Regression analysis projects and source code

Outlier test on an analysis of regression based on the externally studentized residual, r-student. in matlab

The following Matlab project contains the source code and Matlab examples used for outlier test on an analysis of regression based on the externally studentized residual, r-student. . Among the considerations in the use of analysis of regression, outliers or bad values can seriously disturb the least-squares fit.

Rma1multap in matlab

The following Matlab project contains the source code and Matlab examples used for rma1multap. One-sample repeated measures is used to analyze the relationship between the independent variable and dependent variable when:(1) the dependent variable is quantitative in nature and is measured on a level that at least approximates interval characteristics, (2) the independent variable is within-subjects in nature, and (3) the independent variable has three or more levels.

Goodness of fit (modified) in matlab

The following Matlab project contains the source code and Matlab examples used for goodness of fit (modified).  GFIT2 Computes goodness of fit for regression model  USAGE:        [gf] = gfit2(t,y)        [gf] = gfit2(t,y,gFitMeasure)        [gf] = gfit2(t,y,gFitMeasure,options)  INPUT:            t: matrix or vector of target values for regression model            y: matrix or vector of output from regression model.

Two phase linear regression model in matlab

The following Matlab project contains the source code and Matlab examples used for two phase linear regression model.    INPUTS:        x - vector row with 'x' values        y - vector row with 'y' values        r - expected 'x'-coordinate of break point            if r is empty it is calculated during            the optimisation        p - if p is equal to 1 the fit is plotted    OUTPUT:        th - estimated paremeters of the regression            lines            y_1 = th(1) + th(2) * x            y_2 = th(3) + th(4) * x        r - the estimated break point

Solve complex curve fit problem with parameter pooling & stratification by nonlinear least-squares. in matlab

The following Matlab project contains the source code and Matlab examples used for solve complex curve fit problem with parameter pooling & stratification by nonlinear least-squares. . This example solves a complex curve fitting problem that involves parameter pooling and stratification using a nonlinear least-squares approach. This example also takes advantage of some new language features with MATLAB 7.  * Anonymous functions  * Nested functions

Resolve a calibration problem that is: to estimate mean value and confidence interval of x since y in matlab

The following Matlab project contains the source code and Matlab examples used for resolve a calibration problem that is: to estimate mean value and confidence interval of x since y . MYREGRINV: Resolve a calibration problem (inverse regression problem) that is: to estimate mean value and confidence interval of x since y.

Regstats enhanced. robust std.errors; loops on a matrix of responses, 'onlydata' model. in matlab

The following Matlab project contains the source code and Matlab examples used for regstats enhanced. robust std.errors; loops on a matrix of responses, 'onlydata' model. . [ADDITION_1] This enhanced version of regstats has implemented several methods to estimate robust standard errors for coefficients:  - HC0: White robust t statistics (Eicker,1963,1967; Huber,1967; White,1980)  - HC1: With dfe correction (Hinkley,1977)  - HC2: White weighted by 1-h (MacKinnon & White,1985)  - HC3: White weighted by (1-h)^2 (Davidson & MacKinnon,1993)

Linear deming regression in matlab

The following Matlab project contains the source code and Matlab examples used for linear deming regression. [ b sigma2_x x_est y_est stats] = deming(x,y,lambda,alpha) deming() performs a linear Deming regression to find the linear coefficients:                     y = b(1) + b(2)*x under the assumptions that x and y *both* contain measurement error with measurement error variance related as lambda = sigma2_y/sigma2_x (sigma2_x and sigma2_y is the measurement error variance of the x and y variables, respectively).

Pls regression or discriminant analysis, with leave one out cross validation and prediction. in matlab

The following Matlab project contains the source code and Matlab examples used for pls regression or discriminant analysis, with leave one out cross validation and prediction.. Leave-one-out cross-validation for PLS regression or discriminant analysis pls_cv = plscv(x,y,vl,'da') input: x (samples x descriptors) for cross-validation y (samples x variables) for regression or   (samples x classes) for discriminant analysis.

Johansen's multivariate analysis of variance procedure under heteroscedasticity. in matlab

The following Matlab project contains the source code and Matlab examples used for johansen's multivariate analysis of variance procedure under heteroscedasticity. . The multivariate analysis of variance (MANOVA) is the statistical procedure used to comparing the mean vectors of several multivariate normal populations.

Robust multivariate regression using the student-t distribution in matlab

The following Matlab project contains the source code and Matlab examples used for robust multivariate regression using the student-t distribution . The function mvsregress performs regression on multivariate data using the Student-t distribution. Its usage syntax is similar to that of the Statistics Toolbox function mvregress that does regression with the normal distribution. The contribution includes a user manual.

Polynomial curve fitting in matlab

The following Matlab project contains the source code and Matlab examples used for polynomial curve fitting. Polynomial Fitting By Khaled Sharif Description: This function will take two sets of data of equal length and attempt to fit them to polynomials using the polyfit function varying the degree of polynomials from 1 to 100 and choosing the one that gives the least average deviation from the points.

Negative binomial regression in matlab

The following Matlab project contains the source code and Matlab examples used for negative binomial regression. Performs Negative-Binomial regression. Regression coefficients are updated using IRLS, and the dispersion parameter is estimated via Chi^2 dampening. See Hardin, J.W. and Hilbe, J.M. Generalized Linear Models and Extensions. 3rd Ed. p. 251-254. for more information.

Interval prediction of a single value for a geometric mean regression-reduced major axis regression. in matlab

The following Matlab project contains the source code and Matlab examples used for interval prediction of a single value for a geometric mean regression-reduced major axis regression. . Model II regression should be used when the two variables in the regression equation are random and subject to error, i.

Pages

Subscribe to RSS - Regression analysis