Function reference • plsRglm

All functions
`AICpls()`	AIC function for plsR models
`CorMat`	Correlation matrix for simulating plsR datasets
`Cornell`	Cornell dataset
`PLS_glm_wvc()`	Light version of PLS_glm for cross validation purposes
`PLS_lm_wvc()`	Light version of PLS_lm for cross validation purposes
`XbordeauxNA`	Incomplete dataset for the quality of wine dataset
`XpineNAX21`	Incomplete dataset from the pine caterpillars example
`aic.dof()` `bic.dof()` `gmdl.dof()`	Akaike and Bayesian Information Criteria and Generalized minimum description length
`aze`	Microsatellites Dataset
`aze_compl`	As aze without missing values
`bootpls()`	Non-parametric Bootstrap for PLS models
`bootplsglm()`	Non-parametric Bootstrap for PLS generalized linear models
`bordeaux`	Quality of wine dataset
`bordeauxNA`	Quality of wine dataset
`boxplots.bootpls()`	Boxplot bootstrap distributions
`coef(<plsRglmmodel>)`	coef method for plsR models
`coef(<plsRmodel>)`	coef method for plsR models
`coefs.plsR()`	Coefficients for bootstrap computations of PLSR models
`coefs.plsR.raw()`	Raw coefficients for bootstrap computations of PLSR models
`coefs.plsRglm()`	Coefficients for bootstrap computations of PLSGLR models
`coefs.plsRglm.raw()`	Raw coefficients for bootstrap computations of PLSGLR models
`coefs.plsRglmnp()`	Coefficients for bootstrap computations of PLSGLR models
`coefs.plsRnp()`	Coefficients for bootstrap computations of PLSR models
`confints.bootpls()`	Bootstrap confidence intervals
`cv.plsR()` `cv.plsRmodel(<default>)` `cv.plsRmodel(<formula>)` `PLS_lm_kfoldcv()` `PLS_lm_kfoldcv_formula()`	Partial least squares regression models with k-fold cross-validation
`cv.plsRglm()` `cv.plsRglmmodel(<default>)` `cv.plsRglmmodel(<formula>)` `PLS_glm_kfoldcv()` `PLS_glm_kfoldcv_formula()`	Partial least squares regression glm models with k-fold cross validation
`cvtable()`	Table method for summary of cross validated PLSR and PLSGLR models
`dicho()`	Dichotomization
`fowlkes`	Fowlkes dataset
`infcrit.dof()`	Information criteria
`kfolds2CVinfos_glm()`	Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares glm models
`kfolds2CVinfos_lm()`	Extracts and computes information criteria and fits statistics for k-fold cross validated partial least squares models
`kfolds2Chisq()`	Computes Predicted Chisquare for k-fold cross-validated partial least squares regression models.
`kfolds2Chisqind()`	Computes individual Predicted Chisquare for k-fold cross validated partial least squares regression models.
`kfolds2Mclassed()`	Number of missclassified individuals for k-fold cross validated partial least squares regression models.
`kfolds2Mclassedind()`	Number of missclassified individuals per group for k-fold cross validated partial least squares regression models.
`kfolds2Press()`	Computes PRESS for k-fold cross validated partial least squares regression models.
`kfolds2Pressind()`	Computes individual PRESS for k-fold cross validated partial least squares regression models.
`kfolds2coeff()`	Extracts coefficients from k-fold cross validated partial least squares regression models
`loglikpls()`	loglikelihood function for plsR models
`permcoefs.plsR()`	Coefficients for permutation bootstrap computations of PLSR models
`permcoefs.plsR.raw()`	Raw coefficients for permutation bootstrap computations of PLSR models
`permcoefs.plsRglm()`	Coefficients for permutation bootstrap computations of PLSGLR models
`permcoefs.plsRglm.raw()`	Raw coefficients for permutation bootstrap computations of PLSGLR models
`permcoefs.plsRglmnp()`	Coefficients for permutation bootstrap computations of PLSGLR models
`permcoefs.plsRnp()`	Coefficients computation for permutation bootstrap
`pine`	Pine dataset
`pineNAX21`	Incomplete dataset from the pine caterpillars example
`pine_full`	Complete Pine dataset
`pine_sup`	Complete Pine dataset
`plot(<table.summary.cv.plsRglmmodel>)`	Plot method for table of summary of cross validated plsRglm models
`plot(<table.summary.cv.plsRmodel>)`	Plot method for table of summary of cross validated plsR models
`plots.confints.bootpls()`	Plot bootstrap confidence intervals
`plsR()` `plsRmodel(<default>)` `plsRmodel(<formula>)` `PLS_lm()` `PLS_lm_formula()`	Partial least squares Regression models with leave one out cross validation
`plsR(<dof>)`	Computation of the Degrees of Freedom
`plsRglm-package`	plsRglm-package
`plsRglm()` `plsRglmmodel(<default>)` `plsRglmmodel(<formula>)` `PLS_glm()` `PLS_glm_formula()`	Partial least squares Regression generalized linear models
`predict(<plsRglmmodel>)`	Print method for plsRglm models
`predict(<plsRmodel>)`	Print method for plsR models
`print(<coef.plsRglmmodel>)`	Print method for plsRglm models
`print(<coef.plsRmodel>)`	Print method for plsR models
`print(<cv.plsRglmmodel>)`	Print method for plsRglm models
`print(<cv.plsRmodel>)`	Print method for plsR models
`print(<plsRglmmodel>)`	Print method for plsRglm models
`print(<plsRmodel>)`	Print method for plsR models
`print(<summary.plsRglmmodel>)`	Print method for summaries of plsRglm models
`print(<summary.plsRmodel>)`	Print method for summaries of plsR models
`signpred()`	Graphical assessment of the stability of selected variables
`simul_data_UniYX()`	Data generating function for univariate plsR models
`simul_data_UniYX_binom()`	Data generating function for univariate binomial plsR models
`simul_data_YX()`	Data generating function for multivariate plsR models
`simul_data_complete()`	Data generating detailed process for multivariate plsR models
`summary(<cv.plsRglmmodel>)`	Summary method for plsRglm models
`summary(<cv.plsRmodel>)`	Summary method for plsR models
`summary(<plsRglmmodel>)`	Summary method for plsRglm models
`summary(<plsRmodel>)`	Summary method for plsR models
`tilt.bootpls()`	Non-parametric tilted bootstrap for PLS regression models
`tilt.bootplsglm()`	Non-parametric tilted bootstrap for PLS generalized linear regression models

Reference

All functions