NAG Fortran Library

G02 - Correlation and Regression Analysis


Chapter Introduction
G02BAF Pearson product-moment correlation coefficients, all variables, no missing values
G02BBF Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
G02BCF Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
G02BDF Correlation-like coefficients (about zero), all variables, no missing values
G02BEF Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
G02BFF Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
G02BGF Pearson product-moment correlation coefficients, subset of variables, no missing values
G02BHF Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
G02BJF Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
G02BKF Correlation-like coefficients (about zero), subset of variables, no missing values
G02BLF Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
G02BMF Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
G02BNF Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
G02BPF Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
G02BQF Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
G02BRF Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
G02BSF Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
G02BTF Update a weighted sum of squares matrix with a new observation
G02BUF Computes a weighted sum of squares matrix
G02BWF Computes a correlation matrix from a sum of squares matrix
G02BXF Computes (optionally weighted) correlation and covariance matrices
G02BYF Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by G02BXF
G02CAF Simple linear regression with constant term, no missing values
G02CBF Simple linear regression without constant term, no missing values
G02CCF Simple linear regression with constant term, missing values
G02CDF Simple linear regression without constant term, missing values
G02CEF Service routines for multiple linear regression, select elements from vectors and matrices
G02CFF Service routines for multiple linear regression, re-order elements of vectors and matrices
G02CGF Multiple linear regression, from correlation coefficients, with constant term
G02CHF Multiple linear regression, from correlation-like coefficients, without constant term
G02DAF Fits a general (multiple) linear regression model
G02DCF Add/delete an observation to/from a general linear regression model
G02DDF Estimates of linear parameters and general linear regression model from updated model
G02DEF Add a new variable to a general linear regression model
G02DFF Delete a variable from a general linear regression model
G02DGF Fits a general linear regression model for new dependent variable
G02DKF Estimates and standard errors of parameters of a general linear regression model for given constraints
G02DNF Computes estimable function of a general linear regression model and its standard error
G02EAF Computes residual sums of squares for all possible linear regressions for a set of independent variables
G02ECF Calculates R2 and CP values from residual sums of squares
G02EEF Fits a linear regression model by forward selection
G02FAF Calculates standardized residuals and influence statistics
G02FCF Computes Durbin--Watson test statistic
G02GAF Fits a generalized linear model with Normal errors
G02GBF Fits a generalized linear model with binomial errors
G02GCF Fits a generalized linear model with Poisson errors
G02GDF Fits a generalized linear model with gamma errors
G02GKF Estimates and standard errors of parameters of a general linear model for given constraints
G02GNF Computes estimable function of a generalized linear model and its standard error
G02HAF Robust regression, standard M-estimates
G02HBF Robust regression, compute weights for use with G02HDF
G02HDF Robust regression, compute regression with user-supplied functions and weights
G02HFF Robust regression, variance-covariance matrix following G02HDF
G02HKF Calculates a robust estimation of a correlation matrix, Huber's weight function
G02HLF Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
G02HMF Calculates a robust estimation of a correlation matrix, user-supplied weight function


© The Numerical Algorithms Group Ltd, Oxford UK. 1999