[SciPy-User] how to use kendalltau_seasonal

[Andreas]

Hi,
thanks for the info!

> Imagine you need to check whether there's a trend in a time series X, but want to take a potential seasonal dependence into account.
> Reshape your data so that it's 2D, with seasons as columns, and feed it to the function.

Ah, good to know.

> You'll get a dictionary giving you the value of Kendall's tau (w/ and w/o the seasonal effects) with the corresponding p-values that should help you figure out without there's a trend in your data and whether seasonality plays a role.

Mhh. I think I do understand the statistics. However, I'm still not
totally sure about all the elements of the returned dictionary:

   scipy.stats.mstats.kendalltau_seasonal(TT.reshape((15,12))).keys()
   Out[39]:
   ['chi2 total',
    'global tau (alt)',
    'chi2 trend',
    'seasonal tau',
    'global p-value (dep)',
    'global tau',
    'global p-value (indep)',
    'seasonal p-value']

Can you please shed some more light on this? Once I understand what the
function does, I'll be more than happy to document it ...

Cheers,
Andreas.

> Example:
> You have a 60-element array corresponding to 5 years of consecutive monthly data, and want to check the trend, taking potential monthly variations into account. In that case, your "season" is a month. Reshape it as a (5,12), and use the result as input to the function.
> 
> More info:
> http://pubs.usgs.gov/sir/2005/5275/pdf/sir2005-5275.pdf
> Hirsch R.M., Slack J.R. and Smith R.A. (1982), Techniques of Trend Analysis
> for Monthly Water Quality Data, Water Resources Research 18(1), 107-12
> 
> I'm sure there must be a R equivalent somewhere.
> In any case, please do help to document the function as needed. Don't hesitate to contact me if you need more info.
> Cheers
> P.
> 
> 
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