Least Squares Regression

A contour of a 2D region is defined by an ordered set of points where the neighboring elements contain the neighboring points. Such representation can be obtained with many techniques such as boundary tracing and chain codes. (In a simple 2D point set or a curve the points do not have to lie in a...

CurveFitter performs statistical regression analysis to estimate the values of parameters for linear, multivariate, polynomial, exponential and nonlinear functions. The regression analysis determines the values of the parameters that cause the function to best fit the observed data that you...

DataFitting is a powerful statistical analysis program that performs linear and nonlinear regression analysis (i.e. curve fitting). DataFitting determines the values of parameters for an equation, whose form you specify, that cause the equation to best fit a set of data values. DataFitting can...


Platforms: Windows, Windows 7, Other

FindGraph is a comprehensive graphing, curve fitting, and digitizing tool. FindGraph offers 12 generic fits, including linear regression, logistic functions, fourier approximation, rationals, neural networks, B-splines and parametric curves least squares approximations, plus a library of over 300...


Platforms: Windows

% Original model: y = X*beta + u% We are concerned that a regressor in X% could be endogenous.% W contains the instruments and all regressors in X% except the suspected one.% An example:% m_t = b1 + b2*r_t + b3*y_t + b4*m_(t-1) + b5*m_(t-2) + u_t% In this model, we have a concern that r_t could...


Platforms: Matlab

Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both...


Platforms: Matlab

Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both...


Platforms: Matlab

Model II regression should be used when the two variables in the regression equation are random and subject to error, i.e. not controlled by the researcher. Model I regression using ordinary least squares underestimates the slope of the linear relationship between the variables when they both...


Platforms: Matlab

The Kernel-Machine Library is a freely available (released under the GPL) C++ library to promote the use and progress of kernel machines. It is both for academic use and for developing real world applications. The Kernel-Machine Library draws heavily from features of modern C++ such as template...


Platforms: *nix

GS-Calc is a spreadsheet application that will help you to organize your data, track your expenses and incomes, create reports, search and edit databases. Using fast GS-Calc pivot tables and other advanced functions you can perform the most demanding statistical calculations and data analysis....


Platforms: Windows

NMath Stats is a .NET statistics library that provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics,...


Platforms: Windows

Is a cross-platform software package for econometric analysis, written in the C programming language. It is free, open-source software. You may redistribute it and/or modify it under the terms of the GNU General Public License (GPL) as published by the Free Software Foundation. Features Easy...


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

Group and Block Sparse Signal reconstruction viaMatching Pursuit - BMP, GMPGradient Pursuit - block_gp, group_gpNearly Orthogonal Matching Pursuit - block_nomp, group_nompPartial Conjugate Gradient Pursuit - block_pcgp, group_pcgpOrthogonal Least Squares - BOLS, GOLS


Platforms: Matlab

In this submission we present the code of the Replacement Method (RM) [1-4] and Enhanced Replacement Method (ERM)[5], that were proposed some time ago by our group. Both methods produce linear regression QSPR-QSAR models that are quite close the FS (full search) ones with much less computational...


Platforms: Matlab

It is known that there is no sufficient Matlab program about neuro-fuzzy classifiers. Generally, ANFIS is used as classifier. ANFIS is a function approximator program. But, the usage of ANFIS for classifications is unfavorable. For example, there are three classes, and labeled as 1, 2 and 3. The...


Platforms: Matlab

rilt Regularized Inverse Laplace Transform [g,yfit,cfg] = rilt(t,y,s,g0,alpha) Array g(s) is the Inverse Laplace Transform of the array y(t), calculated by a regularized least squares method.This script is an emulation of S. Provencher CONTIN program, written in Fortran. See...


Platforms: Matlab

I need to thank Duane Hanselman for suggesting this great idea.Fminspleas is a simple nonlinear least squares tool that fits regression models of the formY = a1*f1(X,C) + a2*f2(X,C) + ... + an*fn(X,C)X can be any array, so it works on multidimensionalproblems, and C is the set of only...


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

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