![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/346953/image.png)
Binary Logistic Regression Curve
29 views (last 30 days)
Show older comments
Jonathan Moorman
on 2 Jul 2020
Answered: Aditya Patil
on 17 Aug 2020
Hello! I am trying to create a logistical regression curve for my binary data in Figure 3. Is this possible to do in MATLAB, and if so, how could it be done? My code is below? Thanks
%Figure 2 Graphing
scatter(FactoredLength, FactoredAmplitude,5,'filled')
hold on
coefficients = polyfit(FactoredLength, FactoredAmplitude, 1);
xFit = linspace(min(FactoredLength), max(FactoredLength), 1000);
yFit = polyval(coefficients , xFit);
plot(xFit, yFit, 'r-', 'LineWidth', 2);
xlabel('Factored Length')
ylabel('Probability')
grid on;
hold off
figure
% Making data binary
Probability = ((exp(log10(FactoredAmplitude)))./(1+exp(log10(FactoredAmplitude))));
yHat(Probability > app.ThreshHoldValueEditField.Value) = 1;
yHat(Probability < app.ThreshHoldValueEditField.Value) = 0;
%Figure 3 Graphing
scatter(FactoredLength,yHat)
xlabel('Factored Length')
ylabel('Probability')
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/325822/image.png)
0 Comments
Accepted Answer
Aditya Patil
on 17 Aug 2020
Use the fitglm function to fit logistic regression model to data. Check the following code for example,
% Create random data
x = rand(100, 1);
y = x > 0.5;
y(1:50) = x(1:50) > 0.3; % To avoid perfect seperation
% Fit model
mdl = fitglm(x, y, "Distribution", "binomial");
xnew = linspace(0,1,1000)'; % test data
ynew = predict(mdl, xnew);
scatter(x, y);
hold on;
plot(xnew, ynew);
This will give following output.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/346953/image.png)
0 Comments
More Answers (0)
See Also
Categories
Find more on Linear and Nonlinear Regression in Help Center and File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!