popSpectra2D {ChemoSpec2D}  R Documentation 
This function unstacks a Spectra2D
object and conducts IRLBA
PCA on it.
To unstack, each F1 slice (parallel to F2) is concatenated one after the other
so that each 2D spectrum becomes a 1D spectrum. The length of this spectrum will be
equal to the length of the F2 dimension times the length of the F1 dimension.
PCA is performed on the collection of 1D spectra (one spectrum from each 2D spectrum).
The IRLBA algorithm is used because the resulting matrix (n samples in rows x F1 * F2 columns)
can be very large, and other PCA algorithms can struggle.
popSpectra2D(spectra, n = 3, choice = "noscale", ...)
spectra 
An object of S3 class 
n 
Integer. The number of components desired. 
choice 
A character string indicating the choice of scaling. One 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. "autoscale"
is called "standard normal variate" or "correlation matrix PCA"
in some literature. This action is performed on the unstacked matrix, as is centering.
An object of classes prcomp
, pop
and computed_via_irlba
modified to include a list element called $method
, a character string describing the
preprocessing carried out and the type of PCA performed (used to annotate
plots).
Bryan A. Hanson, DePauw University.
J. Baglama and L. Reichel, "Augmented Implicitly Restarted Lanczos Bidiagonalization Methods" SIAM J. Sci. Comput. (2005).
For other data reduction methods for Spectra2D
objects, see
miaSpectra2D
and pfacSpectra2D
.
data(MUD1) res < popSpectra2D(MUD1) plotScores(MUD1, res, main = "POP Scores", ellipse = "cls") plotScree(res) MUD1a < plotLoadings2D(MUD1, res, load_lvls = c(0.2, 0.1, 0.1, 0.2), load_cols = rep("black", 4), main = "POP Comp. 1 Loadings" )