plotting multiple fitting graphs in a single graph
28 views (last 30 days)
Show older comments
Hi ,
I want to plot 2 fitting graphs on the same plot, i tried to use hold on , but it doesn't work
here is the code the one that ( with large scale of noise =5 on the same figure with large scale of noise =10)
% with large scale of noise = 10
xData = [10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10]
yData = [99 99 99 99 99 99 98 92 85 70 48 31 7 3 1 0 0 0 0 0 0]
x= xData'
y=yData'
% Set up fittype and options.
ft = fittype( 'a/(1+exp(-b*x))', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = [0.957166948242946 0.485375648722841];
% Fit model to data.
[fitresult, gof] = fit( x, y, ft, opts );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h = plot( fitresult, xData, yData );
hold on
legend( h, 'y vs. x', 'untitled fit 1', 'Location', 'NorthEast', 'Interpreter', 'none' );
% Label axes
xlabel( 'x', 'Interpreter', 'none' );
ylabel( 'y', 'Interpreter', 'none' );
grid on
saveas(gcf,'myfigure.pdf')
hold on
% with large scale of noise = 5
tData = [10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10]
bData = [99 99 99 99 99 99 99 99 98 84 47 8 1 0 0 0 0 0 0 0 0]
x= tData'
y=bData'
% Set up fittype and options.
ft = fittype( 'a/(1+exp(-b*x))', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = [0.957166948242946 0.485375648722841];
% Fit model to data.
[fitresult, gof] = fit( x, y, ft, opts );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h = plot( fitresult, tData, bData );
0 Comments
Answers (2)
Cris LaPierre
on 16 Nov 2022
By including the figure command, you are telling MATLAB to create a new figure window. Removing that and making some minor cosmetic changes, here is how I would do it.
% with large scale of noise = 10
xData = [10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10]';
yData = [99 99 99 99 99 99 98 92 85 70 48 31 7 3 1 0 0 0 0 0 0]';
% Set up fittype and options.
ft1 = fittype( 'a/(1+exp(-b*x))', 'independent', 'x', 'dependent', 'y' );
opts1 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts1.Display = 'Off';
opts1.StartPoint = [0.957166948242946 0.485375648722841];
% Fit model to data.
[fitresult1, gof1] = fit( xData, yData, ft1, opts1 );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
plot( fitresult1, xData, yData );
legend('y vs. x', 'untitled fit 1', 'Location', 'NorthEast', 'Interpreter', 'none' );
% Label axes
xlabel( 'x', 'Interpreter', 'none' );
ylabel( 'y', 'Interpreter', 'none' );
grid on
% with large scale of noise = 5
tData = [10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10]';
bData = [99 99 99 99 99 99 99 99 98 84 47 8 1 0 0 0 0 0 0 0 0]';
% Set up fittype and options.
ft2 = fittype( 'a/(1+exp(-b*x))', 'independent', 'x', 'dependent', 'y' );
opts2 = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts2.Display = 'Off';
opts2.StartPoint = [0.957166948242946 0.485375648722841];
% Fit model to data.
[fitresult2, gof2] = fit( tData, bData, ft2, opts2 );
% Plot fit with data.
hold on
plot( fitresult2, tData, bData,'c.' );
hold off
Star Strider
on 16 Nov 2022
The code works as posted.
If you want to plot both results on the same axes, that is straightforward. See the third figure —
% with large scale of noise = 10
xData = [10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10];
yData = [99 99 99 99 99 99 98 92 85 70 48 31 7 3 1 0 0 0 0 0 0];
x= xData(:);
y=yData(:);
% Set up fittype and options.
ft = fittype( 'a/(1+exp(-b*x))', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = [0.957166948242946 0.485375648722841];
% Fit model to data.
[fitresult, gof] = fit( x, y, ft, opts );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h1 = plot( fitresult, xData, yData )
hold on
legend( h1, 'y vs. x', 'untitled fit 1', 'Location', 'NorthEast', 'Interpreter', 'none' );
% Label axes
xlabel( 'x', 'Interpreter', 'none' );
ylabel( 'y', 'Interpreter', 'none' );
grid on
saveas(gcf,'myfigure.pdf')
hold on
% with large scale of noise = 5
tData = [10 9 8 7 6 5 4 3 2 1 0 -1 -2 -3 -4 -5 -6 -7 -8 -9 -10];
bData = [99 99 99 99 99 99 99 99 98 84 47 8 1 0 0 0 0 0 0 0 0];
x= tData(:);
y=bData(:);
% Set up fittype and options.
ft = fittype( 'a/(1+exp(-b*x))', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = [0.957166948242946 0.485375648722841];
% Fit model to data.
[fitresult, gof] = fit( x, y, ft, opts );
% Plot fit with data.
figure( 'Name', 'untitled fit 1' );
h2 = plot( fitresult, tData, bData )
figure
plot(h1(1).XData, h1(1).YData, 'sb', 'DisplayName','Data 1')
hold on
plot(h1(2).XData, h1(2).YData, '-r', 'DisplayName','Curve 1')
plot(h2(1).XData, h2(1).YData, 'db', 'DisplayName','Data 2')
plot(h2(2).XData, h2(2).YData, '-g', 'DisplayName','Curve 2')
hold off
grid
legend('Location','best')
Make appropriate changes to get the desired result.
.
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!