mbplsda: Multi-block partial least squares discriminant analysis

In packMBPLSDA: Multi-Block Partial Least Squares Discriminant Analysis

Multi-block partial least squares discriminant analysis

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

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 (<[e-mail]>) and Stephanie Bougeard (<[e-mail]>)

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

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 June 20, 2022, 5:08 p.m.