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#############################################################################################################
# Authors:
# Florian Rohart, 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: 15-04-2015
# 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.
#############################################################################################################
# ========================================================================================================
# Calculate the explained variance of one dataset based on its variates
# ========================================================================================================
explained_variance = function(data, variates, ncomp)
{
#check input data
check = Check.entry.single(data, ncomp)
data = check$X
ncomp = check$ncomp
if (anyNA(data))
{
warning("NA values put to zero, results will differ from PCA methods used with NIPALS")
isna = is.na(data)
data[isna] = 0
}
nor2x <- sum((data)^2) # total variance in the data
exp.varX = NULL
for (h in 1:ncomp)
{
a <- t(variates[, h, drop=FALSE]) %*% data
ta = t(a)
exp_var_new <- a%*%ta /crossprod(variates[, h],variates[, h])/nor2x
exp.varX = append(exp.varX, exp_var_new)
}
names(exp.varX) = paste("comp", 1:ncomp)
# result: vector of length ncomp with the explained variance per component
exp.varX
}
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