NAG Library Chapter Contents

G02 (correg)
Correlation and Regression Analysis


G02 (correg) Chapter Introduction – a description of the Chapter and an overview of the algorithms available

Routine
Name
Mark of
Introduction

Purpose
g02aaf
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22 nagf_correg_corrmat_nearest
Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun
g02abf
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23 nagf_correg_corrmat_nearest_bounded
Computes the nearest correlation matrix to a real square matrix, augmented g02aaf to incorporate weights and bounds
g02aef
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23 nagf_correg_corrmat_nearest_kfactor
Computes the nearest correlation matrix with k-factor structure to a real square matrix
g02ajf
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24 nagf_correg_corrmat_h_weight
Computes the nearest correlation matrix to a real square matrix, using element-wise weighting
g02anf
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25 nagf_correg_corrmat_shrinking
Computes a correlation matrix from an approximate matrix with fixed submatrix
g02apf
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26.0 nagf_correg_corrmat_target
Computes a correlation matrix from an approximate one using a specified target matrix
g02baf
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4 nagf_correg_coeffs_pearson
Pearson product-moment correlation coefficients, all variables, no missing values
g02bbf
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4 nagf_correg_coeffs_pearson_miss_case
Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
g02bcf
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4 nagf_correg_coeffs_pearson_miss_pair
Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
g02bdf
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4 nagf_correg_coeffs_zero
Correlation-like coefficients (about zero), all variables, no missing values
g02bef
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4 nagf_correg_coeffs_zero_miss_case
Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
g02bff
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4 nagf_correg_coeffs_zero_miss_pair
Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
g02bgf
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4 nagf_correg_coeffs_pearson_subset
Pearson product-moment correlation coefficients, subset of variables, no missing values
g02bhf
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4 nagf_correg_coeffs_pearson_subset_miss_case
Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
g02bjf
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4 nagf_correg_coeffs_pearson_subset_miss_pair
Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
g02bkf
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4 nagf_correg_coeffs_zero_subset
Correlation-like coefficients (about zero), subset of variables, no missing values
g02blf
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4 nagf_correg_coeffs_zero_subset_miss_case
Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
g02bmf
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4 nagf_correg_coeffs_zero_subset_miss_pair
Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
g02bnf
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4 nagf_correg_coeffs_kspearman_overwrite
Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
g02bpf
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4 nagf_correg_coeffs_kspearman_miss_case_overwrite
Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
g02bqf
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4 nagf_correg_coeffs_kspearman
Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
g02brf
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4 nagf_correg_coeffs_kspearman_miss_case
Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
g02bsf
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4 nagf_correg_coeffs_kspearman_miss_pair
Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
g02btf
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14 nagf_correg_ssqmat_update
Update a weighted sum of squares matrix with a new observation
g02buf
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14 nagf_correg_ssqmat
Computes a weighted sum of squares matrix
g02bwf
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14 nagf_correg_ssqmat_to_corrmat
Computes a correlation matrix from a sum of squares matrix
g02bxf
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14 nagf_correg_corrmat
Computes (optionally weighted) correlation and covariance matrices
g02byf
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17 nagf_correg_corrmat_partial
Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bxf
g02bzf
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24 nagf_correg_ssqmat_combine
Combines two sums of squares matrices, for use after g02buf
g02caf
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4 nagf_correg_linregs_const
Simple linear regression with constant term, no missing values
g02cbf
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4 nagf_correg_linregs_noconst
Simple linear regression without constant term, no missing values
g02ccf
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4 nagf_correg_linregs_const_miss
Simple linear regression with constant term, missing values
g02cdf
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4 nagf_correg_linregs_noconst_miss
Simple linear regression without constant term, missing values
g02cef
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4 nagf_correg_linregm_service_select
Service routine for multiple linear regression, select elements from vectors and matrices
g02cff
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4 nagf_correg_linregm_service_reorder
Service routine for multiple linear regression, reorder elements of vectors and matrices
g02cgf
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4 nagf_correg_linregm_coeffs_const
Multiple linear regression, from correlation coefficients, with constant term
g02chf
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4 nagf_correg_linregm_coeffs_noconst
Multiple linear regression, from correlation-like coefficients, without constant term
g02daf
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14 nagf_correg_linregm_fit
Fits a general (multiple) linear regression model
g02dcf
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14 nagf_correg_linregm_obs_edit
Add/delete an observation to/from a general linear regression model
g02ddf
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14 nagf_correg_linregm_update
Estimates of linear parameters and general linear regression model from updated model
g02def
