Nonlinear Least Squares Fitting

The function LMFsolve.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago. LMFsolve is its essentially shortened version...

The function The LMFnlsq.m serves for finding optimal solution of an overdetermined system of nonlinear equations in the least-squares sense. The standard Levenberg- Marquardt algorithm was modified by Fletcher and coded in FORTRAN many years ago (see the Reference). This version of LMFnlsq is...

FitChiTool provides an interface similar to the Curve Fitting Toolbox to the fitChiSquare function.This allows you to perform fits using chi-square when measurement errors are known on linear or nonlinear models. It allows allows you to perform fits with multiple independent (more than one x)...


Platforms: Matlab

OLS - Orthogonal Least Squares: Proposed by T. Blumensath, M. E. DaviesStOLS - Stagewise OLS: Combining StOMP ideas with OLSROLS - Regularized OLS: Combining ROMP ideas with OLS


Platforms: Matlab

This example solves a complex curve fitting problem that involves parameter pooling and stratification using a nonlinear least-squares approach. This example also takes advantage of some new language features with MATLAB 7. * Anonymous functions * Nested functions


Platforms: Matlab

BestCurFit calculates the parameters of 12 curves using the method of Linear Least Squares, fitting each curve to user data. The nonlinear equations are linearized. The obtained parameters are used for following optimization procedure using Simplex and Gauss-Newton algorithm. If it does not...


Platforms: Windows

The DynaFit application was developed to perform nonlinear least-squares regression of chemical kinetic, enzyme kinetic, or ligand-receptor binding data. The experimental data can be either initial reaction velocities in dependence on the concentration of varied species (e.g., inhibitor...


Platforms: Windows

Motofit co-refines Neutron and X-ray reflectometry data, using the Abeles matrix / Parratt recursion and least squares fitting (Genetic algorithm or Levenberg Marquardt). It works in the IGOR Pro environment (TM Wavemetrics).


Platforms: Windows, Mac, Linux

zpkfit uses the nonlinear least-squares fitter (lsqnonlin) to fit an analytic model of any (fixed) number of poles and zeroes to numerical (presumably measured) frequency response data. It takes as arguments a frequency vector, a frequency response vector, and vectors of the initial guesses of...


Platforms: Matlab

DMFitter ActiveX control allows you to add sophisticated regression analysis tools to your software in a few lines of code. Features include: both linear and nonlinear (Levenberg-Marquardt) least squares curve fitting algorithms, arbitrary user models (defined analytically or by string...


Platforms: Windows

Statistics::LineFit module least squares line fit, weighted or unweighted. SYNOPSIS use Statistics::LineFit; $lineFit = Statistics::LineFit->new(); $lineFit->setData (@xValues, @yValues) or die "Invalid data"; ($intercept, $slope) = $lineFit->coefficients(); defined $intercept or die...


Platforms: *nix

Statistics::OLS is a Perl module to perform ordinary least squares and associated statistics. SYNOPSIS use Statistics::OLS; my $ls = Statistics::OLS->new(); $ls->setData (@xydataset) or die( $ls->error() ); $ls->setData (@xdataset, @ydataset); $ls->regress(); my ($intercept, $slope)...


Platforms: *nix

The Kalman filter can be interpreted as a feedback approach to minimize the least equare error. It can be applied to solve a nonlinear least square optimization problem. This function provides a way using the unscented Kalman filter to solve nonlinear least square optimization problems. Three...


Platforms: Matlab

Statistics::GaussHelmert is a general weighted least squares estimation module. SYNOPSIS use Statistics::GaussHelmert; # create an empty model my $estimation = new Statistics::GaussHelmert; # setup the model given observations $y, covariance matrices # $Sigma_yy, an initial guess $b0...


Platforms: *nix

This is a wrapper function to solve optimization problems (using FMINCON) of the form:min w.r.t. X of CostFunc(X) = beta*(W_ls*F(X)) + (1 - beta)*(max(W_mm*F(X)))subject to: A*X


Platforms: Matlab

For a system having multiple inputs x and outputs y, the partial coherence is the coherence computed between any individual input and the output when the effect of all other inputs is removed from the output by a linear least squares prediction. This coherence obeys the usual inequality, and will...


Platforms: Matlab

Inputs: 1. a 1 dimensional array of drug concentrations 2. the corresponding m x n array of responses Algorithm: generate a set of initial coefficients including the Hill coefficient fit the data to the 4 parameter dose-response curve using nonlinear least squares Output: a matrix of the 4...


Platforms: Matlab

The design matrix of a GPS network is established for any kind of least squares adjustment, namely;-free (trace minimum),-minimum constrained,-over-determined.The user can adapt easily dmgps to the adjustment problem of a GPS network whose baselines are taken as observations. Moreover, it can be...


Platforms: Matlab

Sake is a PHP implementation of Polynomial Least Squares Regression. The algorithm takes data points as input and returns the resulting polynomial. Graphs can be made using any plotting package. Sake provides the polynomial and will compute as many point


Platforms: Windows, Mac, Linux

Tidal Analysis Program in PYthon (TAPPY) uses the least squares optimization function from scipy to perform a harmonic analysis (calculate amplitude and phases of a set of sine waves) of a hourly time series of water level values.


Platforms: Windows, Mac, Linux