Least Squares Fit Routine (LuSiFeR) is a program for least squares data analysis. Least Squares Fit Routine (LuSiFeR) is a software application that was designed for least squares data analysis. The first package is in the form of a script for MATLAB, while the second is a standalone program written in C++, with extended capabilities.Both packages perform least-squares regression analysis on a user-supplied dataset, and calculate appropriate fitted parameters (with uncertainties) and an associated X2 value.

QuickFit Suite is a 32 bit software product that consists of 2 programs - 1. QuickFit performs a polynomial least squares fit (up to ninth order) on user-entered data.2. QuickMVR performs a multivariable regression on user entered-data.. Home..

variogramfit performs a least squares fit of various theoretical variograms to an experimental, isotropic variogram. The user can choose between various bounded (e.g. spherical) and unbounded (e.g. exponential) models. A nugget variance can be modelled as well, but higher nested models are not supported. The function works best with the function fminsearchbnd available on the FEX. You should download it from the File Exchange (File ID: #8277). If you don't have fminsearchbnd, variogramfit uses fminsearch.

% Based on - A simple method for fitting of bounding rectangle to closed regions - D. Chaudhuri a , A. Samal b. % fit_rectangle - Function provides a least squares fit to the % given boundary points of an object of unknown shape. % % Inputs - Boundary elements that must be a Nx2 array. (atleast 3 required) % Output - % Bounding_points(4x2): % Function will return a struct consisting of the bounding points % equation_of_diagonals(2x2): % Function will return the equation of the diagonal in the form % y = mx +c (giving back m and c foreach diagonal).

• Platform: Matlab, Scripts
• Publisher: Onkar Raut
• Date: 27-04-2013
• Size: 10 KB

Among the considerations in the use of analysis of regression, outliers or bad values can seriously disturb the least-squares fit. They falls far from the line implied by the rest of the data. If these points are really outliers, then the estimate of the intercept may be incorrect and the residual mean square may be an inflated estimate of the true variance. There are methods for scaled residuals useful in finding observations that are outliers. One of them is the externally studentized residual, usually called R-student.

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

• Platform: Matlab, Scripts
• Publisher: Angshul Majumdar
• Date: 07-05-2013
• Size: 10 KB

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that a) global convergence is assured b) a solution is found even for a vertical line. The complete uncertainty matrix (i.e. variances AND covariance of the fitting parameters) is determined. For non-vertical straight lines the usual parameters (slope/y-axis intersect.

• Platform: Matlab, Scripts
• Publisher: Mathias Anton
• Date: 13-02-2013
• Size: 10 KB

Constructs discrete least-squares rational approximations to data using the full-Newton algorithm for solving separable non-linear least-squares problems that was developed in: Carlos F. Borges, A Full-Newton Approach to Separable Nonlinear Least Squares Problems and its Application to Discrete Least Squares Rational Approximation, Electronic Transactions on Numerical Analysis, Volume 35, pp. 57-68, 2009..

• Platform: Matlab, Scripts
• Publisher: Carlos Borges
• Date: 27-06-2013
• Size: 10 KB

Like the finite difference method, the Taylor Series Least Squares method can be used to estimate derivatives. The TLS technique can be used to estimate derivatives from scattered or unstructured data. The Hermite Taylor Series Least Squares technique augments the TLS approach with information about the derivative of the function. The Constrained Hermite Taylor Series Least Squares technique augments the HTLS technique by constraining the least squares problem to match the derivative at the point of interest.

• Platform: Matlab, Scripts
• Publisher: Rob McDonald
• Date: 23-04-2013
• Size: 10 KB

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 implemented in MATLAB and complemented by setting iteration parameters as options. This part of the code has been strongly influenced by Duane Hanselman's function mmfsolve.m. Next to it, a finite difference approximation of Jacobian matrix is appended to it as a nested subfunction as well as a function for dispaying of intermediate results.

• Platform: Matlab, Scripts
• Publisher: Miroslav Balda
• Date: 01-02-2013
• Size: 10 KB

This package contains a set of tools that allows to deform in real-time points and images using the Moving Least Squares algorithms. This is a fast technique to get good image deformations without using the computational expansive techniques provided by the thin-plates splines algorithms. The algorithm was published in the paper "Image Deformation Using Moving Least Squares" by Scott Schaefer, Travis McPhail, Joe Warren.

