mbplsda: Multi-block partial least squares discriminant analysis

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/mbplsda.R

Description

Function to perform a multi-block partial least squares discriminant analysis (MBPLSDA) of several explanatory blocks defined as an object of class ktab, to explain a dependent dataset (Y-block) defined as an object of class dudi, in order to get model parameters for the indicated number of components.

Usage

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mbplsda(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"), 
scannf = TRUE, nf = 2)

Arguments

dudiY

an object of class dudi containing the dependent variables

ktabX

an object of class ktab containing the blocks of explanatory variables

scale

logical value indicating whether the explanatory variables should be standardized

option

option for the block weighting. If uniform, the weight of each explanatory block is equal to 1/number of explanatory blocks, and the weight of the Y-block is eqyual to 1. If none, the block weight is equal to the block inertia.

scannf

logical value indicating whether the eigenvalues bar plot should be displayed

nf

integer indicating the number of components to be calculated

Details

no details are needed

Value

call

the matching call

tabX

data frame of explanatory variables centered, eventually scaled (if scale=TRUE)and weighted (if option="uniform")

tabY

data frame of dependent variables centered, eventually scaled (if scale=TRUE)and weighted (if option="uniform")

nf

integer indicating the number of kept dimensions

lw

numeric vector of row weights

X.cw

numeric vector of column weights for the explanalatory dataset

blo

vector of the numbers of variables in each explanatory dataset

rank

rank of the analysis

eig

numeric vector containing the eigenvalues

TL

dataframe useful to manage graphical outputs

TC

dataframe useful to manage graphical outputs

faX

matrix containing the global variable loadings associated with the global explanatory dataset

Tc1

matrix containing the partial variable loadings associated with each explanatory dataset(unit norm)

Yc1

matrix of the variable loadings associated with the dependent dataset

lX

matrix of the global components associated with the whole explanatory dataset(scores of the individuals)

TlX

matrix containing the partial components associated with each explanatory dataset

lY

matrix of the components associated with the dependent dataset

cov2

squared covariance between lY and TlX

XYcoef

list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset

intercept

intercept of the regression of the whole explanatory dataset onto the dependent dataset

XYcoef.raw

list of matrices of the regression coefficients of the whole raw explanatory dataset onto the raw dependent dataset

intercept.raw

intercept of the regression of the whole raw explanatory dataset onto the raw dependent dataset

bip

block importances for a given dimension

bipc

cumulated block importances for a given number of dimensions

vip

variable importances for a given dimension

vipc

cumulated variable importances for a given number of dimensions

Note

This function is coming from the mbpls function of the R package ade4 (application in order to explain a disjunctive table, limitation of the number of calculated components)

Author(s)

Marion Brandolini-Bunlon (<marion.brandolini-bunlon@inra.fr>) and Stephanie Bougeard (<stephanie.bougeard@anses.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

Bougeard, S. and Dray, S. (2018) Supervised Multiblock Analysis in R with the ade4 Package.Journal of Statistical Software,86(1), 1-17.

See Also

packMBPLSDA-package

Examples

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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)

packMBPLSDA documentation built on March 15, 2020, 9:06 a.m.