Least Squares Fit Method

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... Platforms: Matlab

POLYFIT_ROOTS Least-squares polynomial fit to data.[R,K] = POLYFIT_ROOTS(X, Y, N, TOL) finds the roots R and constant K so that the polynomial P(s) = K*(s-R(1))*(s-R(2))* ... *(s-R(N)) is the best least-squares fit to the data Y at points X.Argument TOL bounds the accuracy of the fit, and if... Platforms: Matlab

Cosinor analysis uses the least squares method to fit a sine wave to a time series. Cosinor analysis is often used in the analysis of biologic time series that demonstrate predictible rhythms. This method can be used with an unequally spaced time series. Follows cosinor analysis of a time series... Platforms: Matlab

PDL::Fit::Levmar is a Perl module with Levenberg-Marquardt fit/optimization routines. Levenberg-Marquardt routines for least-squares fit to functions non-linear in fit parameters. This module provides a PDL ( PDL::PDL ) interface to the non-linear fitting library levmar (written in C). Levmar... Platforms: *nix

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

Performs linear regression using the Least Squares method. Determines the regression coefficients, the generalized correlation coefficient and the standard error of estimate. Does multivariate regression. Displays 2D and 3D plots. Platforms: Windows

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

1 Introduction This toolbox implements all the routine PLS-related/based algorithms which kind of serves as the basics in chemometrics, including PLS calibration and prediction, leave one out cross validation (LOOCV), K-fold cross validation, Monte Carlo cross validation(MCCV), double cross... Platforms: Matlab

The zip includes:(1) lsqAffineSpace: a 'low level' routine that takes a set of m-dimensional real sample data and returns the optimal-fit k-dimensional affine subspace, with some options for discarding outliers.(2) lsqPlane and lsqLine: lsqAffineSpace wrappers, that compute optimal line and... Platforms: Matlab

Functions NELSONFUN and NELSONFIT evaluate and fit (with non-linear least-squares) the Nelson-Siegel function, a popular yield-curve approximation device. Platforms: Matlab

Demonstrates a way to implement the LSM algorithm.This is done on a example from the book of Astrom and Wittenmark, Adaptive Control, 2nd ed 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

Function: Savitzky-Golay Smoothing and Differentiation Filter The Savitzky-Golay smoothing/differentiation filter (i.e., the polynomial smoothing/differentiation filter, or the least-squares smoothing/differentiation filters) optimally fit a set of data points to polynomials of different degrees.... 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

This function implements a method of using genetic algorithms to optimise the form of a polynomial, i.e. reducing the number of terms required in comparison to a least-squares fit using all possible terms, as described in the following paper:Clegg, J. et al, "The use of a genetic algorithm to... Platforms: Matlab

The code implemented a special case of the ellipsoid fitting technique proposed in the paper Least Squares Ellipsoid Specific Fitting, corresponding to case k= 4. Just put the two files in the same directory and run "testFit4". You can change the noise level to observe the fitting accuracy and... Platforms: Matlab

M-files for unimodality (or monotonically) constrained least squares regression. 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

The Unscrambler® is the complete multivariate analysis and experimental design software, equipped with powerful methods including Principal Component Analysis (PCA), Multivariate Curve Resolution (MCR), Partial Least Squares Regression (PLS-R), 3-Way PLS Regression, K-Means Clustering and SIMCA... 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