Re: [R] multiple comparisons of time series data

From: Thomas Adams <Thomas.Adams_at_noaa.gov>
Date: Tue 30 May 2006 - 04:41:34 EST

Kyle,

You might try the Wilcoxon Rank Sum test (and there is also the paired rank sum test) that may be useful. Both are found in R. There is an application of the test in the textbook by Loucks, D.P., Stedinger J.R., and Haith, D., 1981. "Water Resources Systems Planning and Analysis", Prentice-Hall, Eaglewood Cliffs, New Jersey. I hope this helps…

Go HOKIES!

Tom

Kyle Hall wrote:
> I am interested in a statistical comparison of multiple (5) time series'
> generated from modeling software (Hydrologic Simulation Program Fortran). The
> model output simulates daily bacteria concentration in a stream. The multiple
> time series' are a result of varying our representation of the stream within
> the model.
>
> Our main question is: Do the different methods used to represent a stream
> produce different results at a statistically significant level?
>
> We want to compare each otput time series to determine if there is a
> difference before looking into the cause within the model. In a previous
> study, the Kolmogorov-Smirnov k-sample test was used to compare multiple time
> series'.
>
> I am unsure about the strength of the Kolmogorov-Smirnov test and I have set
> out to determine if there are any other tests to compare multiple time
> series'.
>
> I know htat R has the ks.test but I am unsure how this test handles multiple
> comparisons. Is there something similar to a pairwise.t.test with a
> bonferroni corection, only with time series data?
>
> Does R currently (v 2.3.0) have a comparison test that takes into account the
> strong serial correlation of time series data?
>
>
> Kyle Hall
>
> Graduate Research Assistant
> Biological Systems Engineering
> Virginia Tech
>
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-- 
Thomas E Adams
National Weather Service
Ohio River Forecast Center
1901 South State Route 134
Wilmington, OH 45177

EMAIL:	thomas.adams@noaa.gov

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FAX:	937-383-0033

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Received on Tue May 30 04:52:29 2006

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