MultANOVA (version 1.0.1)

DCDA: Diagonal Canonical Discriminant Analysis (DCDA)

Description

Performs the Diagonal Canonical Discriminant Analysis of a term from the model defined by formula as defined in Mahieu & Cariou (2025).

Usage

DCDA(formula, design, responses, term)

Value

Returns a list of the following elements:

scores

A data.frame giving the scores of the individuals of the diagonal canonical variates.

eigen

A matrix giving the eigenvalues, corresponding percentages of inertia and cumulative percentages of inertia of the DCDA.

level.coord

A matrix giving the coordinates of the levels of the term on the diagonal canonical variates.

var.coord

A matrix giving the coordinates of the variables of the on the diagonal canonical variates.

raw.coef

A matrix giving the raw diagonal canonical coefficients.

error

A list containing several information relative to the error. Most of this information are used to compute the approximate ellipses when calling plot.DCDA.

svd

Results of the svd of the weighted orthogonalized effect matrix of term.

Arguments

formula

A formula with no left term that specify the model from the elements of the design argument.

design

A data.frame that contains only factors specifying the design on which rely the specified model of formula argument.

responses

A matrix or data.frame that contains only numerics or integers being the responses variables to be explained by the model from formula.

term

A character specifying the term from formula for which the MultLSD tests must be performed.

References

Mahieu, B., & Cariou, V. (2025). MultANOVA Followed by Post Hoc Analyses for Designed High‐Dimensional Data: A Comprehensive Framework That Outperforms ASCA, rMANOVA, and VASCA. Journal of Chemometrics, 39(7). tools:::Rd_expr_doi("https://doi.org/10.1002/cem.70039")

Dudoit, S., Fridlyand, J., & Speed, T. P. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. Journal of the American Statistical Association, 97(457), 77–87. tools:::Rd_expr_doi("https://doi.org/10.1198/016214502753479248")

Examples

data(OTU)
acd=DCDA(~Lot+Atm+Time,OTU[,1:4],OTU[,-c(1:4)],"Time")
lsd=MultLSD(~Lot+Atm+Time,OTU[,1:4],OTU[,-c(1:4)],"Time")
fish=FisherS(~Lot+Atm+Time,OTU[,1:4],OTU[,-c(1:4)],"Time")
plot(acd,axes = c(1,2),pair.comp = lsd,expansion.var = 1.5,select.var = which(fish[2,]<=0.05))