hats_alignSpectra2D {ChemoSpec2D}  R Documentation 
Align the spectra in a Spectra2D
object using an implementation
of the HATS algorithm described by Robinette et al.. Currently, only
global, not local, alignment is carried out.
hats_alignSpectra2D( spectra, maxF2 = NULL, maxF1 = NULL, dist_method = "cosine", minimize = FALSE, thres = 0.99, no.it = 20L, restarts = 2L, method = "MBO", fill = "noise", plot = FALSE, debug = 1 )
spectra 
An object of S3 class 
maxF2 
Integer. The most extreme positive 
maxF1 
Integer. As for 
dist_method 
Character. The distance method to use in the objective function.
See 
minimize 
Logical. Is the goal to minimize the objective function? If so, use 
thres 
Numeric. Prior to launching the optimization, the objective function is evaluated
for no shift in case this is actually the best alignment (saving a great deal of time).
If this initial check exceeds the value of 
no.it 
Integer. The maximum number of iterations in the optimization. 
restarts 
Integer. The maximum number of independent rounds of optimization. 
method 
Character. Currently only 
fill 
Aligning spectra requires that at least some spectra be shifted left/right and up/down. When a spectrum is shifted, spaces are opened that must be filled with something:

plot 
Logical. Shall a plot of the alignment progress be made? The plot is useful for diagnostic purposes. Every step of the alignment has a corresponding plot so you should probably direct the output to a pdf file. 
debug 
Integer.

An object of S3 class Spectra2D
.
I suggest that you plot your possibly misaligned spectra first, zooming in on a small region where
alignment might be an issue, and get an idea of the size of the alignment problem. This will help
you choose good values for maxF1
and maxF2
which will speed up the search.
The algorithm uses random numbers to initialize the search, so set the seed for reproducible results. Different seeds may give different results; you may find it useful to experiment a bit and see how the alignment turns out.
Be sure that your choice of thres
, minimize
and dist_method
are selfconsistent.
Some dist_method
choices are bounded, others unbounded, and some should be minimized, others maximized.
You should use sampleDist
to visualize the distances ahead of time. The method
chosen should return a wide numerical range between samples or it won't give a good alignment result.
Bryan A. Hanson, DePauw University.
Roughly follows the algorithm described in Robinette et al. 2011 Anal. Chem. vol. 83, 16491657 (2011) dx.doi.org/10.1021/ac102724x
## Not run: set.seed(123) library("ggally") # for diagnostic plots data(MUD2) sumSpectra(MUD2) mylvls < seq(3, 30, 3) # Plot before alignment plotSpectra2D(MUD2, which = c(2, 3, 5, 6), showGrid = TRUE, lvls = LofL(mylvls, 4), cols = LofC(c("black", "red", "blue", "green"), 4, 10, 2) ) # Carry out alignment # You might want to direct the diagnostic output here to a pdf file # This alignment takes about 90 seconds including the plotting overhead MUD2a < hats_alignSpectra2D(MUD2, method = "MBO", debug = 1, plot = TRUE) # Plot after alignment plotSpectra2D(MUD2a, which = c(2, 3, 5, 6), showGrid = TRUE, lvls = LofL(mylvls, 4), cols = LofC(c("black", "red", "blue", "green"), 4, 10, 2) ) ## End(Not run)