NAG Toolbox

G02 – Correlation and Regression Analysis

• G02 Introduction
• g02aa – Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun
• nag_correg_corrmat_nearest – g02aa
• g02ab – Computes the nearest correlation matrix to a real square matrix, augmented g02aa to incorporate weights and bounds
• nag_correg_corrmat_nearest_bounded – g02ab
• g02ae – Computes the nearest correlation matrix with k-factor structure to a real square matrix
• nag_correg_corrmat_nearest_kfactor – g02ae
• g02ba – Pearson product-moment correlation coefficients, all variables, no missing values
• nag_correg_coeffs_pearson – g02ba
• g02bb – Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
• nag_correg_coeffs_pearson_miss_case – g02bb
• g02bc – Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
• nag_correg_coeffs_pearson_miss_pair – g02bc
• g02bd – Correlation-like coefficients (about zero), all variables, no missing values
• nag_correg_coeffs_zero – g02bd
• g02be – Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
• nag_correg_coeffs_zero_miss_case – g02be
• g02bf – Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
• nag_correg_coeffs_zero_miss_pair – g02bf
• g02bg – Pearson product-moment correlation coefficients, subset of variables, no missing values
• nag_correg_coeffs_pearson_subset – g02bg
• g02bh – Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
• nag_correg_coeffs_pearson_subset_miss_case – g02bh
• g02bj – Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
• nag_correg_coeffs_pearson_subset_miss_pair – g02bj
• g02bk – Correlation-like coefficients (about zero), subset of variables, no missing values
• nag_correg_coeffs_zero_subset – g02bk
• g02bl – Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
• nag_correg_coeffs_zero_subset_miss_case – g02bl
• g02bm – Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
• nag_correg_coeffs_zero_subset_miss_pair – g02bm
• g02bn – Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
• nag_correg_coeffs_kspearman_overwrite – g02bn
• g02bp – Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
• nag_correg_coeffs_kspearman_miss_case_overwrite – g02bp
• g02bq – Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
• nag_correg_coeffs_kspearman – g02bq
• g02br – Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
• nag_correg_coeffs_kspearman_miss_case – g02br
• g02bs – Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
• nag_correg_coeffs_kspearman_miss_pair – g02bs
• g02bt – Update a weighted sum of squares matrix with a new observation
• nag_correg_ssqmat_update – g02bt
• g02bu – Computes a weighted sum of squares matrix
• nag_correg_ssqmat – g02bu
• g02bw – Computes a correlation matrix from a sum of squares matrix
• nag_correg_ssqmat_to_corrmat – g02bw
• g02bx – Computes (optionally weighted) correlation and covariance matrices
• nag_correg_corrmat – g02bx
• g02by – Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bx
• nag_correg_corrmat_partial – g02by
• g02ca – Simple linear regression with constant term, no missing values
• nag_correg_linregs_const – g02ca
• g02cb – Simple linear regression without constant term, no missing values
• nag_correg_linregs_noconst – g02cb
• g02cc – Simple linear regression with constant term, missing values
• nag_correg_linregs_const_miss – g02cc
• g02cd – Simple linear regression without constant term, missing values
• nag_correg_linregs_noconst_miss – g02cd
• g02ce – Service function for multiple linear regression, select elements from vectors and matrices
• nag_correg_linregm_service_select – g02ce
• g02cf – Service function for multiple linear regression, re-order elements of vectors and matrices
• nag_correg_linregm_service_reorder – g02cf
• g02cg – Multiple linear regression, from correlation coefficients, with constant term
• nag_correg_linregm_coeffs_const – g02cg
• g02ch – Multiple linear regression, from correlation-like coefficients, without constant term
• nag_correg_linregm_coeffs_noconst – g02ch
• g02da – Fits a general (multiple) linear regression model
• nag_correg_linregm_fit – g02da
• g02dc – Add/delete an observation to/from a general linear regression model
• nag_correg_linregm_obs_edit – g02dc
• g02dd – Estimates of linear parameters and general linear regression model from updated model
• nag_correg_linregm_update – g02dd
• g02de – Add a new independent variable to a general linear regression model
• nag_correg_linregm_var_add – g02de
