Cannot declare a costum fit type using a function file

Hello everyone

i have some experimental data i would like to try to fit a theoretical model to. specifically, i have a data-curve(x,y) where i would like to see if i can fit a theorical datacurve.

i have made a function file which calculates the theoritcal curve and would like to use it to make a costum fit type. to do that, i have tried to follow the guidelines given here: GUIDE . The guide also uses a function file to generate a theoretical curve(piecewiseLine) and fit it to data so i believe i should be able to use it.

my situation is, however, a little bit different. My measured data is on arbitrary scale and needs to be scaled down by a multiplier. Furthermore i would like add a constant to address experimental artifacts*. so the fit-type becomes (theoretical curve)*k+c. notice that it is the multiplier and constant i want to use for fitting - i will not change the parameters for the theoretical curve (yet).

here is the code for my function file:

function P = ElipCurve(R,L)
% Function for calculating the scattering from an ellipsoid particle
%   R is the semis axises, L is the length of the particle

Q = 0.001:0.001:0.5; % Qrange

funPhi = @(x1) R.*(sin(x1).^2+(L/R)^2.*cos(x1).^2).^0.5;
funTot = @(x) (3.*(sin(Q.*funPhi(x))-Q.*funPhi(x).*cos(Q.*funPhi(x)))./(Q.^3.*funPhi(x).^3)).^2.*sin(x);

P = integral(funTot,0,pi/2,'ArrayValued',true);
end

and here is the code for defining the fittype

R = 30; % Semi-axes in ångstrøm
L = 120; % length in ångstrøm
File = 1; % the specific observation you want to fit the model to
xdata = DataFiles{1,File}.data(:,1);
ydata = DataFiles{1,File}.data(:,2);

F = fittype(ElipCurve(R,L).*k+c,'independent',{'k','c'});

i get the error: Undefined function or variable 'c'.

I really don't understand the error message - i define "c" as an independent variable that can be varied to improve? can anyone suggest other methods or see any mistakes in my code?

*sounds shady i know, but i will report the constant. it is only thought of a measure of discrepancy between the perfect model and reality - if the constant is larger than the uncertainty i will not use the fit.