" for metric, "ordinal" for non-metric (default: "ratio")</dd>
<dt>...</dt>
<dd>Additional arguments passed to smacof::mds()</dd></dl>

Arguments

SMACOF MDS (Metric or Non-metric) — tidy_mds_smacof

<dl>

<dt>dist_mat</dt>
<dd>A distance matrix (dist object)</dd>


<dt>ndim</dt>
<dd>Number of dimensions (default: 2)</dd>


<dt>type</dt>
<dd>Character; "ratio" for metric, "ordinal" for non-metric (default: "ratio")</dd>


<dt>...</dt>
<dd>Additional arguments passed to smacof::mds()</dd>

</dl>

tidy_mds_smacof: SMACOF MDS (Metric or Non-metric)

Description

Usage

Value

Arguments