R: Multi-Block Partial Least Squares Discriminant Analysis

packMBPLSDA-package {packMBPLSDA}

R Documentation

Multi-Block Partial Least Squares Discriminant Analysis

Description

Several functions are provided to implement a MBPLSDA : components search, optimal model components number search, optimal model validity test by permutation tests, observed values evaluation of optimal model parameters and predicted categories, bootstrap values evaluation of optimal model parameters and predicted cross-validated categories. The use of this package is described in Brandolini-Bunlon et al (2019. Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134).

Details

Index of help topics:

boot_mbplsda            bootstraped simulations for multi-block partial
                        least squares discriminant analysis
cvpred_mbplsda          Cross-validated predicted categories from a
                        multi-block partial least squares discriminant
                        model
disjunctive             Disjunctive table
ginv                    generalized inverse of a matrix X
inertie                 inertia of a matrix
mbplsda                 Multi-block partial least squares discriminant
                        analysis
medical                 medical dataset
nutrition               nutritional dataset
omics                   metabolomic dataset
packMBPLSDA-package     Multi-Block Partial Least Squares Discriminant
                        Analysis
permut_mbplsda          Permutation testing of a multi-block partial
                        least squares discriminant model
plot_boot_mbplsda       Plot the results of the fonction boot_mbplsda
                        in a pdf file
plot_cvpred_mbplsda     Plot the results of the fonction cvpred_mbplsda
                        in a pdf file
plot_permut_mbplsda     Plot the results of the fonction permut_mbplsda
                        in a pdf file
plot_pred_mbplsda       Plot the results of the fonction pred_mbplsda
                        in a pdf file
plot_testdim_mbplsda    Plot the results of the fonction
                        testdim_mbplsda in a pdf file
pred_mbplsda            Observed parameters and predicted categories
                        from a multi-block partial least squares
                        discriminant model
status                  physiopathological status data
testdim_mbplsda         Test of number of components by two-fold
                        cross-validation for a multi-block partial
                        least squares discriminant model

Author(s)

Marion Brandolini-Bunlon, Stephanie Bougeard, Melanie Petera, Estelle Pujos-Guillot

Maintainer: Marion Brandolini-Bunlon <marion.brandolini-bunlon@inra.fr>

References

Brandolini-Bunlon, M., Petera, M., Gaudreau, P., Comte, B., Bougeard, S., Pujos-Guillot, E.(2019). A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology. Presented at 12emes Journees Scientifiques RFMF, Clermont-Ferrand, FRA(05-21-2019 - 05-23-2019).

Brandolini-Bunlon, M., Petera, M., Gaudreau, P., Comte, B., Bougeard, S., Pujos-Guillot, E.(2019). Multi-block PLS discriminant analysis for the joint analysis of metabolomic and epidemiological data. Metabolomics, 15(10):134

Brandolini-Bunlon, M., Petera, M., Gaudreau, P., Comte, B., Bougeard, S., Pujos-Guillot, E.(2020). A new tool for multi-block PLS discriminant analysis of metabolomic data: application to systems epidemiology. Presented at Chimiometrie 2020, Liege, BEL(01-27-2020 - 01-29-2020).

Examples

data(status)
data(medical)
data(omics)
data(nutrition)
ktabX <- ktab.list.df(list(medical = medical, nutrition = nutrition, omics = omics))
disjonctif <- (disjunctive(status))
dudiY   <- dudi.pca(disjonctif , center = FALSE, scale = FALSE, scannf = FALSE)
modelembplsQ <- mbplsda(dudiY, ktabX, scale = TRUE, option = "uniform", scannf = FALSE, nf = 2)