Nonlinear Least Squares Fitting

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.

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

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

Both functions caculate the Lomb normalized periodogram (aka Lomb-Scargle, Gauss-Vanicek or Least-Squares spectrum) of a vector x with coordinates in t, which is essentially a generalization of the DFT for unevenly sampled data.The codes are transcriptions from Fortran of the subroutines found in...


Platforms: Matlab

1 Introduction PLS-DA for data analysis in chemistry and OMICS studies. Also included in this package are 3 variable selection methods: 1) target projection (TP) 2) competitive adaptive reweighted sampling (CARS) 3) subwindow permutation analysis (SPA). SPA is based on model pupulation analysis...


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

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...


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

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

The code implements the algorithm as given in Chen et. al 1991.It takes max tolerance from the user.It selects Ms significant regressors from the total M regressors.Unfortunately, the error obtained after training the RBF network isn't as expected. Is there a mistake in my code???Please feel free...


Platforms: Matlab

This code optimises the parameters for the term structure of volatility (TSOV) applied to historical forward curves - see discussion in Harris in reference 1 below. TSOVs avaible are: TermType - 1 = sigma = A exp (-CT) - 2 = sigma = A exp (-CT) + D - 3 = sigma = (A + BT) exp (-CT) + DRequired...


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

This is a robust and accurate circle fit. It works well even if datapoints are observed only within a small arc. This circle fit was proposed by V. Pratt in article "Direct least-squares fitting of algebraic surfaces", Computer Graphics, Vol. 21, pages 145-152 (1987). It is more stable than the...


Platforms: Matlab

This library implements a randomized algorithm for solving the least-squares equation x = arg min norm(A * x - b, 2) or the underdetermined system min(norm(x, 2)) s.t. Ax=b. With high probability the running this o(mn^2) for an m-by-n matrix.For details of the solver see the paper at:Blendenpik:...


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

Solves the linear least squares problem with nonnegative variables using the predictor-corrector 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,...


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

The GNU Scientific Library (GSL) is a numerical library for C and C++ programmers. It is free software under the GNU General Public License. GNU Scientific Library provides a wide range of mathematical routines such as random number generators, special functions and least-squares fitting. There...


Platforms: *nix

This package is a loosely defined collection of tools for application of Python in a laboratory environment. It consists of pure python protocol drivers for various useful devices, including: National Instrument DSTP protocol to interface Python to LabVIEW VXI-11 protocol, to allow python to talk...


Platforms: Windows, Mac, *nix, Python, BSD Solaris

This is a fast and non-iterative ellipse fit. Usage: A = EllipseDirectFit(XY)Input: XY(n,2) is the array of coordinates of n points x(i)=XY(i,1), y(i)=XY(i,2) Output: A = [a b c d e f]' is the vector of coefficients of the equation of the best fitting ellipse: ax^2 + bxy + cy^2 + dx + ey + f =...


Platforms: Matlab