Package: stops 1.6-2

Thomas Rusch

stops:Structure Optimized Proximity Scaling

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.

Authors:Thomas Rusch [aut, cre], Patrick Mair [aut], Kurt Hornik [ctb]

stops_1.6-2.tar.gz
stops_1.6-2.tar.gz(r-4.5-noble)stops_1.6-2.tar.gz(r-4.4-noble)
stops_1.6-2.tgz(r-4.4-emscripten)stops_1.6-2.tgz(r-4.3-emscripten)
stops.pdf |stops.html✨
stops/json (API)
NEWS

# Installstops in R:
install.packages('stops',repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://r-forge.r-project.org/projects/stops

Uses libs:
  • openjdk– OpenJDK Java runtime, using Hotspot JIT
Datasets:

48 exports 0.00 score 187 dependencies 235 downloads

Last updated 7 days agofrom:76993b30da

Exports:c_associationc_clumpinessc_clusterednessc_complexityc_convexityc_dependencec_faithfulnessc_functionalityc_hierarchyc_inequalityc_linearityc_manifoldnessc_nonmonotonicityc_outlyingc_regularityc_shepardnessc_skinninessc_sparsityc_striatednessc_stringinessljoptimstop_bcmdsstop_clcastop_cldaestop_cldakstop_elasticstop_isomap1stop_isomap2stop_lmdsstop_powerelasticstop_powermdsstop_powersammonstop_powerstressstop_rstressstop_sammonstop_sammon2stop_smacofSpherestop_smacofSymstop_smddaestop_smddakstop_smdsstop_spmddaestop_spmddakstop_spmdsstop_sstressstoplossstopstgpoptim

Dependencies:abindacepackaskpassbackportsbase64encbitbit64bootbroombslibcachemcandisccarcarDatacheckmateclassclicliprclueclustercmaescodacodetoolscolorspacecommonmarkcordilleracowplotcpp11crayoncrosstalkcurldata.tabledbscandeldirDerivdeSolvedfoptimDiceDesignDiceKrigingDiceOptimdigestdoBydoParalleldplyre1071ellipseenergyevaluatefansifarverfastmapfontawesomeforcatsforeachforeignFormulafsgdataGeneralizedUmatrixgenericsgeometryggplot2glmnetgluegridExtragslgtablegtoolshavenheplotshighrHmischmshtmlTablehtmltoolshtmlwidgetshttpuvhttrisobanditeratorsjomojquerylibjsonliteknitrlabelinglaterlatticelazyevallifecyclelinproglme4lpSolvemagicmagrittrmaptreeMASSMatrixMatrixModelsmemoisemgcvmicemicrobenchmarkmimeminervaminqamitmlmnormtmodelrmunsellmvtnormnlmenloptrnnetnnlsnumDerivopensslordinalpanpbivnormpbkrtestpermutepillarpkgconfigplotlyplotrixpolynompompprettyunitsprogressProjectionBasedClusteringpromisesproxypsopurrrquantregR6randtoolboxrappdirsRColorBrewerRcppRcppArmadilloRcppEigenRcppParallelRcppProgressreadrrgenoudrglrJavarlangrmarkdownrngWELLrpartrstudioapisassscagnosticsscalesshapeshinyshinyjsshinythemessmacofsmacofxsourcetoolsSparseMstringistringrsurvivalsystgptibbletidyrtidyselecttinytextzdbucminfutf8vctrsveganviridisviridisLitevroomweightswithrwordcloudxfunxtableyaml

A tutorial on STOPS

Rendered fromstops.html.asisusingR.rsp::asison Jun 29 2024.

