You can either use New Fit Function dialog from withinĬurve Fitting dialog or you can write it manually. Here is an example of a single exponent in general form and its variant suitable for fitting: regularĬalculations in both cases are identical.īefore proceeding to write custom fitting functions familiarize yourself with the concept of function in general here and in Igor manual.ĭevising custom fitting functions is easier than it may seem. Such functions must take two arguments: a wave containing all parameters for calculation and the value of independent variable. User fitting functions are similar to all other functions except that they must adhere to specific parameter format to make them useable in fitting. To do this you need to write a user fitting function. It is also not possible to use multiple built-in functions, such as kinetics with with liner drift. While Igor allows imposing numerical constraints on coefficients of built-in functions and even setting them tem to a fixed value, you cannot change how parameters are used in calculations.
Such restrictions are known as constraints. In some cases it also helps to limit the range of acceptable values of parameters, such as limiting kinetic rates to positive values. Thus, in kinetic analysis start with the minimum number of exponents and advance to higher order if you are sure you cannot describe data adequately with the selected order. Conventional wisdom goes that one can fit any process with sufficient number of exponents, typically over four. It is always a good idea to use the minimum number of parameters needed to describe your process adequately.
At this moment most data analyzed in this group involve only one meaningful independent variable – time –although other situations are possible, for example when both time and concentration change across a series.įrom visual, qualitative evaluation of data you need to estimate how many processes are involved and how many parameters you need to describe them. At the very least it is necessary to know general type of dependence (exponential, linear, titration, polynomial, mixed) and the number of independent variables.
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How to choose correct function?Īs described here, you must have reasonable understanding of the process you are analyzing. Such flexibility, however, takes its toll on accuracy of fitting and often in efforts it takes to converge such generic fit. Generic functions are defined in the most common form with maximum number of unrestricted parameters, which makes them useable with a broad range of processes. These typically include linear, polynomial, exponential and some other common dependencies. Any data fitting must have a function that describes the process you are analyzing.Īll fitting packages provide a set of generic built-in functions.
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