## Improving chose of guesses in my fit

Hi everyone,

I do some fit on my data, and my guesses is no good enough.

Here an example of plotting my data:

And this is the code of the function that fit my data:

%fit function

function [Xmax,beta] = fit_function(x,y,peaks,t_peaks,peak_num,check_fit)

% ymax calculated analytically with Wolfram Mathematica

ymax = @(b) (2-b(2)/b(1))*(2*b(1)/b(2)-1)^(-b(2)/2/b(1));

modelfun = @(b,x) b(3)/ymax(b)*exp((x-b(4))*(b(1)-b(2))).*sech(b(1)*(x-b(4)));

bguess=[60, 15, peaks(peak_num), x(t_peaks(peak_num))];

%bguesses is [alpha, beta, amplitude, x offset]

%alpha and beta controlling of the slope of the peak, each one of them control in one slope side.

beta = nlinfit(x, y, modelfun, bguess);

Xmax=(log(-1+(2*beta(1)/beta(2)))/(2*beta(1)))+beta(4);

%check fit:

if check_fit==1

fittedCurve = modelfun(beta, x);

hold on;

plot(x, fittedCurve, ‘m’);

legend(‘Original Data’,’Fitted Curve’);

else

end

end

In this fit, I have 4 guesses. The first two of them(call alpha and beta), chosing as a constant number, according to my attempts to see what would be suitable. The other two of them is depend on the data and according to the data, the code chose the appropriate guesses. (actually is the amplitude and x offset)

So, if you look on the code, you see that 60 and 15 is the constant guesses, and the "peaks(peak_num)" and "x(t_peaks(peak_num))", is the guesses that variable according to the maximum point of the data.

The problem is that in some cases,alpha and beta, the two that are constant, sometimes suitable and sometime unsuitable.

I want to insert guesses, that will be change according to the data, exactlly like the last two guesses of the amplitude and x offset. I thinking about derivative of the peaks or something like this, but I managed to mess with it..

do you have idae for good variable guesses or how to do something that will be appropriate to any try of fitting my data?

thank you all (:Hi everyone,

I do some fit on my data, and my guesses is no good enough.

Here an example of plotting my data:

And this is the code of the function that fit my data:

%fit function

function [Xmax,beta] = fit_function(x,y,peaks,t_peaks,peak_num,check_fit)

% ymax calculated analytically with Wolfram Mathematica

ymax = @(b) (2-b(2)/b(1))*(2*b(1)/b(2)-1)^(-b(2)/2/b(1));

modelfun = @(b,x) b(3)/ymax(b)*exp((x-b(4))*(b(1)-b(2))).*sech(b(1)*(x-b(4)));

bguess=[60, 15, peaks(peak_num), x(t_peaks(peak_num))];

%bguesses is [alpha, beta, amplitude, x offset]

%alpha and beta controlling of the slope of the peak, each one of them control in one slope side.

beta = nlinfit(x, y, modelfun, bguess);

Xmax=(log(-1+(2*beta(1)/beta(2)))/(2*beta(1)))+beta(4);

%check fit:

if check_fit==1

fittedCurve = modelfun(beta, x);

hold on;

plot(x, fittedCurve, ‘m’);

legend(‘Original Data’,’Fitted Curve’);

else

end

end

In this fit, I have 4 guesses. The first two of them(call alpha and beta), chosing as a constant number, according to my attempts to see what would be suitable. The other two of them is depend on the data and according to the data, the code chose the appropriate guesses. (actually is the amplitude and x offset)

So, if you look on the code, you see that 60 and 15 is the constant guesses, and the "peaks(peak_num)" and "x(t_peaks(peak_num))", is the guesses that variable according to the maximum point of the data.

The problem is that in some cases,alpha and beta, the two that are constant, sometimes suitable and sometime unsuitable.

I want to insert guesses, that will be change according to the data, exactlly like the last two guesses of the amplitude and x offset. I thinking about derivative of the peaks or something like this, but I managed to mess with it..

do you have idae for good variable guesses or how to do something that will be appropriate to any try of fitting my data?

thank you all (: Hi everyone,

I do some fit on my data, and my guesses is no good enough.

Here an example of plotting my data:

And this is the code of the function that fit my data:

%fit function

function [Xmax,beta] = fit_function(x,y,peaks,t_peaks,peak_num,check_fit)

% ymax calculated analytically with Wolfram Mathematica

ymax = @(b) (2-b(2)/b(1))*(2*b(1)/b(2)-1)^(-b(2)/2/b(1));

modelfun = @(b,x) b(3)/ymax(b)*exp((x-b(4))*(b(1)-b(2))).*sech(b(1)*(x-b(4)));

bguess=[60, 15, peaks(peak_num), x(t_peaks(peak_num))];

%bguesses is [alpha, beta, amplitude, x offset]

%alpha and beta controlling of the slope of the peak, each one of them control in one slope side.

beta = nlinfit(x, y, modelfun, bguess);

Xmax=(log(-1+(2*beta(1)/beta(2)))/(2*beta(1)))+beta(4);

%check fit:

if check_fit==1

fittedCurve = modelfun(beta, x);

hold on;

plot(x, fittedCurve, ‘m’);

legend(‘Original Data’,’Fitted Curve’);

else

end

end

In this fit, I have 4 guesses. The first two of them(call alpha and beta), chosing as a constant number, according to my attempts to see what would be suitable. The other two of them is depend on the data and according to the data, the code chose the appropriate guesses. (actually is the amplitude and x offset)

So, if you look on the code, you see that 60 and 15 is the constant guesses, and the "peaks(peak_num)" and "x(t_peaks(peak_num))", is the guesses that variable according to the maximum point of the data.

The problem is that in some cases,alpha and beta, the two that are constant, sometimes suitable and sometime unsuitable.

I want to insert guesses, that will be change according to the data, exactlly like the last two guesses of the amplitude and x offset. I thinking about derivative of the peaks or something like this, but I managed to mess with it..

do you have idae for good variable guesses or how to do something that will be appropriate to any try of fitting my data?

thank you all (: curve fitting, matlab MATLAB Answers — New Questions