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```
#############################################################################################################
# Authors:
# Florian Rohart, The University of Queensland, The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD
# Benoit Gautier, The University of Queensland, The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD
# Kim-Anh Le Cao, The University of Queensland, The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD
#
# created: 22-04-2015
# last modified: 04-10-2017
#
# Copyright (C) 2015
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
#############################################################################################################
# ========================================================================================================
# block.plsda: perform a horizontal PLS-DA on a combination of datasets, input as a list in X
# this function is a particular setting of internal_mint.block, the formatting of the input is checked in internal_wrapper.mint.block
# ========================================================================================================
# X: a list of data sets (called 'blocks') matching on the same samples. Data in the list should be arranged in samples x variables, with samples order matching in all data sets. \code{NA}s are not allowed.
# Y: a factor or a class vector for the discrete outcome.
# indY: to supply if Y is missing, indicate the position of the outcome in the list X.
# ncomp: numeric vector of length the number of blocks in \code{X}. The number of components to include in the model for each block (does not necessarily need to take the same value for each block). By default set to 2 per block.
# design: the input design.
# scheme: the input scheme, one of "horst", "factorial" or ""centroid". Default to "centroid"
# mode: input mode, one of "canonical", "classic", "invariant" or "regression". Default to "regression"
# scale: boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE).
# init: intialisation of the algorithm, one of "svd" or "svd.single". Default to "svd"
# tol: Convergence stopping value.
# max.iter: integer, the maximum number of iterations.
# near.zero.var: boolean, see the internal \code{\link{nearZeroVar}} function (should be set to TRUE in particular for data with many zero values). Setting this argument to FALSE (when appropriate) will speed up the computations
# all.outputs: calculation of non-essential outputs (e.g. explained variance, loadings.Astar, etc)
block.plsda = function(X,
Y,
indY,
ncomp = 2,
design,
scheme,
mode,
scale = TRUE,
init = "svd",
tol = 1e-06,
max.iter = 100,
near.zero.var = FALSE,
all.outputs = TRUE)
{
# check inpuy 'Y' and transformation in a dummy matrix
if (!missing(Y))
{
if (is.null(dim(Y)))
{
Y = factor(Y)
} else {
stop("'Y' should be a factor or a class vector.")
}
if (nlevels(Y) == 1)
stop("'Y' should be a factor with more than one level")
Y.input = Y
Y = unmap(Y)
colnames(Y) = levels(Y.input)
rownames(Y) = rownames(X[[1]])
} else if (!missing(indY)) {
temp = X[[indY]] #not called Y to not be an input of the wrapper.sparse.mint.block
if (is.null(dim(temp)))
{
temp = factor(temp)
} else {
stop("'Y' should be a factor or a class vector.")
}
if (nlevels(temp) == 1)
stop("'X[[indY]]' should be a factor with more than one level")
Y.input = temp
X[[indY]] = unmap(temp)
colnames(X[[indY]]) = levels(Y.input)
rownames(X[[indY]]) = rownames(X[[ifelse(indY==1,2,1)]])
} else if (missing(indY)) {
stop("Either 'Y' or 'indY' is needed")
}
# call to 'internal_wrapper.mint.block'
result = internal_wrapper.mint.block(X=X, Y=Y, indY=indY, ncomp=ncomp, design=design, scheme=scheme, mode=mode, scale=scale,
init=init, tol=tol, max.iter=max.iter, near.zero.var=near.zero.var, all.outputs = all.outputs)
# calculate weights for each dataset
weights = get.weights(result$variates, indY = result$indY)
# choose the desired output from 'result'
out=list(call = match.call(),
X = result$A[-result$indY],
Y = Y.input,
ind.mat = result$A[result$indY][[1]],
ncomp = result$ncomp,
mode = result$mode,
variates = result$variates,
loadings = result$loadings,
crit = result$crit,
AVE = result$AVE,
names = result$names,
init = result$init,
tol = result$tol,
iter = result$iter,
max.iter = result$max.iter,
nzv = result$nzv,
scale = result$scale,
design = result$design,
scheme = result$scheme,
indY = result$indY,
weights = weights,
explained_variance = result$explained_variance)#[-result$indY])
# give a class
class(out) = c("block.plsda","block.pls","sgccda","sgcca","DA")
return(invisible(out))
}
```

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