Chapter Introduction (pdf version)
NAG Library Manual

G02 – Correlation and Regression Analysis

G02 Chapter Introduction
Routine
Name
Mark of
Introduction

Purpose
G02BAF
Example Text
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4 Pearson product-moment correlation coefficients, all variables, no missing values
G02BBF
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4 Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
G02BCF
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4 Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
G02BDF
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4 Correlation-like coefficients (about zero), all variables, no missing values
G02BEF
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4 Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
G02BFF
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4 Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
G02BGF
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4 Pearson product-moment correlation coefficients, subset of variables, no missing values
G02BHF
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4 Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
G02BJF
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4 Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
G02BKF
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4 Correlation-like coefficients (about zero), subset of variables, no missing values
G02BLF
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4 Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
G02BMF
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4 Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
G02BNF
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4 Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
G02BPF
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4 Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
G02BQF
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4 Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
G02BRF
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4 Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
G02BSF
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4 Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
G02BTF
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14 Update a weighted sum of squares matrix with a new observation
G02BUF
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14 Computes a weighted sum of squares matrix
G02BWF
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14 Computes a correlation matrix from a sum of squares matrix
G02BXF
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14 Computes (optionally weighted) correlation and covariance matrices
G02BYF
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17 Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by G02BXF
G02CAF
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4 Simple linear regression with constant term, no missing values
G02CBF
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4 Simple linear regression without constant term, no missing values
G02CCF
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4 Simple linear regression with constant term, missing values
G02CDF
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4 Simple linear regression without constant term, missing values
G02CEF
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4 Service routines for multiple linear regression, select elements from vectors and matrices
G02CFF
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4 Service routines for multiple linear regression, re-order elements of vectors and matrices
G02CGF
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4 Multiple linear regression, from correlation coefficients, with constant term
G02CHF
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4 Multiple linear regression, from correlation-like coefficients, without constant term
G02DAF
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14 Fits a general (multiple) linear regression model
G02DCF
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14 Add/delete an observation to/from a general linear regression model
G02DDF
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14 Estimates of linear parameters and general linear regression model from updated model
G02DEF
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14 Add a new independent variable to a general linear regression model
G02DFF
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14 Delete an independent variable from a general linear regression model
G02DGF
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14 Fits a general linear regression model to new dependent variable
G02DKF
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14 Estimates and standard errors of parameters of a general linear regression model for given constraints
G02DNF
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14 Computes estimable function of a general linear regression model and its standard error
G02EAF
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14 Computes residual sums of squares for all possible linear regressions for a set of independent variables
G02ECF
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14 Calculates R2 and CP values from residual sums of squares
G02EEF
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14 Fits a linear regression model by forward selection
G02EFF
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21 Stepwise linear regression
G02FAF
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14 Calculates standardized residuals and influence statistics
G02FCF
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15 Computes Durbin–Watson test statistic
G02GAF
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14 Fits a generalized linear model with Normal errors
G02GBF
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14 Fits a generalized linear model with binomial errors
G02GCF
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14 Fits a generalized linear model with Poisson errors
G02GDF
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14 Fits a generalized linear model with gamma errors
G02GKF
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14 Estimates and standard errors of parameters of a general linear model for given constraints
G02GNF
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14 Computes estimable function of a generalized linear model and its standard error
G02HAF
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13 Robust regression, standard M-estimates
G02HBF
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13 Robust regression, compute weights for use with G02HDF
G02HDF
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13 Robust regression, compute regression with user-supplied functions and weights
G02HFF
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13 Robust regression, variance-covariance matrix following G02HDF
G02HKF
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14 Calculates a robust estimation of a correlation matrix, Huber's weight function
G02HLF
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14 Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
G02HMF
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14 Calculates a robust estimation of a correlation matrix, user-supplied weight function
G02JAF
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21 Linear mixed effects regression using Restricted Maximum Likelihood (REML)
G02JBF
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21 Linear mixed effects regression using Maximum Likelihood (ML)

Chapter Introduction (pdf version)
NAG Library Manual

The Numerical Algorithms Group Ltd, Oxford, UK. 2006