ChemoSpec2D: pfacSpectra2D – R documentation

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pfacSpectra2D

PARAFAC Analysis of a Spectra2D Object

Description

Carry out PARAFAC analysis of a Spectra2D object. Function parafac from multiway is used. For large data sets, computational time may be long enough that it might desirable to run in batch mode and possibly use parallel processing.

Usage

pfacSpectra2D(spectra, parallel = FALSE, setup = FALSE, nfac = 2, ...)

Arguments

`spectra`	An object of S3 class `Spectra2D`.
`parallel`	Logical. Should parallel processing be used? Unless you love waiting, you should use parallel processing for larger data sets. If you are working on a shared machine and/or another process (created by you or another user) might also try to access all or some of the cores in your CPU, you should be careful to avoid hogging the cores. `parallel::detectCores()` will tell you how many cores are available to everyone. You can run `options(mc.cores = 2)` to set the number of cores this function will use.
`setup`	Logical. If `TRUE` the parallel environment will be automatically configured for you. If `FALSE`, the user must configure the environment themselves (desirable for instance if working on Azure or AWS EC2).
`nfac`	Integer. The number of factors/components to compute.
`...`	Additional parameters to be passed to function `parafac`. You should give thought to value of `const`, allowed options can be seen in `const`. The default is to compute an unconstrained solution. However, in some cases one may wish to apply a non-negativity constraint. Also, to suppress the progress bar, you can use `verbose = FALSE`.

Value

An object of class pfac and parafac, modified to include a list element called $method which is parafac.

Warning

To get reproducible results you will need to set.seed(). See the example.

Author(s)

Bryan A. Hanson, DePauw University.

References

R. Bro "PARAFAC. Tutorial and applications" Chemometrics and Intelligent Laboratory Systems vol. 38 pgs. 149-171 (1997).

A. Smilde, R. Bro and P. Geladi "Multi-way Analysis: Applications in the Chemical Sciences" Wiley (2004).

Examples

data(MUD1)
set.seed(123)
res <- pfacSpectra2D(MUD1, parallel = FALSE, nfac = 2)
plotScores(MUD1, res,
  leg.loc = "topright",
  ellipse = "cls", main = "PARAFAC Score Plot"
)
res1 <- plotLoadings2D(MUD1, res,
  load_lvls = c(1, 5, 10, 15, 25),
  main = "PARAFAC Comp. 1 Loadings"
)
res2 <- plotLoadings2D(MUD1, res,
  load_lvls = c(1, 5, 10, 15, 25),
  ref = 2, ref_lvls = seq(5, 35, 5),
  ref_cols = rep("black", 7),
  main = "PARAFAC Comp. 1 Loadings + Ref. Spectrum"
)

# Selection of loading matrix levels can be aided by the following
# Use res1$names to find the index of the loadings

inspectLvls(res1,
  which = 11, ylim = c(0, 50),
  main = "Histogram of Loadings Matrix"
)

ChemoSpec2D

Exploratory Chemometrics for 2D Spectroscopy

v0.4.187

GPL-3

Authors

Bryan A. Hanson [aut, cre] (<https://orcid.org/0000-0003-3536-8246>)

Initial release

2021-03-15