Re: how to speed up multiple regressions?

[Craig Markwardt <craigmnet(at)cow.physics.wisc.edu>]

Charlotte DeMott <demott@atmos.colostate.edu> writes:

> Hi Craig,
> 
> Thanks for taking a look.  I was hoping someone would point out to me a very
> obvious blunder I was making.  I had high hopes upon reading your message,
> but I think I'm sticking all of this in a loop to compute the significance
> because datadof is NOT a constant for all points in my array.  In the first
> loop I included in the original post, data_tau is the decorrelation timescale
> at each data point which is, unfortunately, not constant.  T_CVF, as you
> indicated, requires the 2nd argument (datadof in my case) to be a scalar.  My
> problem is that datadof isn't the same for all data points.
> 
> However, your post make me realize that I can do the regression in a slightly
> different way that will eliminate this problem, and save me loads of time.
> 
> So while your suggestion wasn't the fix I was looking for, it jarred my tired
> brain enough to think of another work-around.  So thanks!

Okay, so I didn't see that data_tau was variable, sorry :-)

I'm glad you figured out a way to do it.  I wanted to mention a
possible way to get around the T_CVF problem.  What you can do is
precompute a table of values and then use spline interpolation.  Spine
interpolation using SPL_INIT and SPL_INTERP is vectorizable, so it
should be really fast.  You can put a lot of samples into your table
so it can be quite precise (you might interpolate in log-log space if
your dynamic range is large).

Craig

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Craig B. Markwardt, Ph.D.         EMAIL:    craigmnet@cow.physics.wisc.edu
Astrophysics, IDL, Finance, Derivatives | Remove "net" for better response
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