I read that a new feature for Igor Pro 6 is that "all-at-once fit functions can now implement multivariate fit functions." I understand how this is to be done with 1 dimensional data, but have been able to find instructions on how this should be implemented for multiple dimensions. Any help would be appreciated!
[quote=Rischaard]I read that a new feature for Igor Pro 6 is that "all-at-once fit functions can now implement multivariate fit functions." I understand how this is to be done with 1 dimensional data, but have been able to find instructions on how this should be implemented for multiple dimensions. Any help would be appreciated![/quote]
Hm.... it looks like the description is missing from the documentation!
The format is analogous to a regular multivariate fit function. With a univariate fit function you write a function like this:
Function myfunc(w, x) : FitFunc
Wave w
Variablex
...
end
To make it multivariate, you just add more independent variable inputs:
Function myfunc(w, x1, x2) : FitFunc
Wave w
Variable x1, x2
...
end
Similarly, the extension for all-at-once fitting functions is to simply add more independent variable inputs:
Function myAllAtOnceFunc(pw, xw1, xw2) : FitFunc
Wave pw
wave xw1, xw2
...
end
Hm.... it looks like the description is missing from the documentation!
The format is analogous to a regular multivariate fit function. With a univariate fit function you write a function like this:
To make it multivariate, you just add more independent variable inputs:
Similarly, the extension for all-at-once fitting functions is to simply add more independent variable inputs:
John Weeks
WaveMetrics, Inc.
support@wavemetrics.com
July 6, 2010 at 03:16 pm - Permalink