[R] baseline fitters

[Thaden, John J]

I am pretty pleased with baselines I fit to chromatograms using the
runquantile() function in caTools(v1.6) when its probs parameter is 
set to 0.2 and its k parameter to ~1/20th of n (e.g., k ~ 225 for n ~ 
4500, where n is time series length).  This ignores occasional low-
side outliers, and, after baseline subtraction, I can re-adjust any
negative values to zero.
  
But runquantile's computation time proves exceedingly long for my large
datasets, particularly if I set the endrule parameter to 'func'.  Here is
what caTools author Jarek Tuszynski says about relative speeds of various
running-window functions:

   - runmin, runmax, runmean run at O(n) 
   - runmean(..., alg="exact") can have worst case speed of O(n^2) for 
     some small data vectors, but average case is still close to O(n). 
   - runquantile and runmad run at O(n*k) 
   - runmed - related R function runs at O(n*log(k))

The obvious alternative runmin() performs poorly due to dropout (zero-
and low-value 'reverse-spikes') in the data. And runmed fits a baseline that,
upon subtraction, obviously will send half the values into the negative, not
suitable for my application. I jimmied something together
with runmin and runmedian that is considerably faster; unfortunately,
the fit seems less good, at least by eye, due still to the bad runmin
behavior.

I'd be interested in other baseline fitting suggestions implemented
or implementable in R (I'm using v. 2.4.1pat under WinXP).  Why, for
instance, can I not find a running trimmean function? Because it 
offers no computational savings over runquantile?

Also, the 'func' setting for the caTools endrule parameter--which adjusts the
value of k as ends are approached--is said not to be optimized (using
this option does add further time to computations).  Is there an alter-
native that would be speedier, e.g., setting endrule = "keep" and then
subsequently treating ends with R's smoothEnds() function?

-John Thaden
Little Rock, Arkansas USA

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