Least Squares Methods

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...

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...

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

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

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...


Platforms: Matlab

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

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

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

Motofit co-refines Neutron and X-ray reflectometry data, using the Abeles matrix / Parratt recursion and least squares fitting (Genetic algorithm or Levenberg Marquardt). It works in the IGOR Pro environment (TM Wavemetrics).


Platforms: Windows, Mac, Linux

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

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

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

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 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

This is a wrapper function to solve optimization problems (using FMINCON) of the form:min w.r.t. X of CostFunc(X) = beta*(W_ls*F(X)) + (1 - beta)*(max(W_mm*F(X)))subject to: A*X


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