packMBPLSDA-package | R Documentation |
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).
Index: This package was not yet installed at build time.
Marion Brandolini-Bunlon, Stephanie Bougeard, Melanie Petera, Estelle Pujos-Guillot
Maintainer: Marion Brandolini-Bunlon <marion.brandolini-bunlon@inra.fr>
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).
mbplsda
testdim_mbplsda
plot_testdim_mbplsda
permut_mbplsda
plot_permut_mbplsda
pred_mbplsda
plot_pred_mbplsda
cvpred_mbplsda
plot_cvpred_mbplsda
boot_mbplsda
plot_boot_mbplsda
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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.