Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks.Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks. Thank you for taking the time to help. I will do my best to be clear and concise.
I have 3 sets of data (atttached), each with x and y data – not necesarilly the same length.
Each y(x) function can be normalized into p(t) using 4 parameters:
A – a constant that is extracted from each individual y(x), ‘A’ looks like [A1, A2, A3]
B – a fitting parameter unique to each y(x)
C – a fitting parameter applied to all y(x)
D – a constraint variable dependent on A and B applied to all y(x)
So the set looks something like:
I would like to use lsqcurvefit to fit my data to my function p(t) and output the unique (C and D) values.
I have a final constraint to increase the accuracy of lsqcurvefit: , where D is another global constant like C.
Where I’m at now, I want to adjust C and D to fit my function p(t) to y(x), but I don’t know how to also include the unique A values for each y(x).
I’ve pasted my current code below where I am able to get an individual fit for each y(x) but without consistent C and D parameters.
%% Data analysis
Results = NaN(3,2); % Result [C,D] for each individual fitting
for t = 1:3
%TL is summary matrix of all y(x) for the 3 experiments, NaN fills in
%where some y(x) are shorter than others.
x = TL(~isnan(TL(:,2*t-1)),2*t-1); %extracts x values from TL
y = TL(~isnan(TL(:,2*t)),2*t); %extracts y values from TL

%P = parameters: P(1) = C | P(2) = D
%fun below is the normalization for p(t)
fun = @(P,x)A(t)*(1-1/(2*(1-P(1))))*(1.304*exp(-(P(2)*x/(r/1000)^2).^0.5)-0.304*exp(-0.254*P(2)*x/(r/1000)^2))+1/(2*(1-P(1)));
P0 = [0.4, 1*10^-9]; %Initial parameters
lb = [0.1, 1*10^-10]; %Lower bound
ub = [0.5, 1*10^-8]; %Upper bound

Results(t,:) = lsqcurvefit(fun,P0,x,y,lb,ub); %Result consolidation for individual fitting

subplot(3,1,t)
plot(x,y,’ko’,x,fun(Results(t,:),x),’b-‘)
end
From looking at other posts on using lsqcurvefit with multiple datasets, I think my obstacle has come down to whether I should (1) run lsqcurvefit on all y(x) and somehow input the unique A constants or (2) run lsqcurvefit on individual y(x) and somehow correlate C and D to match between each fitting.
I hope this is presented clearly, looking forward to your thoughts and discussion – Thanks. lsqcurvefit, curve fitting, optimization MATLAB Answers — New Questions

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Hello! i have been to run this model fitting code but keeps on getting these error messages as:
FUN must have two input arguments.
userFcn_ME = initEvalErrorHandler(userFcn_ME,funfcn_x_xdata{3}, …
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);
Find attached, the code,, Please help

function dydt = sir_model(t, y, beta, gamma)
S = y(1);
I = y(2);
R = y(3);
N = S + I + R; % Total population

dSdt = -beta * S * I / N;
dIdt = beta * S * I / N – gamma * I;
dRdt = gamma * I;

dydt = [dSdt; dIdt; dRdt];
end

function error = model_error(params, time, observed_I, y0)
beta = params(1);
gamma = params(2);

% Solve the differential equations
[~, y] = ode45(@(t, y) sir_model(t, y, beta, gamma), time, y0);

% Extract the infectious data from the solution
model_I = y(:, 2);

% Compute the error as the difference between model and observed data
error = model_I – observed_I’;
end

% Define the time vector and observed data
time = [0, 1, 2, 3, 4, 5]; % Example time points
observed_I = [10, 15, 20, 25, 30, 35]; % Example observed infectious data

% Initial guess for parameters
beta_guess = 0.3;
gamma_guess = 0.1;
params0 = [beta_guess, gamma_guess];

% Initial conditions
S0 = 1000; % Initial number of susceptible individuals
I0 = observed_I(1); % Initial number of infectious individuals
R0 = 0; % Initial number of recovered individuals
y0 = [S0; I0; R0];

% Define the objective function for fitting
obj_fun = @(params) model_error(params, time, observed_I, y0);

% Perform the curve fitting
options = optimoptions(‘lsqcurvefit’, ‘Display’, ‘off’);
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);

