What is the good software for nonlinear curve fitting rather than Excel and Origin?
Dear Kotagiri
I use Matlab Curve Fitting Toolbox which provides graphical tools and command-line functions for fitting curves and surfaces to data.
you can do it with Matlab optimization toolbox as well,
Dear Kotagiri
I use Matlab Curve Fitting Toolbox which provides graphical tools and command-line functions for fitting curves and surfaces to data.
you can do it with Matlab optimization toolbox as well,
The right software may also depend on which measure of fit you want to use. Matlab's toolbox just mentioned certainly includes many varieties in that regard, and therefore may be a good start, as you can quickly see how the solution will vary with the objective that you have.
I second to Prof. Behrouz Ahmadi-Nedushan . Since Matlab has a Graphical user interface you can visualize how well your data is fitting in every iteration ..
You could also use QtiPlot for fitting your nonlinear data. It uses Levenberg-Marquardt method / nonlinear least squared as well as many other methods. You may download it from here http://www.qtiplot.com/
When it goes to data evaluation in science, I would recommend R.
see: http://www.r-project.org/ and try a internet research with the keywords
R nonlinear curve fitting.
I like the Matlab Curve Fitting Toolbox too - but it is more expensive.
My favorite tool for such task is gnuplot - a free software designed primarily to produce various graphs. It is also able to make nonlinear fits, using Levenberg-Marquardt method. Not only the unknown parameters but also their uncertainties are computed and reported. gnuplot is available both for Windows and Linux.
You might consider Pmetrics. This software uses nonparametric approaches to modeling, which results in more likely results than parametric approaches that use mainly means and variances, as the likelihood obtained is not constrained by the assumption of any shape of the model parameter distributions, such as Gaussian, etc. You can make any model you can describe with ordinary differential equations (analytic solutions are also available for simpler models). The real strength of this approach is that our can develop maximally precise control strategies such as multiple model (MM) dosage regimens of drugs. It also lends itself to optimal design strategies that use Bayesian classification and Bayes risk approaches, as developed by David Bayard in our lab. These are better than methods based on the Fisher information matrix, such as ED optimal, etc. It currently is available from Michael Neely in our lab free at www.lapk.org.
I recommend gnuplot, free software with a lot of features. Easy to use.
I wish there was a broad comparison between Origin, Matlab curve fitting toolbox and Mathematica somewhere.
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