Package index
-
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()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