• Platform: Matlab, Scripts
• Publisher: Gabriele Lombardi
• Date: 03-03-2013
• Size: 1157 KB

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 its complete MATLAB implementation complemented by setting parameters of iterations as options. This part of the code has been strongly influenced by Duane Hanselman's function mmfsolve.m. Calling of the function is rather simple and is one of the following: LMFnlsq % for help output x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0); x = LMFnlsq(Eqns,X0,'Name',Value,.

• Platform: Matlab, Scripts
• Publisher: Miroslav Balda
• Date: 05-06-2013
• Size: 870 KB

The problem of fitting a straight line to data with uncertainties in both coordinates is solved using a weighted total least-squares algorithm. The parameters are transformed from the usual slope/y-axis intersection pair to slope angle and distance to the origin. The advantages of this are that a) global convergence is assured b) a solution is found even for a vertical line. The complete uncertainty matrix (i.e. variances AND covariance of the fitting parameters) is determined. For non-vertical straight lines the usual parameters (slope/y-axis intersect.

• Platform: Matlab, Scripts
• Publisher: Mathias Anton
• Date: 01-01-2013
• Size: 10 KB

We present a Matlab toolbox which can solve basic problems related to the Total Least Squares (TLS) method in the modeling. By illustrative examples we show how to use the TLS method for solution of: - linear regression model - nonlinear regression model - fitting data in 3D space - identification of dynamical system This toolbox requires another two functions, which are already published in Matlab Central File Exchange. Those functions will be installed to computer via supporting install package 'requireFEXpackage' included in TLS package.

• Platform: Matlab, Scripts
• Publisher: Ivo Petras
• Date: 09-04-2013
• Size: 10 KB

ORTHLLS2D returns the Orthogonal Linear Least Squares estimate for parameters of line a x + b y + c = 0 function f = OrthLLS2D(x, y) Inputs x and y must be real vectors of equal size. Output f is the real vector [a b c] where a, b and c are the estimated parameters of the linear equation. Since a more general function called LINORTFITN has already been submitted to File Exchange (ID number: 16800) in October 2007 by Mr. F. Carr, my file is supposed to be used as a brief introduction to the analytical problem in an extremely simple case.

• Platform: Matlab, Scripts
• Publisher: Francesco Pozzi
• Date: 12-06-2013
• Size: 10 KB

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 ommited is taken to be 1e-14. A polynomial of degree n < N is returned if it fits the data with error less than TOL. See polyfit_roots_drv.m for examples..

• Platform: Matlab, Scripts
• Publisher: Amit Hochman
• Date: 20-06-2013
• Size: 82 KB

This function calculates the minimum-norm solution of the least squares problem A*X = B. Where A is low-rank matrix. The function LSMIN is faster then the matlab alternative X = pinv(A)*B. Uses the LAPACK functions (S,C,D,Z)EGLSS or (S,C,D,Z)EGLSD..

• Platform: Matlab, Scripts
• Publisher: Ivo Houtzager
• Date: 12-01-2013
• Size: 10 KB

Demonstration of least squares data fitting using both inverse and backslash operators. This example was developed for use in teaching modeling, simulation, and optimization in graduate engineering courses. A corresponding video is available at:.

• Platform: Matlab, Scripts
• Publisher: James Allison
• Date: 26-06-2013
• Size: 10 KB

Calculate a least squares arc fitted solution to a set of points stored on your device's storage. App scans for, and displays only PNEZD (comma delimited file - Point Number, Northing, Easting, Elevation, Description) formatted text files placed on your device. Each file should contain only those points to be least squares arc fitted. After initial calculation, select point from list to change weight of any point, or choose to include / exclude any point, and recalculate. List shows perpendicular offsets to calculated arc, weighting of each point, and if the point has been included in the solution.

This application calculates the angular and linear coefficients of a linear regression considering the Linear Least Squares methodology..

• Platform: Android 2.x, Android 3.x, Android 4.4, Android 4.x
• Publisher: Prof. Braga
• Date: 22-03-2014
• Size: 282 KB
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