Package: sgPLS 1.8

Benoit Liquet

sgPLS: Sparse Group Partial Least Square Methods

Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.

Authors:Benoit Liquet and Pierre Lafaye de Micheaux and Camilo Broc

sgPLS_1.8.tar.gz
sgPLS_1.8.tar.gz(r-4.5-noble)sgPLS_1.8.tar.gz(r-4.4-noble)
sgPLS_1.8.tgz(r-4.4-emscripten)sgPLS_1.8.tgz(r-4.3-emscripten)
sgPLS.pdf |sgPLS.html✨
sgPLS/json (API)

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

Peer review:

Datasets:
  • simuData - Simulated Data for group PLS-DA model

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

1.00 score 1 stars 7 scripts 197 downloads 29 exports 83 dependencies

Last updated 1 years agofrom:a45f57c83b. Checks:OK: 1 NOTE: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 29 2024
R-4.5-linuxNOTEOct 29 2024

Exports:gPLSgPLSdalambda.quadranormvper.varianceperfperf.gPLSperf.sgPLSperf.sPLSplotcimpredict.gPLSpredict.sgPLSpredict.sPLSselect.sgplsselect.splssgPLSsgPLSdasoft.thresholdingsoft.thresholding.groupsoft.thresholding.sparse.groupsPLSsPLSdastep1.group.spls.sparsitystep1.sparse.group.spls.sparsitystep1.spls.sparsitystep2.splstuning.gPLS.Xtuning.sgPLS.Xtuning.sPLS.X

Dependencies:base64encBHBiocParallelbslibcachemclicodetoolscolorspacecorpcorcpp11digestdplyrellipseevaluatefansifarverfastmapfontawesomeformatRfsfutile.loggerfutile.optionsgenericsggplot2ggrepelgluegridExtragsignalgtablehighrhtmltoolshtmlwidgetsigraphisobandjquerylibjsonliteknitrlabelinglambda.rlatticelifecyclemagrittrMASSMatrixmatrixStatsmemoisemgcvmimemixOmicsmunsellmvtnormnlmepillarpkgconfigplyrpracmapurrrR6rappdirsrARPACKRColorBrewerRcppRcppEigenreshape2rglrlangrmarkdownRSpectrasassscalessnowstringistringrtibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Group and Sparse Group Partial Least Square ModelsgPLS-package
Group Partial Least Squares (gPLS)gPLS
Group Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)gPLSda
Percentage of variance of the Y matrix explained by the score-vectors obtained by PLS approachesper.variance
Compute evaluation criteria for PLS, sPLS, PLS-DA and sPLS-DAperf perf.gPLS perf.gPLSda perf.sgPLS perf.sgPLSda perf.sPLS perf.sPLSda
Plots a cluster image mapping of correlations between outcomes and all predictorsplotcim
Predict Method for sPLS, gPLS, sgPLS, sPLDda, gPLSda, sgPLSdapredict.gPLS predict.gPLSda predict.sgPLS predict.sgPLSda predict.sPLS predict.sPLSda
Output of selected variables from a gPLS model or a sgPLS modelselect.sgpls
Output of selected variables from a sPLS modelselect.spls
Sparse Group Partial Least Squares (sgPLS)sgPLS
Internal Functionslambda.quadra normv soft.thresholding soft.thresholding.group soft.thresholding.sparse.group step1.group.spls.sparsity step1.sparse.group.spls.sparsity step1.spls.sparsity step2.spls
Sparse Group Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)sgPLSda
Simulated Data for group PLS-DA modelsimuData
Sparse Partial Least Squares (sPLS)sPLS
Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)sPLSda
Choice of the tuning parameter (number of groups) related to predictor matrix for gPLS model (regression mode)tuning.gPLS.X
Choice of the tuning parameters (number of groups and mixing parameter) related to predictor matrix for sgPLS model (regression mode)tuning.sgPLS.X
Choice of the tuning parameter (number of variables) related to predictor matrix for sPLS model (regression mode)tuning.sPLS.X