• g02df – Delete an independent variable from a general linear regression model
• nag_correg_linregm_var_del – g02df
• g02dg – Fits a general linear regression model to new dependent variable
• nag_correg_linregm_fit_newvar – g02dg
• g02dk – Estimates and standard errors of parameters of a general linear regression model for given constraints
• nag_correg_linregm_constrain – g02dk
• g02dn – Computes estimable function of a general linear regression model and its standard error
• nag_correg_linregm_estfunc – g02dn
• g02ea – Computes residual sums of squares for all possible linear regressions for a set of independent variables
• nag_correg_linregm_rssq – g02ea
• g02ec – Calculates R^2 and C_P values from residual sums of squares
• nag_correg_linregm_rssq_stat – g02ec
• g02ee – Fits a linear regression model by forward selection
• nag_correg_linregm_fit_onestep – g02ee
• g02ef – Stepwise linear regression
• nag_correg_linregm_fit_stepwise – g02ef
• g02fa – Calculates standardized residuals and influence statistics
• nag_correg_linregm_stat_resinf – g02fa
• g02fc – Computes Durbin–Watson test statistic
• nag_correg_linregm_stat_durbwat – g02fc
• g02ga – Fits a generalized linear model with Normal errors
• nag_correg_glm_normal – g02ga
• g02gb – Fits a generalized linear model with binomial errors
• nag_correg_glm_binomial – g02gb
• g02gc – Fits a generalized linear model with Poisson errors
• nag_correg_glm_poisson – g02gc
• g02gd – Fits a generalized linear model with gamma errors
• nag_correg_glm_gamma – g02gd
• g02gk – Estimates and standard errors of parameters of a general linear model for given constraints
• nag_correg_glm_constrain – g02gk
• g02gn – Computes estimable function of a generalized linear model and its standard error
• nag_correg_glm_estfunc – g02gn
• g02gp – Computes a predicted value and its associated standard error based on a previously fitted generalized linear model
• nag_correg_glm_predict – g02gp
• g02ha – Robust regression, standard M-estimates
• nag_correg_robustm – g02ha
• g02hb – Robust regression, compute weights for use with g02hd
• nag_correg_robustm_wts – g02hb
• g02hd – Robust regression, compute regression with user-supplied functions and weights
• nag_correg_robustm_user – g02hd
• g02hf – Robust regression, variance-covariance matrix following g02hd
• nag_correg_robustm_user_varmat – g02hf
• g02hk – Calculates a robust estimation of a correlation matrix, Huber's weight function
• nag_correg_robustm_corr_huber – g02hk
• g02hl – Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
• nag_correg_robustm_corr_user_deriv – g02hl
• g02hm – Calculates a robust estimation of a correlation matrix, user-supplied weight function
• nag_correg_robustm_corr_user – g02hm
• g02ja – Linear mixed effects regression using Restricted Maximum Likelihood (REML)
• nag_correg_mixeff_reml – g02ja
• g02jb – Linear mixed effects regression using Maximum Likelihood (ML)
• nag_correg_mixeff_ml – g02jb
• g02jc – Hierarchical mixed effects regression, initialization function for g02jd, g02je
• nag_correg_mixeff_hier_init – g02jc
• g02jd – Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML)
• nag_correg_mixeff_hier_reml – g02jd
• g02je – Hierarchical mixed effects regression using Maximum Likelihood (ML)
• nag_correg_mixeff_hier_ml – g02je
• g02ka – Ridge regression, optimizing a ridge regression parameter
• nag_correg_ridge_opt – g02ka
• g02kb – Ridge regression using a number of supplied ridge regression parameters
• nag_correg_ridge – g02kb
• g02la – Partial least squares (PLS) regression using singular value decomposition
• nag_correg_pls_svd – g02la
• g02lb – Partial least squares (PLS) regression using Wold's iterative method
• nag_correg_pls_wold – g02lb
• g02lc – PLS parameter estimates following partial least squares regression by g02la, g02lb
• nag_correg_pls_fit – g02lc
• g02ld – PLS predictions based on parameter estimates from g02lc
• nag_correg_pls_pred – g02ld
• g02qf – Quantile linear regression, simple interface, independent, identically distributed (IID) errors
• nag_correg_quantile_linreg_easy – g02qf
• g02qg – Quantile linear regression, comprehensive interface
• nag_correg_quantile_linreg – g02qg
• g02zk – Option setting function for g02qg
• nag_correg_optset – g02zk
• g02zl – Option getting function for g02qg
• nag_correg_optget – g02zl

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G03