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.
mbplsda(dudiY, ktabX, scale = TRUE, option = c("uniform", "none"),
scannf = TRUE, nf = 2)an object of class dudi containing the dependent variables
an object of class ktab containing the blocks of explanatory variables
logical value indicating whether the explanatory variables should be standardized
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.
logical value indicating whether the eigenvalues bar plot should be displayed
integer indicating the number of components to be calculated
the matching call
data frame of explanatory variables centered, eventually scaled (if scale=TRUE)and weighted (if option="uniform")
data frame of dependent variables centered, eventually scaled (if scale=TRUE)and weighted (if option="uniform")
integer indicating the number of kept dimensions
numeric vector of row weights
numeric vector of column weights for the explanalatory dataset
vector of the numbers of variables in each explanatory dataset
rank of the analysis
numeric vector containing the eigenvalues
dataframe useful to manage graphical outputs
dataframe useful to manage graphical outputs
matrix containing the global variable loadings associated with the global explanatory dataset
matrix containing the partial variable loadings associated with each explanatory dataset(unit norm)
matrix of the variable loadings associated with the dependent dataset
matrix of the global components associated with the whole explanatory dataset(scores of the individuals)
matrix containing the partial components associated with each explanatory dataset
matrix of the components associated with the dependent dataset
squared covariance between lY and TlX
list of matrices of the regression coefficients of the whole explanatory dataset onto the dependent dataset
intercept of the regression of the whole explanatory dataset onto the dependent dataset
list of matrices of the regression coefficients of the whole raw explanatory dataset onto the raw dependent dataset
intercept of the regression of the whole raw explanatory dataset onto the raw dependent dataset
block importances for a given dimension
cumulated block importances for a given number of dimensions
variable importances for a given dimension
cumulated variable importances for a given number of dimensions
no details are needed
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.
# NOT RUN {
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)
# }
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