Package: plsRglm 1.7.1

plsRglm: Partial Least Squares Regression for Generalized Linear Models

Provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

Authors:Frederic Bertrand [cre, aut], Myriam Maumy-Bertrand [aut]

plsRglm_1.7.1.tar.gz
plsRglm_1.7.1.zip(r-4.7)plsRglm_1.7.1.zip(r-4.6)plsRglm_1.7.1.zip(r-4.5)
plsRglm_1.7.1.tgz(r-4.6-x86_64)plsRglm_1.7.1.tgz(r-4.6-arm64)plsRglm_1.7.1.tgz(r-4.5-x86_64)plsRglm_1.7.1.tgz(r-4.5-arm64)
plsRglm_1.7.1.tar.gz(r-4.7-arm64)plsRglm_1.7.1.tar.gz(r-4.7-x86_64)plsRglm_1.7.1.tar.gz(r-4.6-arm64)plsRglm_1.7.1.tar.gz(r-4.6-x86_64)
plsRglm_1.7.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
plsRglm/json (API)

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

Bug tracker:https://github.com/fbertran/plsrglm/issues

Pkgdown/docs site:https://fbertran.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • aze - Microsatellites Dataset
  • aze_compl - As aze without missing values
  • bordeaux - Quality of wine dataset
  • bordeauxNA - Quality of wine dataset
  • CorMat - Correlation matrix for simulating plsR datasets
  • Cornell - Cornell dataset
  • fowlkes - Fowlkes dataset
  • pine - Pine dataset
  • pine_full - Complete Pine dataset
  • pine_sup - Complete Pine dataset
  • pineNAX21 - Incomplete dataset from the pine caterpillars example
  • XbordeauxNA - Incomplete dataset for the quality of wine dataset
  • XpineNAX21 - Incomplete dataset from the pine caterpillars example

On CRAN:

Conda:

openblascpp

9.10 score 19 stars 7 packages 145 scripts 2.2k downloads 2 mentions 94 exports 84 dependencies

Last updated from:b12cb78f5d. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK281
linux-devel-x86_64OK306
source / vignettesOK355
linux-release-arm64OK277
linux-release-x86_64OK309
macos-release-arm64OK133
macos-release-x86_64OK369
macos-oldrel-arm64OK132
macos-oldrel-x86_64OK258
windows-develOK236
windows-releaseOK223
windows-oldrelOK195
wasm-releaseOK237

Exports:aic.dofAICplsbic.dofbootplsbootplsglmboxplots.bootplsclassbiplotcoef.plsRglmmodelcoef.plsRmodelcoefs.plsRcoefs.plsR.rawcoefs.plsRglmcoefs.plsRglm.rawcoefs.plsRglmnpcoefs.plsRnpconfints.bootplsconfints2signifindcv.plsRcv.plsRglmcv.plsRglmmodel.defaultcv.plsRglmmodel.formulacv.plsRmodel.defaultcv.plsRmodel.formulacv.plsRmulticv.plsRmultiModelcv.plsRmultiModel.defaultcv.plsRmultiModel.formulacvtablecvtable.plsRcvtable.plsRglmdichogmdl.dofinfcrit.dofkfolds2Chisqkfolds2Chisqindkfolds2coeffkfolds2CVinfos_glmkfolds2CVinfos_lmkfolds2Mclassedkfolds2Mclassedindkfolds2Presskfolds2Pressindloglikplspermcoefs.plsRpermcoefs.plsR.rawpermcoefs.plsRglmpermcoefs.plsRglm.rawpermcoefs.plsRglmnppermcoefs.plsRnpplot.table.summary.cv.plsRglmmodelplot.table.summary.cv.plsRmodelplots.confints.bootplsPLS_glmPLS_glm_formulaPLS_glm_kfoldcvPLS_glm_kfoldcv_formulaPLS_glm_wvcPLS_lmPLS_lm_formulaPLS_lm_kfoldcvPLS_lm_kfoldcv_formulaPLS_lm_wvcplsRplsR.dofplsRglmplsRglmmodel.defaultplsRglmmodel.formulaplsRmodel.defaultplsRmodel.formulaplsRmultiplsRmultiModelplsRmultiModel.defaultplsRmultiModel.formulapredict.plsRglmmodelpredict.plsRmodelpredict.plsRmultiModelprint.cv.plsRglmmodelprint.cv.plsRmodelprint.plsRglmmodelprint.plsRmodelprint.summary.plsRglmmodelprint.summary.plsRmodelsignpredsimul_data_completesimul_data_UniYXsimul_data_UniYX_binomsimul_data_YXsummary.cv.plsRglmmodelsummary.cv.plsRmodelsummary.cv.plsRmultiModelsummary.plsRglmmodelsummary.plsRmodeltilt.bootplstilt.bootplsglm

