PCA by nonlinear iterative partial least squares
1 2  RnipalsPca(Matrix, nPcs = 2, varLimit = 1, maxSteps = 5000,
threshold = 1e06, verbose = interactive(), ...)

Matrix 
Preprocessed (centered, scaled) numerical matrix samples in rows and variables as columns. 
nPcs 
Number of components that should be extracted. 
varLimit 
Optionally the ratio of variance that should be
explained. 
maxSteps 
Defines how many iterations can be done before algorithm should abort (happens almost exclusively when there were some wrong in the input data). 
threshold 
The limit condition for judging if the algorithm has converged or not, specifically if a new iteration is done if (T_{old}  T)^T(T_{old}  T) > \code{limit}. 
verbose 
Show simple progress information. 
... 
Only used for passing through arguments. 
Can be used for computing PCA on a numeric matrix using either the
NIPALS algorithm which is an iterative approach for estimating the
principal components extracting them one at a time. NIPALS can
handle a small amount of missing values. It is not recommended to
use this function directely but rather to use the pca() wrapper
function. There is a C++ implementation given as nipalsPca
which is faster.
A pcaRes
object.
Henning Redestig
Wold, H. (1966) Estimation of principal components and related models by iterative least squares. In Multivariate Analysis (Ed., P.R. Krishnaiah), Academic Press, NY, 391420.
1 2 3 4 5 6 7  data(metaboliteData)
mat < prep(t(metaboliteData))
## c++ version is faster
system.time(pc < RnipalsPca(mat, method="rnipals", nPcs=2))
system.time(pc < nipalsPca(mat, nPcs=2))
## better use pca()
pc < pca(t(metaboliteData), method="rnipals", nPcs=2)

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