% Display the fitted parameters
beta_fit = params_fit(1);
gamma_fit = params_fit(2);
disp([‘Fitted beta: ‘, num2str(beta_fit)]);
disp([‘Fitted gamma: ‘, num2str(gamma_fit)]);Hello! i have been to run this model fitting code but keeps on getting these error messages as:
FUN must have two input arguments.
userFcn_ME = initEvalErrorHandler(userFcn_ME,funfcn_x_xdata{3}, …
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);
Find attached, the code,, Please help

function dydt = sir_model(t, y, beta, gamma)
S = y(1);
I = y(2);
R = y(3);
N = S + I + R; % Total population

dSdt = -beta * S * I / N;
dIdt = beta * S * I / N – gamma * I;
dRdt = gamma * I;

dydt = [dSdt; dIdt; dRdt];
end

function error = model_error(params, time, observed_I, y0)
beta = params(1);
gamma = params(2);

% Solve the differential equations
[~, y] = ode45(@(t, y) sir_model(t, y, beta, gamma), time, y0);

% Extract the infectious data from the solution
model_I = y(:, 2);

% Compute the error as the difference between model and observed data
error = model_I – observed_I’;
end

% Define the time vector and observed data
time = [0, 1, 2, 3, 4, 5]; % Example time points
observed_I = [10, 15, 20, 25, 30, 35]; % Example observed infectious data

% Initial guess for parameters
beta_guess = 0.3;
gamma_guess = 0.1;
params0 = [beta_guess, gamma_guess];

% Initial conditions
S0 = 1000; % Initial number of susceptible individuals
I0 = observed_I(1); % Initial number of infectious individuals
R0 = 0; % Initial number of recovered individuals
y0 = [S0; I0; R0];

% Define the objective function for fitting
obj_fun = @(params) model_error(params, time, observed_I, y0);

% Perform the curve fitting
options = optimoptions(‘lsqcurvefit’, ‘Display’, ‘off’);
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);

% Display the fitted parameters
beta_fit = params_fit(1);
gamma_fit = params_fit(2);
disp([‘Fitted beta: ‘, num2str(beta_fit)]);
disp([‘Fitted gamma: ‘, num2str(gamma_fit)]); Hello! i have been to run this model fitting code but keeps on getting these error messages as:
FUN must have two input arguments.
userFcn_ME = initEvalErrorHandler(userFcn_ME,funfcn_x_xdata{3}, …
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);
Find attached, the code,, Please help

function dydt = sir_model(t, y, beta, gamma)
S = y(1);
I = y(2);
R = y(3);
N = S + I + R; % Total population

dSdt = -beta * S * I / N;
dIdt = beta * S * I / N – gamma * I;
dRdt = gamma * I;

dydt = [dSdt; dIdt; dRdt];
end

function error = model_error(params, time, observed_I, y0)
beta = params(1);
gamma = params(2);

% Solve the differential equations
[~, y] = ode45(@(t, y) sir_model(t, y, beta, gamma), time, y0);

% Extract the infectious data from the solution
model_I = y(:, 2);

% Compute the error as the difference between model and observed data
error = model_I – observed_I’;
end

% Define the time vector and observed data
time = [0, 1, 2, 3, 4, 5]; % Example time points
observed_I = [10, 15, 20, 25, 30, 35]; % Example observed infectious data

% Initial guess for parameters
beta_guess = 0.3;
gamma_guess = 0.1;
params0 = [beta_guess, gamma_guess];

% Initial conditions
S0 = 1000; % Initial number of susceptible individuals
I0 = observed_I(1); % Initial number of infectious individuals
R0 = 0; % Initial number of recovered individuals
y0 = [S0; I0; R0];

% Define the objective function for fitting
obj_fun = @(params) model_error(params, time, observed_I, y0);

% Perform the curve fitting
options = optimoptions(‘lsqcurvefit’, ‘Display’, ‘off’);
[params_fit, ~] = lsqcurvefit(obj_fun, params0, time, observed_I, [], [], options);

% Display the fitted parameters
beta_fit = params_fit(1);
gamma_fit = params_fit(2);
disp([‘Fitted beta: ‘, num2str(beta_fit)]);
disp([‘Fitted gamma: ‘, num2str(gamma_fit)]); model fitting MATLAB Answers — New Questions

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