plotting multiple fitting graphs in a single graph

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]

xData = 1×21

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]

yData = 1×21

99 99 99 99 99 99 98 92 85 70 48 31 7 3 1 0 0 0 0 0 0

x= xData'

x = 21×1

10 9 8 7 6 5 4 3 2 1

y=yData'

y = 21×1

99 99 99 99 99 99 98 92 85 70

% 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]

tData = 1×21

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]

bData = 1×21

99 99 99 99 99 99 99 99 98 84 47 8 1 0 0 0 0 0 0 0 0

x= tData'

x = 21×1

10 9 8 7 6 5 4 3 2 1

y=bData'

y = 21×1

99 99 99 99 99 99 99 99 98 84

% 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 );

Answer 1

[Cris LaPierre]

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

[Hagar Hendy]

Thank you so much

Answer 2

[Star Strider]

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 )

h1 = 
  2×1 Line array:

  Line    (data)
  Line    (fitted curve)

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 )

h2 = 
  2×1 Line array:

  Line    (data)
  Line    (fitted curve)

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.

.

[Hagar Hendy]

thank you

[Star Strider]

My pleasure!