Home / Products / Numerical Libraries / MATLAB^®-NAG Toolbox

• G02 Introduction
• g02ba – Pearson product-moment correlation coefficients, all variables, no missing values
• g02bb – Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
• g02bc – Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
• g02bd – Correlation-like coefficients (about zero), all variables, no missing values
• g02be – Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
• g02bf – Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
• g02bg – Pearson product-moment correlation coefficients, subset of variables, no missing values
• g02bh – Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
• g02bj – Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
• g02bk – Correlation-like coefficients (about zero), subset of variables, no missing values
• g02bl – Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
• g02bm – Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
• g02bn – Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
• g02bp – Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
• g02bq – Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
• g02br – Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
• g02bs – Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
• g02bt – Update a weighted sum of squares matrix with a new observation
• g02bu – Computes a weighted sum of squares matrix
• g02bw – Computes a correlation matrix from a sum of squares matrix
• g02bx – Computes (optionally weighted) correlation and covariance matrices
• g02by – Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bx
• g02ca – Simple linear regression with constant term, no missing values
• g02cb – Simple linear regression without constant term, no missing values
• g02cc – Simple linear regression with constant term, missing values
• g02cd – Simple linear regression without constant term, missing values
• g02ce – Service routines for multiple linear regression, select elements from vectors and matrices
• g02cf – Service routines for multiple linear regression, re-order elements of vectors and matrices
• g02cg – Multiple linear regression, from correlation coefficients, with constant term
• g02ch – Multiple linear regression, from correlation-like coefficients, without constant term
• g02da – Fits a general (multiple) linear regression model
• g02dc – Add/delete an observation to/from a general linear regression model
• g02dd – Estimates of linear parameters and general linear regression model from updated model
• g02de – Add a new independent variable to a general linear regression model
• g02df – Delete an independent variable from a general linear regression model
• g02dg – Fits a general linear regression model to new dependent variable
• g02dk – Estimates and standard errors of parameters of a general linear regression model for given constraints
• g02dn – Computes estimable function of a general linear regression model and its standard error
• g02ea – Computes residual sums of squares for all possible linear regressions for a set of independent variables
• g02ec – Calculates R^2 and C_P values from residual sums of squares
• g02ee – Fits a linear regression model by forward selection
• g02ef – Stepwise linear regression
• g02fa – Calculates standardized residuals and influence statistics
• g02fc – Computes Durbin–Watson test statistic
• g02ga – Fits a generalized linear model with Normal errors
• g02gb – Fits a generalized linear model with binomial errors
• g02gc – Fits a generalized linear model with Poisson errors
• g02gd – Fits a generalized linear model with gamma errors
• g02gk – Estimates and standard errors of parameters of a general linear model for given constraints
• g02gn – Computes estimable function of a generalized linear model and its standard error
• g02ha – Robust regression, standard M-estimates
• g02hb – Robust regression, compute weights for use with g02hd
• g02hd – Robust regression, compute regression with user-supplied functions and weights
• g02hf – Robust regression, variance-covariance matrix following g02hd
• g02hk – Calculates a robust estimation of a correlation matrix, Huber's weight function
• g02hl – Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
• g02hm – Calculates a robust estimation of a correlation matrix, user-supplied weight function
• g02ja – Linear mixed effects regression using Restricted Maximum Likelihood (REML)
• g02jb – Linear mixed effects regression using Maximum Likelihood (ML)