Timeline for Finding NonlinearModelFit of multiple data sets with the same parameters and in two dimensions
Current License: CC BY-SA 3.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 28, 2016 at 12:51 | vote | accept | maythemoonshine | ||
| Apr 28, 2016 at 9:11 | vote | accept | maythemoonshine | ||
| Apr 28, 2016 at 10:05 | |||||
| Apr 22, 2016 at 19:53 | history | edited | maythemoonshine | CC BY-SA 3.0 | added 238 characters in body |
| Apr 21, 2016 at 19:05 | answer | added | JimB | timeline score: 5 | |
| Apr 21, 2016 at 16:46 | comment | added | JimB | It's not sounding like you have "two dimensions" as in a two-dimensional response but rather from your comments it sounds like you have a single response variable with two predictors (x and t). The mix of continuous and discrete isn't problematic on the predictor side of the equation. Just concatenate the 3x10000 matrices with each row representing {x,t,y} where y is the response variable. (And, of course, initially testing this with just a subset of the data). | |
| Apr 21, 2016 at 16:19 | comment | added | maythemoonshine | Anyway, my data are from a harmonic oscillator which reacts to an external non-trigonometrical driving force | |
| Apr 21, 2016 at 16:12 | comment | added | maythemoonshine | I'm afraid that I cannot share the fitting function, and the datasets are rather large (~ 2x10000 matrixes), but I think that a good approximation would be a sine function dependent of time which suffers a phase shift if you change x (distance)... or maybe sine is too simple, I would have no problem fitting that, if I didn't have to use f. | |
| Apr 21, 2016 at 15:44 | comment | added | JimB | The answer is a definite Yes (and it would depend on the exact model if NonlinearModelFit or use of LogLikelihood would be the best approach). Are you able to share either the complete model (including the error structure) and/or some sample data? | |
| Apr 21, 2016 at 15:29 | review | First posts | |||
| Apr 21, 2016 at 15:35 | |||||
| Apr 21, 2016 at 15:28 | history | asked | maythemoonshine | CC BY-SA 3.0 |