Nothing

```
#############################################################################################################
# Author :
# Kim-Anh Le Cao, ARC Centre of Excellence in Bioinformatics, Institute for Molecular Bioscience, University of Queensland, Australia
# Leigh Coonan, Student, University of Quuensland, Australia
# Fangzhou Yao, Student, University of Queensland, Australia
# Florian Rohart, The University of Queensland, The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, QLD
#
# created: 2011
# last modified: 21-04-2016
#
# Copyright (C) 2011
#
# 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.
#############################################################################################################
tune.pca =
function(X,
ncomp = NULL,
center = TRUE, # sets the mean of the data to zero, ensures that the first PC describes the direction of the maximum variance
scale = FALSE, # variance is unit accross different units
max.iter = 500,
tol = 1e-09,
logratio = 'none',# one of ('none','CLR','ILR')
V = NULL,
multilevel = NULL)
{
result = pca(X = X, ncomp = ncomp,
center = center, scale = scale,
max.iter = max.iter, tol = tol,
logratio = logratio, V = V,
multilevel = multilevel)
is.na.X = is.na(X)
na.X = FALSE
if (any(is.na.X)) na.X = TRUE
# list eigenvalues, prop. of explained varience and cumulative proportion of explained variance
prop.var = result$explained_variance
cum.var = result$cum.var
ind.show = min(10, ncomp)
print(result)
# Plot the principal components and explained variance
# note: if NA values, we have an estimation of the variance using NIPALS
if(!na.X)
{
ylab = "Proportion of Explained Variance"
} else{
ylab = "Estimated Proportion of Explained Variance"
}
barplot(prop.var[1:result$ncomp], names.arg = 1:result$ncomp, xlab = "Principal Components",
ylab = ylab)
result$call = match.call()
class(result) = "tune.pca"
return(invisible(result))
}
```

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.