Search All of the Math Forum:
Views expressed in these public forums are not endorsed by
Drexel University or The Math Forum.
|
|
Marc
Posts:
89
Registered:
7/2/10
|
|
Re: fit power curve with best exponent to data
Posted:
Feb 1, 2013 2:00 AM
|
|
"JK" wrote in message <kebtjc$kp3$1@newscl01ah.mathworks.com>...
> "Tom Lane" <tlane@mathworks.com> wrote in message <kebk50$bq1$1@newscl01ah.mathworks.com>...
> > >> I'm attempting to fit the following equation to a set of data
> > >> characterizing something's autocorrelation function:
> > >>
> > >> y=e^(a*x)^n
> >
> > Your y seems to drop off exponentially, so you want the exponent to be
> > negative, and you're raising it to a power that may not be an integer. If I
> > change your function I can do the following to fit it:
> >
> > >> myexp = fittype('exp(a*(abs(x-b)^n))')
> > myexp =
> > General model:
> > myexp(a,b,n,x) = exp(a*(abs(x-b)^n))
> > >> fit(x,y,myexp,'start',[-20,4.47,1])
> > ans =
> > General model:
> > ans(x) = exp(a*(abs(x-b)^n))
> > Coefficients (with 95% confidence bounds):
> > a = -103.6 (-115.1, -92.15)
> > b = 4.475 (4.474, 4.475)
> > n = 1.065 (1.035, 1.094)
> >
> > While you may not want this function, I hope this illustrates what you can
> > try with a function that you choose yourself.
> >
> > -- Tom
>
> Hi Tom,
>
> Yeah that's exactly what I was looking for, I'm just having trouble getting one type of model to work: an 'x-exponential' form where f(x) = exp[-(|z|/L)^x]. But thanks for the pointer towards 'fit'.
>
> Jack
Although I think your data is simply silly... CFTOOL worked fine for me for several fits....
Exponential:
General model Exp1:
f(x) = a*exp(b*x)
Coefficients (with 95% confidence bounds):
a = 2.545e+157 (-Inf, Inf)
b = -80.99 (-82.45, -79.54)
Goodness of fit:
SSE: 0.003883
R-square: 0.9987
Adjusted R-square: 0.9987
RMSE: 0.008994
Gaussian:
General model Gauss1:
f(x) = a1*exp(-((x-b1)/c1)^2)
Coefficients (with 95% confidence bounds):
a1 = 978 (-3410, 5366)
b1 = 4.294 (4.183, 4.405)
c1 = 0.06875 (0.04881, 0.08869)
Goodness of fit:
SSE: 0.00313
R-square: 0.9989
Adjusted R-square: 0.9989
RMSE: 0.008161
Just as some examples.... Of course, garbage in, garbage out...
Keep walking, gentleman.
|
|
|
|
