[R] Discover significant change in sorted vector

[Hans-Henning Gabriel]

Gabor, initially this looked like the perfect solution, exactly what I  
need.

Unfortunately it is too expensive/costly. I have vectors of length 800  
and more, my machine needs > 5 minutes (I aborted) to compute the  
breakpoints. Required is computation time < 1 sec. :)

Any other suggestions? Maybe there is another approach not that  
perfect as from the strucchange package, but still sufficient?

Best
Henning


Am 22.04.2009 um 14:55 schrieb Gabor Grothendieck:

> Try this:
>
>> a <- c(2,3,3,5,6,8,8,9,15, 25, 34,36,36,38,41,43,44,44,46);
>> ix <- seq_along(a)
>> library(strucchange)
>> bp <- breakpoints(a ~ ix, h = 4)
>> bp
>
>         Optimal 3-segment partition:
>
> Call:
> breakpoints.formula(formula = a ~ ix, h = 4)
>
> Breakpoints at observation number:
> 7 11
>
> Corresponding to breakdates:
> 0.3684211 0.5789474
>> plot(a ~ ix)
>> lines(ix, fitted(bp))
>
>
> On Wed, Apr 22, 2009 at 7:27 AM, Hans-Henning Gabriel
> <hanshenning.gabriel at gmail.com> wrote:
>> Hi,
>>
>> suppose I have a simple sorted vector like this:
>>
>> a <- c(2,3,3,5,6,8,8,9,15, 25, 34,36,36,38,41,43,44,44,46);
>>
>> Is there a function in R, I can use to discover that from index 8  
>> to index
>> 11 the values are changing significantly?
>> The function should return a value pointing to one of the indices  
>> 8, 9, 10
>> or 11. Any of them would be fine.
>> The difficulty is that there may be no big gap. I mean, indices 8  
>> and 11 are
>> somehow "connected" by indices 9 and 10. So, it's not an option to  
>> just
>> search for biggest difference between the values.
>>
>> Perfect would be a function that is able to discover multiple  
>> changes if it
>> is present in the data.
>>
>> Thanks!!
>> Henning
>>
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