Description Usage Arguments Details Value Author(s) See Also Examples
Wrapper function for the als
function in the ALS
package, providing a simple interface with sensible defaults for
hyphenated data.
1 2 3 4 5 6 7 8 9 10 |
Xl |
a list of (numerical) data matrices. Missing values are not allowed. |
x, object |
an object of class ALS. |
PureS |
Initial estimates of pure spectral components. |
maxiter |
maximum number of iterations in ALS. |
what |
Show spectra or elution profiles |
showWindows |
If showing elution profiles, the window borders and the overlap areas between the windows can be shown (by default). Simply set this parameter to FALSE if this is undesired. |
mat.idx |
If showing elution profiles, one can provide the index of the sample(s) that should be shown. For every sample one plot will be made. Default is to show all. |
comp.idx |
Indices of components to be shown. Default is to show all components. |
xlab, ylab, main, ... |
self-explanatory optional arguments |
The plot
method can be used to plot the spectral
components (one plot for the model) or the elution profiles (one plot
for each data matrix, so usually several plots). The summary
method also returns fit statistics like LOF, R2 and RMS. Extractor
functions getTime
and getWavelength
provide the vectors
of time points and wavelengths from the ALS
object.
Function doALS
returns an object of class "ALS", a list
with the following fields:
CList |
a list of matrices with the elution profiles in the columns. Every matrix in this list corresponds with a matrix in the input. |
S |
a matrix with the spectral components in the columns. These are common for all data matrices. |
rss |
residual sum of squares. |
resid |
a list of residual matrices. |
iter |
number of iterations. |
summ.stats |
summary statistics, providing more information about the fit quality. |
See the als
function for more details; only the
summ.stats
field is not part of the original als
output.
Ron Wehrens
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(tea)
new.lambdas <- seq(260, 500, by = 2)
tea <- lapply(tea.raw, preprocess, dim2 = new.lambdas)
tea.split <- splitTimeWindow(tea, c(12, 14), overlap = 10)
Xl <- tea.split[[2]]
Xl.opa <- opa(Xl, 4)
Xl.als <- doALS(Xl, Xl.opa)
Xl.als
summary(Xl.als)
plot(Xl.als, "spectra")
par(mfrow = c(1, 3))
plot(Xl.als, "profiles", ylim = c(0, 600), mat.idx = 1:3)
|
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