`hyperSpec`

object`R/decomposition.R`

`decomposition.Rd`

Decomposition of the spectra matrix is a common procedure in chemometric
data analysis. `scores`

and `loadings`

convert the result matrices
into new `hyperSpec`

objects.

- 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). If not given, the label of`object`

is kept.- label.spc
The new label for the spectra matrix. If not given, the label of

`object`

is kept.- 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.

A `hyperSpec`

object, updated according to `x`

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, `plot_spc()`

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.

See %*% for matrix multiplication of `hyperSpec`

objects.

See e.g. `stats::prcomp()`

and `stats::princomp()`

for
principal component analysis, and package `pls`

for Partial Least
Squares Regression.