Sparse PCA of Spectra Objects
A wrapper which carries out sparse PCA analysis on a
Spectra object. The user can select various options for
scaling. There is no normalization by rows - do this manually using
normSpectra. The data will be centered, as is required by PCA.
s_pcaSpectra(spectra, choice = "noscale", K = 3, para = rep(0.5, K), ...)
spectra |
An object of S3 class |
choice |
A character string indicating the choice of scaling. One of
|
K |
Integer. The number of components desired. |
para |
A vector of |
... |
Other parameters to be passed to |
The scale choice autoscale scales the columns by their standard
deviation. Pareto scales by the square root of the standard
deviation.
An object of class prcomp and converted_from_arrayspc,
which includes a list
element called $method, a character string describing the
pre-processing carried out and the type of PCA performed (used to annotate
plots). A check is carried out to see if the computation was successful
and a warning issued if it failed.
Bryan A. Hanson, DePauw University.
H. Zou, T. Hastie and R. Tibshirani "Sparse Principal Components Analysis" J. Comp. Stat. Graphics vol. 15 no. 2 pgs. 265-286 (2006).
arrayspc for the underlying function,
c_pcaSpectra for classical PCA calculations,
r_pcaSpectra for robust PCA calculations,
irlba_pcaSpectra for PCA via the IRLBA algorithm.
Additional documentation at https://bryanhanson.github.io/ChemoSpec/
For displaying the results, plotScree,
plotScores, plotLoadings,
plot2Loadings, sPlotSpectra,
plotScores3D, plotScoresRGL.
data(SrE.NMR) pca <- s_pcaSpectra(SrE.NMR) plotScree(pca) plotScores(SrE.NMR, pca, main = "SrE NMR Data", pcs = c(1, 2), ellipse = "cls", tol = 0.05 ) plotLoadings(SrE.NMR, pca, main = "SrE NMR Data", loads = 1:2, ref = 1 )
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