Dependencies:abindbackportsbipartitebootbroomcarcarDatacliclustercodacolorspacecorpcorcowplotcpp11DerivdoBydotCall64dplyrfarverfieldsforecastFormulafracdiffgenericsggplot2gluegtableigraphisobandlabelinglatticelifecyclelme4lmtestmagrittrmapsMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmvtnormnetworknlmenloptrnnetnumDerivpbkrtestpermutepillarpkgconfigpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasrlangS7scalessnaspamSparseMstatnet.commonstringistringrsurvivaltibbletidyrtidyselecttimeDateurcautf8vctrsveganviridisLitewithrzoo

Getting Started with plsRglm
Core Fitting Workflows | Linear PLS with matrix and formula interfaces | Generalized PLS models | Cross-Validation and Model Choice | Prediction and Missing Data | Bootstrap Utilities | Further Reading
Rendered fromgetting-started.Rmdusingknitr::rmarkdown

Last update: 2026-03-28
Started: 2026-03-26

plsRglm: Historical Applications and Algorithmic Notes
Motivation | Theory | PLS Regression | PLS Generalized Linear Regression | Stopping Criteria | Bootstrap | Applications | Data | PLS Regression: Cornell | Bootstrap (y, X) | Bootstrap (y, T) | PLS Binary Logistic Regression: Microsatellites | Method and Results: Original Dataset | Specifying Families, Links, or Custom GLRs | Method and Results: Imputed Dataset | PLS Regression Bis: Pine Caterpillar | Bootstrap (y, X) and (y, T) | PLS Ordinal Logistic Regression: Bordeaux Wine Quality | PLS Ordinal Logistic Regression: Hyptis | PLS Poisson Regression: Rock | Creating Simulated Datasets | Simulating PLSR Datasets | Simulating Logistic Binary PLSGLR Datasets | Continuous Covariables | Dichotomous Only Covariables | Discussion | Core Object Classes | Core S3 Methods | Validation Examples | Main Features of the Package | Export Results to LaTeX | Session Information | References
Rendered fromplsRglm.Rmdusingknitr::rmarkdown

