Discover significant change in sorted vector

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