means of optimising parameters in a least squares fit. It is used in our [[software:FluoFit]], [[software:SymPhoTime]] and a variety of other products (online-Fitting, FluoLib...).
Basically it works by examinin... is regarded as state-of-the-art in least squares fitting.
===== Shortcomings =====
ML is cannot be
estimation of the freely varying parameters of a fit. It is an error estimation model of the [['Monte ... basis of the experimental data and repeating the fit for these simulated data sets. The spread of all the best fit paramter sets generated by these fits serves as a measure for the parameter errors.
Specifically, the
he support plane analysis is used for analysing [[fitting]] parameter error intervals. It works by calc... r space. For illustration let's start at the best fit parameter set, which can be regarded as a single ... s done by keeping the removed parameter fixed and fitting all the other parameters. By this we deviate ... ===== Advantages and disadvantages =====
Since [[fitting]] is used to derive a functional dependence o
otic standard errors (ASE) are used for analyzing fitting parameter error intervals. For illustration let's start at the best fit parameter set, which can be regarded as a single ... c standard errors are supported by [[software:FluoFit]] and the [[software:SymPhoTime]] software.
===
the optimization parameter for [[least squares]] fitting (for a definition see there). For a perfect fit it should be near 1. As a measure of the goodness-of-fit it is insufficient. Other methods have to be used
squares]] (or any other [[wp>Regression_analysis|fitting]] method, e.g. [[MLE]]) the residuals are the... of importance within any framework concerned with fitting, as the [[software:SymPhoTime]] software or [[software:FluoFit]].
gm for matching data ('{{wiki>Regression_analysis|fitting}}') with a parametrised model equation. A fam... s.
The least squares measure for the goodness-of-fit is
$$\chi ^2_{red}=\frac{1}{N-n_p}\sum_{i=1}^{N}