Re: data fitting/analysis - Spline?
- From: mariobardini@xxxxxxxxx
- Date: Thu, 4 Sep 2008 17:23:57 -0700 (PDT)
On Sep 3, 11:52 pm, Uwe Schmitt <rocksportroc...@xxxxxxxxxxxxxx>
wrote:
On 3 Sep., 20:22, mariobard...@xxxxxxxxx wrote:
Hi.
I don't know if i'm writing in the right group but i wrote in the
matlab's one and i haven't received any answer so far.
So i hope here omebody can help me. This is my problem:
I'm analyzing a list of x-y values,http://www.geocities.com/mariopwr/data.png
(http://www.geocities.com/mariopwr/dataxy.zipforthe .mat file) ,and
i'd like to find out the best function that fits these points.
I tried using the matlab spline toolbox and I found a quite close (in
my opinion) fit using the least square approximation with order 14
(http://www.geocities.com/mariopwr/spline.png). Do you think it's a
good approximation or should i try a different method?
I'd like to find a way to fit these points and later on other similar
(with similar trend) data.
Anyway, after that i'd want to extract a few information from this
graph like amplitude and width of each wave. Any
suggestion?
Thank you for your hel
Mario
Can you say something about the origin of the data ? Which kind of
noise do you have ?
Do you have a modell for your data ?
You could use leave-one-out or crossvalidation for finding the best
parameters for fitting.
Greetings, Uwe
The values describe the movement (variation of y-coordinate) of the
centre of mass of an animal.
I don't have any model for my data. I just know that the ideal graph
should be periodic and every period describe a full stride. In a
period there are 4 peaks, when the (back or front) legs of the animal
overlap. In data.png you can see there are 8 peaks so that graph
describe almost 2 complete strides.
I was thinking about describing the data using a sum of sinusoidal
function and then compare different graphs paying attention to the
variation of the coefficients that describe the function. But I don't
know if it makes sense and if I get something interesting.
Cheers,
Mario.
.
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