Last update: 2026-03-26
Started: 2026-03-26

Readme and manuals

Help Manual

Help pageTopics
Akaike and Bayesian Information Criteria and Generalized minimum description lengthaic.dof bic.dof gmdl.dof
AIC function for plsR modelsAICpls
Microsatellites Datasetaze
As aze without missing valuesaze_compl
Non-parametric Bootstrap for PLS modelsbootpls
Non-parametric Bootstrap for PLS generalized linear modelsbootplsglm
Quality of wine datasetbordeaux
Quality of wine datasetbordeauxNA
Boxplot bootstrap distributionsboxplots.bootpls
Grouped biplot for PLS scores and loadingsclassbiplot
coef method for plsR modelscoef.plsRglmmodel
coef method for plsR modelscoef.plsRmodel
Coefficients for bootstrap computations of PLSR modelscoefs.plsR
Raw coefficients for bootstrap computations of PLSR modelscoefs.plsR.raw
Coefficients for bootstrap computations of PLSGLR modelscoefs.plsRglm
Raw coefficients for bootstrap computations of PLSGLR modelscoefs.plsRglm.raw
Coefficients for bootstrap computations of PLSGLR modelscoefs.plsRglmnp
Coefficients for bootstrap computations of PLSR modelscoefs.plsRnp
Bootstrap confidence intervalsconfints.bootpls
Detect significant predictors from bootstrap confidence intervalsconfints2signifind
Correlation matrix for simulating plsR datasetsCorMat
Cornell datasetCornell
Partial least squares regression models with k-fold cross-validationcv.plsR cv.plsRmodel.default cv.plsRmodel.formula PLS_lm_kfoldcv PLS_lm_kfoldcv_formula
Partial least squares regression glm models with k-fold cross validationcv.plsRglm cv.plsRglmmodel.default cv.plsRglmmodel.formula PLS_glm_kfoldcv PLS_glm_kfoldcv_formula
Experimental k-fold cross-validation for multivariate-response PLS2cv.plsRmulti cv.plsRmultiModel cv.plsRmultiModel.default cv.plsRmultiModel.formula
Table method for summary of cross validated PLSR and PLSGLR modelscvtable cvtable.plsR cvtable.plsRglm
Dichotomizationdicho
Fowlkes datasetfowlkes
Information criteriainfcrit.dof
Computes Predicted Chisquare for k-fold cross-validated partial least squares regression models.kfolds2Chisq
Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.kfolds2Chisqind
Extracts coefficients from k-fold cross validated partial least squares regression modelskfolds2coeff
Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm modelskfolds2CVinfos_glm
Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares modelskfolds2CVinfos_lm
Number of missclassified individuals for k-fold cross validated partial least squares regression models.kfolds2Mclassed
Number of missclassified individuals per group for k-fold cross validated partial least squares regression models.kfolds2Mclassedind
Computes PRESS for k-fold cross validated partial least squares regression models.kfolds2Press
Computes individual PRESS for k-fold cross validated partial least squares regression models.kfolds2Pressind
loglikelihood function for plsR modelsloglikpls
Coefficients for permutation bootstrap computations of PLSR modelspermcoefs.plsR
Raw coefficients for permutation bootstrap computations of PLSR modelspermcoefs.plsR.raw
Coefficients for permutation bootstrap computations of PLSGLR modelspermcoefs.plsRglm
Raw coefficients for permutation bootstrap computations of PLSGLR modelspermcoefs.plsRglm.raw
Coefficients for permutation bootstrap computations of PLSGLR modelspermcoefs.plsRglmnp
Coefficients computation for permutation bootstrappermcoefs.plsRnp
Pine datasetpine
Complete Pine datasetpine_full
Complete Pine datasetpine_sup
Incomplete dataset from the pine caterpillars examplepineNAX21
Plot method for table of summary of cross validated plsRglm modelsplot.table.summary.cv.plsRglmmodel
Plot method for table of summary of cross validated plsR modelsplot.table.summary.cv.plsRmodel
Plot bootstrap confidence intervalsplots.confints.bootpls
Light version of PLS_glm for cross validation purposesPLS_glm_wvc
Light version of PLS_lm for cross validation purposesPLS_lm_wvc
Partial least squares Regression models with leave one out cross validationplsR plsRmodel.default plsRmodel.formula PLS_lm PLS_lm_formula
Computation of the Degrees of FreedomplsR.dof
Partial least squares Regression generalized linear modelsplsRglm plsRglmmodel.default plsRglmmodel.formula PLS_glm PLS_glm_formula
Experimental multivariate-response PLS2 modelsplsRmulti plsRmultiModel plsRmultiModel.default plsRmultiModel.formula
Print method for plsRglm modelspredict.plsRglmmodel
Print method for plsR modelspredict.plsRmodel
Predict method for experimental plsRmulti modelspredict.plsRmultiModel
Print method for plsRglm modelsprint.coef.plsRglmmodel
Print method for plsR modelsprint.coef.plsRmodel
Print method for plsRglm modelsprint.cv.plsRglmmodel
Print method for plsR modelsprint.cv.plsRmodel
Print method for plsRglm modelsprint.plsRglmmodel
Print method for plsR modelsprint.plsRmodel
Print method for summaries of plsRglm modelsprint.summary.plsRglmmodel
Print method for summaries of plsR modelsprint.summary.plsRmodel
Graphical assessment of the stability of selected variablessignpred
Data generating detailed process for multivariate plsR modelssimul_data_complete
Data generating function for univariate plsR modelssimul_data_UniYX
Data generating function for univariate binomial plsR modelssimul_data_UniYX_binom
Data generating function for multivariate plsR modelssimul_data_YX
Summary method for plsRglm modelssummary.cv.plsRglmmodel
Summary method for plsR modelssummary.cv.plsRmodel
Summary method for experimental multivariate PLS2 CV modelssummary.cv.plsRmultiModel
Summary method for plsRglm modelssummary.plsRglmmodel
Summary method for plsR modelssummary.plsRmodel
Non-parametric tilted bootstrap for PLS regression modelstilt.bootpls
Non-parametric tilted bootstrap for PLS generalized linear regression modelstilt.bootplsglm
Incomplete dataset for the quality of wine datasetXbordeauxNA
Incomplete dataset from the pine caterpillars exampleXpineNAX21