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14 nagf_correg_linregm_var_add
Add a new independent variable to a general linear regression model
g02dff
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14 nagf_correg_linregm_var_del
Delete an independent variable from a general linear regression model
g02dgf
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14 nagf_correg_linregm_fit_newvar
Fits a general linear regression model to new dependent variable
g02dkf
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14 nagf_correg_linregm_constrain
Estimates and standard errors of parameters of a general linear regression model for given constraints
g02dnf
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14 nagf_correg_linregm_estfunc
Computes estimable function of a general linear regression model and its standard error
g02eaf
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14 nagf_correg_linregm_rssq
Computes residual sums of squares for all possible linear regressions for a set of independent variables
g02ecf
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14 nagf_correg_linregm_rssq_stat
Calculates R2 and CP values from residual sums of squares
g02eef
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14 nagf_correg_linregm_fit_onestep
Fits a linear regression model by forward selection
g02eff
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21 nagf_correg_linregm_fit_stepwise
Stepwise linear regression
g02faf
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14 nagf_correg_linregm_stat_resinf
Calculates standardized residuals and influence statistics
g02fcf
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15 nagf_correg_linregm_stat_durbwat
Computes Durbin–Watson test statistic
g02gaf
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14 nagf_correg_glm_normal
Fits a generalized linear model with Normal errors
g02gbf
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14 nagf_correg_glm_binomial
Fits a generalized linear model with binomial errors
g02gcf
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14 nagf_correg_glm_poisson
Fits a generalized linear model with Poisson errors
g02gdf
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14 nagf_correg_glm_gamma
Fits a generalized linear model with gamma errors
g02gkf
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14 nagf_correg_glm_constrain
Estimates and standard errors of parameters of a general linear model for given constraints
g02gnf
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14 nagf_correg_glm_estfunc
Computes estimable function of a generalized linear model and its standard error
g02gpf
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22 nagf_correg_glm_predict
Computes a predicted value and its associated standard error based on a previously fitted generalized linear model
g02haf
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13 nagf_correg_robustm
Robust regression, standard M-estimates
g02hbf
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13 nagf_correg_robustm_wts
Robust regression, compute weights for use with g02hdf
g02hdf
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13 nagf_correg_robustm_user
Robust regression, compute regression with user-supplied functions and weights
g02hff
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13 nagf_correg_robustm_user_varmat
Robust regression, variance-covariance matrix following g02hdf
g02hkf
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14 nagf_correg_robustm_corr_huber
Calculates a robust estimation of a covariance matrix, Huber's weight function
g02hlf
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14 nagf_correg_robustm_corr_user_deriv
Calculates a robust estimation of a covariance matrix, user-supplied weight function plus derivatives
g02hmf
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14 nagf_correg_robustm_corr_user
Calculates a robust estimation of a covariance matrix, user-supplied weight function
g02jaf
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21 nagf_correg_mixeff_reml
Linear mixed effects regression using Restricted Maximum Likelihood (REML)
g02jbf
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21 nagf_correg_mixeff_ml
Linear mixed effects regression using Maximum Likelihood (ML)
g02jcf 23 nagf_correg_mixeff_hier_init
Hierarchical mixed effects regression, initialization routine for g02jdf and g02jef
g02jdf
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23 nagf_correg_mixeff_hier_reml
Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML)
g02jef
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23 nagf_correg_mixeff_hier_ml
Hierarchical mixed effects regression using Maximum Likelihood (ML)
g02kaf
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22 nagf_correg_ridge_opt
Ridge regression, optimizing a ridge regression parameter
g02kbf
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22 nagf_correg_ridge
Ridge regression using a number of supplied ridge regression parameters
g02laf
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22 nagf_correg_pls_svd
Partial least squares (PLS) regression using singular value decomposition
g02lbf
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22 nagf_correg_pls_wold
Partial least squares (PLS) regression using Wold's iterative method
g02lcf
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22 nagf_correg_pls_fit
PLS parameter estimates following partial least squares regression by g02laf or g02lbf
g02ldf
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22 nagf_correg_pls_pred
PLS predictions based on parameter estimates from g02lcf
g02maf
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Example Plot
25 nagf_correg_lars
Least angle regression (LARS), least absolute shrinkage and selection operator (LASSO) and forward stagewise regression
g02mbf
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25 nagf_correg_lars_xtx
Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) and forward stagewise regression using the cross-products matrix
g02mcf
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25 nagf_correg_lars_param
Calculates additional parameter estimates following Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) or forward stagewise regression
g02qff
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23 nagf_correg_quantile_linreg_easy
Linear quantile regression, simple interface, independent, identically distributed (IID) errors
g02qgf
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23 nagf_correg_quantile_linreg
Linear quantile regression, comprehensive interface
g02zkf 23 nagf_correg_optset
Option setting routine for g02qgf
g02zlf 23 nagf_correg_optget
Option getting routine for g02qgf
© The Numerical Algorithms Group Ltd, Oxford, UK. 2017