- Define your model function directly in the code.
- Use "fitnlm" with your data, model function, and initial parameter guesses to fit the non-linear model.
- Extract coefficient estimates and their p-values from the fitted model object for analysis.
- Use MATLAB's plotting functions to visualize the fit and residuals as needed.
how to get p-values from fit function
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federico drudi
il 14 Mag 2024
Commentato: federico drudi
il 23 Mag 2024
Hello, i'm trying to get p-value from a fitting performed with fit function, but i cannot find how to do so, i can only get r2 and RMSE.
Should i use some other function rather than fit?
here my code and the data, hope it will be understandable. thanks you very much in advance.
ft = fittype('(m*c*k*x)/((1-k*x)*(1+(c-1)*k*x))', ...
'dependent',{'y'}, ...
'independent',{'x'}, ...
'coefficients',{'m','c','k'});
coef = ["Xm","Cg","K","R2","RMSE"];
figure (1);
[f,gof] = fit(x,y, ft,'StartPoint',[0.2, 5, 1]);
plot(f);
hold on
plot(x,y);
figure(2);
plot(f,x,y,"residuals");
%save coefficients
format long g
c = coeffvalues(f);
R2 = gof.rsquare;
RMSE = gof.rmse;
r = horzcat(c,R2,RMSE);
coef = [coef;r];
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Pratyush
il 15 Mag 2024
Hi federico,
To obtain p-values for the coefficients from a fitting process in MATLAB, you should use the "fitnlm" function from the Statistics and Machine Learning Toolbox instead of the "fit" function from the Curve Fitting Toolbox. The "fitnlm" function fits non-linear models and provides detailed statistical analysis, including p-values for the model coefficients, which help assess their statistical significance. Here's a brief guide on how to adjust your code:
Hope this helps.
3 Commenti
Voss
il 23 Mag 2024
Use array operators ./ and .* to handle array x properly:
model = @(b,x) (b(1)*b(2)*b(3)*x)./((1-b(3)*x).*(1+(b(2)-1)*b(3)*x))
% ^^ ^^
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