Package: subselect 0.16.0

subselect: Selecting Variable Subsets

A collection of functions which (i) assess the quality of variable subsets as surrogates for a full data set, in either an exploratory data analysis or in the context of a multivariate linear model, and (ii) search for subsets which are optimal under various criteria. Theoretical support for the heuristic search methods and exploratory data analysis criteria is in Cadima, Cerdeira, Minhoto (2003, <doi:10.1016/j.csda.2003.11.001>). Theoretical support for the leap and bounds algorithm and the criteria for the general multivariate linear model is in Duarte Silva (2001, <doi:10.1006/jmva.2000.1920>). There is a package vignette "subselect", which includes additional references.

Authors:Jorge Orestes Cerdeira [aut], Pedro Duarte Silva [aut, cre], Jorge Cadima [aut], Manuel Minhoto [aut]

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subselect.pdf |subselect.html✨
subselect/json (API)

# Install 'subselect' in R:
install.packages('subselect', repos = c('https://apedrods.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • farm - Sixty-two economic indicators from 99 Portuguese farms.

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

fortranopenblascpp

2.60 score 2 stars 403 downloads 2 mentions 29 exports 3 dependencies

Last updated 16 hours agofrom:bf89093dc1. Checks:12 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKApr 01 2025
R-4.5-win-x86_64OKApr 01 2025
R-4.5-mac-x86_64OKApr 01 2025
R-4.5-mac-aarch64OKApr 01 2025
R-4.5-linux-x86_64OKApr 01 2025
R-4.4-win-x86_64OKApr 01 2025
R-4.4-mac-x86_64OKApr 01 2025
R-4.4-mac-aarch64OKApr 01 2025
R-4.4-linux-x86_64OKApr 01 2025
R-4.3-win-x86_64OKApr 01 2025
R-4.3-mac-x86_64OKApr 01 2025
R-4.3-mac-aarch64OKApr 01 2025

Exports:annealccr12.coefeleapsgcd.coefgeneticglhHmatglhHmat.data.frameglhHmat.defaultglhHmat.formulaglmHmatglmHmat.glmimproveldaHmatldaHmat.data.frameldaHmat.defaultldaHmat.formulaleapslmHmatlmHmat.data.framelmHmat.defaultlmHmat.formulalmHmat.lmrm.coefrv.coeftau2.coeftrim.matrixwald.coefxi2.coefzeta2.coef

Dependencies:corpcorISwRMASS

subselect

Rendered fromsubselect.Rnwusingutils::Sweaveon Apr 01 2025.

Last update: 2020-03-14
Started: 2013-07-05

Readme and manuals

Help Manual

Help pageTopics
Simulated Annealing Search for an optimal k-variable subsetanneal
First Squared Canonical Correlation for a multivariate linear hypothesisccr12.coef
A Leaps and Bounds Algorithm for finding the best variable subsetseleaps leaps
Sixty-two economic indicators from 99 Portuguese farms.farm
Computes Yanai's GCD in the context of the variable-subset selection problemgcd.coef
Genetic Algorithm searching for an optimal k-variable subsetgenetic
Total and Effect Deviation Matrices for General Linear HypothesisglhHmat glhHmat.data.frame glhHmat.default glhHmat.formula
Input matrices for subselect search routines in generalized linear modelsglmHmat glmHmat.glm
Restricted Local Improvement search for an optimal k-variable subsetimprove
Total and Between-Group Deviation Matrices in Linear Discriminant AnalysisldaHmat ldaHmat.data.frame ldaHmat.default ldaHmat.formula
Total and Effect Deviation Matrices for Linear Regression and Canonical Correlation AnalysislmHmat lmHmat.data.frame lmHmat.default lmHmat.formula lmHmat.lm
Computes the RM coefficient for variable subset selectionrm.coef
Computes the RV-coefficient applied to the variable subset selection problemrv.coef
Computes the Tau squared coefficient for a multivariate linear hypothesistau2.coef
Given an ill-conditioned square matrix, deletes rows/columns until a well-conditioned submatrix is obtained.trim.matrix
Wald statistic for variable selection in generalized linear modelswald.coef
Computes the Xi squared coefficient for a multivariate linear hypothesisxi2.coef
Computes the Zeta squared coefficient for a multivariate linear hypothesiszeta2.coef