decomposition: Convert Principal Component Decomposition or the like into a...

In hyperSpec: Work with Hyperspectral Data, i.e. Spectra + Meta Information (Spatial, Time, Concentration, ...)

Description

Decomposition of the spectra matrix is a common procedure in chemometric data analysis. scores and loadings convert the result matrices into new hyperSpec objects.

Usage

decomposition(object, x, wavelength = seq_len(ncol(x)), label.wavelength,
  label.spc, scores = TRUE, retain.columns = FALSE, ...)

Arguments

object	A hyperSpec object.
x	matrix with the new content for object@data$spc. Its size must correspond to rows (for scores) and to either columns or rows (for loadings) of object.
wavelength	for a scores-like x: the new object@wavelength.
label.wavelength	The new label for the wavelength axis (if x is scores-like)
label.spc	The new label for the spectra matrix
scores	is x a scores-like matrix?
retain.columns	for loading-like decompostition (i.e. x holds loadings, pure component spectra or the like), the data columns need special attention. Columns with different values across the rows will be set to NA if retain.columns is TRUE, otherwise they will be deleted.
...	ignored.

Details

Multivariate data are frequently decomposed by methods like principal component analysis, partial least squares, linear discriminant analysis, and the like. These methods yield latent spectra (or latent variables, loadings, components, ...) that are linear combination coefficients along the wavelength axis and scores for each spectrum and loading.

The loadings matrix gives a coordinate transformation, and the scores are values in that new coordinate system.

The obtained latent variables are spectra-like objects: a latent variable has a coefficient for each wavelength. If such a matrix (with the same number of columns as object has wavelengths) is given to decomposition (also setting scores = FALSE), the spectra matrix is replaced by x. Moreover, all columns of object@data that did not contain the same value for all spectra are set to NA. Thus, for the resulting hyperSpec object, plotspc and related functions are meaningful. plotmap cannot be applied as the loadings are not laterally resolved.

The scores matrix needs to have the same number of rows as object has spectra. If such a matrix is given, decomposition will replace the spectra matrix is replaced by x and object@wavelength by wavelength. The information related to each of the spectra is retained. For such a hyperSpec object, plotmap and plotc and the like can be applied. It is also possible to use the spectra plotting, but the interpretation is not that of the spectrum any longer.

Value

A hyperSpec object, updated according to x

Author(s)

C. Beleites

See Also

See %*% for matrix multiplication of hyperSpec objects.

See e.g. prcomp and princomp for principal component analysis, and package pls for Partial Least Squares Regression.

Examples

pca <- prcomp (flu)

pca.loadings <- decomposition (flu, t (pca$rotation), scores = FALSE)
pca.center <- decomposition (flu, pca$center, scores = FALSE)
pca.scores <- decomposition (flu, pca$x)

plot (pca.center)
plot (pca.loadings, col = c ("red", "gray50"))
plotc (pca.scores, groups = .wavelength)

Example output

Loading required package: lattice
Loading required package: grid
Loading required package: ggplot2
Package hyperSpec, version 0.98-20161118

To get started, try
   vignette ("introduction", package = "hyperSpec")
   package?hyperSpec 
   vignette (package = "hyperSpec")

If you use this package please cite it appropriately.
   citation("hyperSpec")
will give you the correct reference.

The project homepage is http://hyperspec.r-forge.r-project.org

hyperSpec documentation built on May 2, 2019, 5:45 p.m.