# Fit nonlinear regression model

3 次查看（过去 30 天）
Hidd_1 2023-5-19

I am trying the fit the eclosed data,
for the X-Axis:
x = 0:1:248;
I enclosed the data for the Y-axis. (Each row is a curve, there are 5 row = 5 curves)
with the following objective function: here is my code:
Data = DF.Inter_cubic(:,1:230);
Data1 = array2table(Data);
x = 1:size(Data,2);
Sig = @(p,x) p(4)./(1 + exp(-p(1).*x + p(7))) + p(5)./(1 + exp(-p(2).*x + p(8))) + p(6)./(1 + exp(-p(3).*x + p(9))) + p(10);
beta0 = [Data(1,1) Data(2,1) Data(3,1) Data(4,1) Data(5,1)];
for k = 1:size(Data1,1)
mdl(1,:) = fitnlm(Data1(k,:),Sig,beta0(1,k));
end
I am getting error message regarding using the function "fitnlm", and regarding the initial value beta0 are they the intial values of the data?

### 采纳的回答

the cyclist 2023-5-19
Answering your main question: beta0 is the initial guess at the coefficients of the fit. In your case, MATLAB is expecting a vector of length 10, because you have 10 parameters to fit.
There are lots of problems with both your MATLAB syntax, and how you are trying to fit your curves. In the code below, I have fixed the syntax problems, by
• creating separate tables for each fit
• storing the resulting models in a cell array
But, I did not try to fix the equation you are trying to fit to.
x = (1:size(Data,2))';
% Define the fitting function
Sig = @(p,x) p(4)./(1 + exp(-p(1).*x + p(7))) + p(5)./(1 + exp(-p(2).*x + p(8))) + p(6)./(1 + exp(-p(3).*x + p(9))) + p(10);
% Initial guess of coefficients
beta0 = ones(1,10);
for k = 1:size(Data,1)
% Put the data for this curve into a table
y = Data(k,:)';
tbl = table(x,y);
% Fit the model
mdl{k} = fitnlm(tbl,Sig,beta0);
% Plot the fit against the data
figure
hold on
plot(x,Data(k,:),'o')
plot(x,predict(mdl{k},x))
end
Warning: Rank deficient, rank = 4, tol = 8.082340e-13.
Warning: Rank deficient, rank = 4, tol = 8.081976e-13.
Warning: Some columns of the Jacobian are effectively zero at the solution, indicating that the model is insensitive to some of its parameters. That may be because those parameters are not present in the model, or otherwise do not affect the predicted values. It may also be due to numerical underflow in the model function, which can sometimes be avoided by choosing better initial parameter values, or by rescaling or recentering. Parameter estimates may be unreliable. Warning: Rank deficient, rank = 4, tol = 8.082340e-13.
Warning: Rank deficient, rank = 4, tol = 8.081976e-13.
Warning: Some columns of the Jacobian are effectively zero at the solution, indicating that the model is insensitive to some of its parameters. That may be because those parameters are not present in the model, or otherwise do not affect the predicted values. It may also be due to numerical underflow in the model function, which can sometimes be avoided by choosing better initial parameter values, or by rescaling or recentering. Parameter estimates may be unreliable. Warning: Rank deficient, rank = 4, tol = 8.082340e-13.
Warning: Rank deficient, rank = 4, tol = 8.081976e-13.
Warning: Some columns of the Jacobian are effectively zero at the solution, indicating that the model is insensitive to some of its parameters. That may be because those parameters are not present in the model, or otherwise do not affect the predicted values. It may also be due to numerical underflow in the model function, which can sometimes be avoided by choosing better initial parameter values, or by rescaling or recentering. Parameter estimates may be unreliable. Warning: Rank deficient, rank = 4, tol = 8.082340e-13.
Warning: Rank deficient, rank = 4, tol = 8.081976e-13.
Warning: Some columns of the Jacobian are effectively zero at the solution, indicating that the model is insensitive to some of its parameters. That may be because those parameters are not present in the model, or otherwise do not affect the predicted values. It may also be due to numerical underflow in the model function, which can sometimes be avoided by choosing better initial parameter values, or by rescaling or recentering. Parameter estimates may be unreliable. Warning: Rank deficient, rank = 4, tol = 2.680475e-12.
Warning: Rank deficient, rank = 4, tol = 1.142958e-12.
Warning: Rank deficient, rank = 4, tol = 8.476406e-13.
Warning: Rank deficient, rank = 4, tol = 8.122245e-13.
Warning: Some columns of the Jacobian are effectively zero at the solution, indicating that the model is insensitive to some of its parameters. That may be because those parameters are not present in the model, or otherwise do not affect the predicted values. It may also be due to numerical underflow in the model function, which can sometimes be avoided by choosing better initial parameter values, or by rescaling or recentering. Parameter estimates may be unreliable. Hidd_1 2023-5-19
Thanks a lot!

### 类别

Help CenterFile Exchange 中查找有关 Linear and Nonlinear Regression 的更多信息

R2020a

### Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!