NIPALS PCA
PCA by non-linear iterative partial least squares
nipalsPca(Matrix, nPcs = 2, varLimit = 1, maxSteps = 5000, threshold = 1e-06, ...)
Matrix |
Pre-processed (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}. |
... |
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.
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, 391-420.
prcomp, princomp, pca
data(metaboliteData) mat <- prep(t(metaboliteData)) pc <- nipalsPca(mat, nPcs=2) ## better use pca() pc <- pca(t(metaboliteData), method="nipals", nPcs=2)
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