I have multiple sets of data (between 3 and 6 depending on the cases) dependent of space, time, and some parameters. The data are the response of a harmonic oscillator under a non-trigonometrical driving force. I want to fit them using a function dependent of space, time, and those parameters, which has the form:
All of the parameters BUT ONE must be the same for all the data sets, while time is a continuous variable for all data sets, and space is a discrete variable, constant for each set. I already have minimized the chi squared, which is in the form:
but the result is not satisfying and moreover it gives no Parameter errors.
The parameter which changes for each data set is the phase, which I insert in the formula by writing
f[x, t+phase, par1, par2]
Is there a way to make a non linear model fit of my data all in one go? I know similar questions have been asked, but my dataset has the further complication of depending on two variables, one of which is discrete, and adapting those answers to my problems is beyond my capabilities.