Linear Least Squares Regression

ORTHLLS2D returns the Orthogonal Linear Least Squares estimate for parameters of line a x + b y + c = 0function 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...

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...

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


Platforms: Matlab

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...


Platforms: Matlab

Often, measured data is comprised of N sampled values of z, evaluated at N locations (x,y). With this function, you can calculate the coefficients of the best-fit x,y polynomial using a linear least squares approximation.You can use this function if you have a set of N data triplets x,y,z, and...


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

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...


Platforms: Matlab

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...


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

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...


Platforms: Matlab

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...


Platforms: Matlab

Very limited application of least squares. It gives you the plotted curves of original and plotted data with the equation in polynomial form. you can also evaluate that polynomial at different points for prediction.note:1.it would be best viewed on 1024x768.2. if you copy and paste data in data...


Platforms: Matlab

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.


Platforms: Matlab

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:


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

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

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

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


Platforms: Matlab

Solves the linear least squares problem with nonnegative variables using the newton's algorithm in:Portugal, Judice and Vicente, A comparison of block pivoting and interior point algorithms for linear least squares problems with nonnegative variables, Mathematics of Computation, 63(1994), pp....


Platforms: Matlab