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!
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!
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