Last update: 2023-01-20
Started: 2023-01-20

Readme and manuals

Help Manual

Help pageTopics
Banking Crises DistancesBankingCrisesDistances
MDS Bootstrap for stops objectsbootmds.stops
c-association calculates the c-association based on the maximal information coefficient We define c-association as the aggregated association between any two columns in confsc_association
c-clumpinessc_clumpiness
c-clusteredness calculates c-clusteredness as the OPTICS cordillera. The higher the more clustered.c_clusteredness
c-complexity Calculates the c-complexity based on the minimum cell number We define c-complexity as the aggregated minimum cell number between any two columns in confs This is one of few c-structuredness indices not between 0 and 1, but can be between 0 and (theoretically) infinityc_complexity
c-convexityc_convexity
c-dependence calculates c-dependence as the aggregated distance correlation of each pair if nonidentical columnsc_dependence
c-faithfulness calculates the c-faithfulness based on the index by Chen and Buja 2013 (M_adj) with equal input neigbourhoodsc_faithfulness
c-functionality calculates the c-functionality based on the maximum edge value We define c-functionality as the aggregated functionality between any two columns of confsc_functionality
c-hierarchy captures how well a partition/ultrametric (obtained by hclust) explains the configuration distances. Uses variance explained for euclidean distances and deviance explained for everything else.c_hierarchy
c-inequality Calculates c-inequality (as in an economic measure of inequality) as Pearsons coefficient of variation of the fitted distance matrix. This can help with avoiding degenerate solutions. This is one of few c-structuredness indices not between 0 and 1, but 0 and infinity.c_inequality
c-linearity calculates c-linearity as the aggregated multiple correlation of all columns of the configuration.c_linearity
c-manifoldness calculates c-manifoldness as the aggregated maximal correlation coefficient (i.e., Pearson correlation of the ACE transformed variables) of all pairwise combinations of two different columns in confs. If there is an NA (happens usually when the optimal transformation of any variable is a constant and therefore the covariance is 0 but also one of the sds in the denominator), it gets skipped.c_manifoldness
wrapper for getting the mine coefficientsc_mine
c-nonmonotonicity calculates the c-nonmonotonicity based on the maximum asymmetric score We define c-nonmonotonicity as the aggregated nonmonotonicity between any two columns in confs this is one of few c-structuredness indices not between 0 and 1c_nonmonotonicity
c-outlyingc_outlying
c-regularity calculates c-regularity as 1 - OPTICS cordillera for k=2. The higher the more regular.c_regularity
c-shepardness calculates the c-shepardness as the correlation between a loess smoother of the transformed distances and the transformed dissimilaritiesc_shepardness
c-skinninessc_skinniness
c-sparsityc_sparsity
c-striatednessc_striatedness
c-stringinessc_stringiness
S3 coef method for stops objectscoef.stops
MDS Jackknife for stops objectsjackmds.stops
calculate k nearest neighbours from a distance matrixknn_dist
(Adaptive) Version of Luus-Jaakola Optimizationljoptim
Pen digitsPendigits500
S3 plot method for stops objectsplot.stops
S3 print method for stops objectsprint.stops
S3 print method for summary.stopsprint.summary.stops
S3 residuals method for stopsresiduals.stops
STOPS version of approximated power stress models.stop_apstress
STOPS version of Box Cox Stressstop_bcmds
STOPS version of CLCA.stop_clca
STOPS version of CLDA with free epsilon.stop_cldae
STOPS version of CLDA with free k.stop_cldak
STOPS version of strainstop_cmdscale
STOPS versions of elastic scaling models (via smacofSym)stop_elastic
STOPS version of isomap to optimize over integer k.stop_isomap1
STOPS version of isomap over real epsilon.stop_isomap2
STOPS version of lMDSstop_lmds
STOPS version of elastic scaling with powers for proximities and distancesstop_powerelastic
STOPS version of powermdsstop_powermds
STOPS version of sammon with powersstop_powersammon
STOPS version of powerstressstop_powerstress
STOPS version of restricted powerstressstop_rpowerstress
STOPS version of rstressstop_rstress
STOPS version of Sammon mappingstop_sammon
Another STOPS version of Sammon mapping models (via smacofSym)stop_sammon2
STOPS versions of smacofSphere modelsstop_smacofSphere
STOPS version of smacofSym modelsstop_smacofSym
STOPS version of sparsified multidimensional distance analysis for fixed eps and taustop_smddae
STOPS version of sparsified multidimensional distance analysis for fixed k and taustop_smddak
STOPS version of sparsified MDS.stop_smds
STOPS version of sparsified post multidimensional distance analysis for fixed tau and epsilon.stop_spmddae
STOPS version of sparsified post multidimensional distance analysis for fixed tau and k.stop_spmddak
STOPS version of sparsified POST-MDS for fixed taustop_spmds
STOPS version of sstressstop_sstress
Calculate the weighted multiobjective loss function used in STOPSstoploss
High Level STOPS Functionstops
S3 summary method for stopssummary.stops
Swiss rollSwissroll
Bayesian Optimization by a (treed) Bayesian Gaussian Process Prior (with jumps to linear models) surrogate model Essentially a wrapper for the functionality in tgp that has the same slots as optim with defaults for STOPS models.tgpoptim