Fit model with 3 independent variables and many parameters to data
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is it possible to fit the parameters of a non linear model with more than 2 independent variable (let's say 3 or 4 for example) to data???
here attached is my code, where the coefficients are a b c d and the independent variables are x,q,w
function [beta]=fitgen(Xtest,Ytest,Wtest,Ztest,p,p_low,p_up)
mdl=@(a,b,c,d,w,q,x) zfit(a,b,c,d,w,q,x);
algo1='Trust-Region';
fit_opt = fitoptions('Method','NonlinearLeastSquares',... 'Lower',p_low,'Upper',p_up,... 'Robust','on',... 'Normalize','off',... 'Algorithm',algo1); fit_typ = fittype(mdl,'option',fit_opt);
[Yfitt,gof,output]=fit([Xtest,Ytest,Wtest],Ztest,fit_typ,'Start',p)
%%where the function zfit is (I used a linear model just for sake of simplicity, but the final purpose is to fit a non linear model):
function [z]=zfit(a,b,c,d,w,q,x)
[x,q,w]=meshgrid(x,q,w);
z=a*x+b*q+c*w+d;
end
thank you very much in advance
0 Comments
Accepted Answer
the cyclist
on 11 Feb 2013
Yes, if you have the Statistics Toolbox you can use the nlinfit() function to do this. Here is a very simple example.
function [] = nlinfitExample()
% Here is an example of using nlinfit(). For simplicity, none of
% of the fitted parameters are actually nonlinear!
% Define the data to be fit
x=(0:1:10)'; % Explanatory variable
y = 5 + 3*x + 7*x.^2; % Response variable (if response were perfect)
y = y + 2*randn((size(x)));% Add some noise to response variable
% Define function that will be used to fit data
% (F is a vector of fitting parameters)
f = @(F,x) F(1) + F(2).*x + F(3).*x.^2;
F_fitted = nlinfit(x,y,f,[1 1 1]);
% Display fitted coefficients
disp(['F = ',num2str(F_fitted)])
% Plot the data and fit
figure
plot(x,y,'*',x,f(F_fitted,x),'g');
legend('data','fit')
end
In my case, I just have one explanatory variable vector, x, but that could have been a matrix with multiple variables in each column.
Hope that helps.
2 Comments
nony lew
on 16 Apr 2018
I have used this answer of yours to fit my datas. And it went perfectly for me too, thank you.
x=[4.3...]; y=[11987.36...];
f = @(F,x) F(1)*x +F(2)+F(3)*(sin(F(4)*x+F(5))); F_fitted = nlinfit(x,y,f,[1 1 1 1 1]); % Display fitted coefficients disp(['F = ',num2str(F_fitted)]) % Plot the data and fit figure plot(x,y,'*',x,f(F_fitted,x),'r'); legend('data','fit')
I have another question:
How can I modify this code to apply to structures?
if in my case:
x = xdata (k) .part is a structure <1x200 struct>
and
how can i change the code to fetch every dataset?
Thanks
Thi Na Le
on 25 Mar 2020
More Answers (2)
Manuel
on 11 Feb 2013
1 Comment
the cyclist
on 11 Feb 2013
I suggest you ask this as a new question. Questions buried within other questions rarely get attention.
NgocDuy Nguyen
on 7 Oct 2021
Hello everyone,
I have tried to fit a function f(x,y,z) to determine F1, F2, F3, ...., F9 coefficients.
My code is as follows:
function [] = nlinfitExample()
[x1,x2,x3] = meshgrid(362:1,362:1,362:1);
[ff]=meshgrid(362:1);
f=[ff(x1,x2,x3)];
[F_fitted] = meshgrid(9:1);
[F] = meshgrid(9:1);
x = x1;
y = x2;
z = x3;
% Define function that will be used to fit data
% (F is a vector of fitting parameters)
f = @(F,x,y,z) F(1)*exp(-((x-20)^2+(y-20)^2)/12)+F(2)*exp(-((x-38)^2+(y-50)^2)/43)+F(3)*exp(-((x-350)^2+(y-82)^2)/13)+F(4)*exp(-((x-58)^2+(y-82)^2)/24)+F(5)*exp(-((x-70)^2+(y-110)^2)/244)+F(6)+(0.0000532793*x^2-5-F(7)*(y-x)/(y+x))*ln(z-F(8)*((-1)^x+(-1)^y))+F(9)*0.0000532793*x^2+0.0000177598*x^2*ln(x+y)-0.022930657*x;
%F_fitted = nlinfit(x,y,z,f,[F(1), F(2), F(3), F(4), F(5), F(6), F(7), F(8), F(9)]);
F_fitted = nlinfit(x,y,z,f, [1 1 1 1 1 1 1 1 1]);
% Display fitted coefficients
disp(['F = ',num2str(F_fitted)])
end
But it gives the error as follows:
Error:
Requires a vector second input argument.
Error in nlinfitExample (line 17)
F_fitted = nlinfit(x,y,z,f, [1 1 1 1 1 1 1 1 1]);
Could you please help me to solve this problem?
Thank you.
1 Comment
the cyclist
on 11 Oct 2021
Please open this as a new question, not making it an "answer" on a 8-year-old question
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