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
- g02ab – Computes the nearest correlation matrix to a real square matrix, augmented g02aa to incorporate weights and bounds
- nag_correg_corrmat_nearest_bounded
- g02ae – Computes the nearest correlation matrix with k-factor structure to a real square matrix
- nag_correg_corrmat_nearest_kfactor
- g02aj – Computes the nearest correlation matrix to a real square matrix, using element-wise weighting
- nag_nearest_correlation_h_weight
- g02an – Computes a correlation matrix from an approximate matrix with fixed submatrix
- nag_nearest_correlation_shrinking
- g02ba – Pearson product-moment correlation coefficients, all variables, no missing values
- nag_correg_coeffs_pearson
- g02bb – Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
- nag_correg_coeffs_pearson_miss_case
- g02bc – Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
- nag_correg_coeffs_pearson_miss_pair
- g02bd – Correlation-like coefficients (about zero), all variables, no missing values
- nag_correg_coeffs_zero
- g02be – Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
- nag_correg_coeffs_zero_miss_case
- g02bf – Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
- nag_correg_coeffs_zero_miss_pair
- g02bg – Pearson product-moment correlation coefficients, subset of variables, no missing values
- nag_correg_coeffs_pearson_subset
- g02bh – Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
- nag_correg_coeffs_pearson_subset_miss_case
- g02bj – Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
- nag_correg_coeffs_pearson_subset_miss_pair
- g02bk – Correlation-like coefficients (about zero), subset of variables, no missing values
- nag_correg_coeffs_zero_subset
- g02bl – Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
- nag_correg_coeffs_zero_subset_miss_case
- g02bm – Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
- nag_correg_coeffs_zero_subset_miss_pair
- g02bn – Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
- nag_correg_coeffs_kspearman_overwrite
- g02bp – Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
- nag_correg_coeffs_kspearman_miss_case_overwrite
- g02bq – Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
- nag_correg_coeffs_kspearman
- g02br – Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
- nag_correg_coeffs_kspearman_miss_case
- g02bs – Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
- nag_correg_coeffs_kspearman_miss_pair
- g02bt – Update a weighted sum of squares matrix with a new observation
- nag_correg_ssqmat_update
- g02bu – Computes a weighted sum of squares matrix
- nag_correg_ssqmat
- g02bw – Computes a correlation matrix from a sum of squares matrix
- nag_correg_ssqmat_to_corrmat
- g02bx – Computes (optionally weighted) correlation and covariance matrices
- nag_correg_corrmat
- g02by – Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bx
- nag_correg_corrmat_partial
- g02bz – Combines two sums of squares matrices, for use after g02bu
- nag_correg_ssqmat_combine
- g02ca – Simple linear regression with constant term, no missing values
- nag_correg_linregs_const
- g02cb – Simple linear regression without constant term, no missing values
- nag_correg_linregs_noconst
- g02cc – Simple linear regression with constant term, missing values
- nag_correg_linregs_const_miss
- g02cd – Simple linear regression without constant term, missing values
- nag_correg_linregs_noconst_miss
- g02ce – Service function for multiple linear regression, select elements from vectors and matrices
- nag_correg_linregm_service_select
- g02cf – Service function for multiple linear regression, reorder elements of vectors and matrices
- nag_correg_linregm_service_reorder
- g02cg – Multiple linear regression, from correlation coefficients, with constant term
- nag_correg_linregm_coeffs_const
- g02ch – Multiple linear regression, from correlation-like coefficients, without constant term
- nag_correg_linregm_coeffs_noconst
- g02da – Fits a general (multiple) linear regression model
- nag_correg_linregm_fit
- g02dc – Add/delete an observation to/from a general linear regression model
- nag_correg_linregm_obs_edit
- g02dd – Estimates of linear parameters and general linear regression model from updated model
- nag_correg_linregm_update
- g02de – Add a new independent variable to a general linear regression model
- nag_correg_linregm_var_add
- g02df – Delete an independent variable from a general linear regression model
- nag_correg_linregm_var_del
- g02dg – Fits a general linear regression model to new dependent variable
- nag_correg_linregm_fit_newvar
- g02dk – Estimates and standard errors of parameters of a general linear regression model for given constraints
- nag_correg_linregm_constrain
- g02dn – Computes estimable function of a general linear regression model and its standard error
- nag_correg_linregm_estfunc
- g02ea – Computes residual sums of squares for all possible linear regressions for a set of independent variables
- nag_correg_linregm_rssq
- g02ec – Calculates R2 and CP values from residual sums of squares
- nag_correg_linregm_rssq_stat
- g02ee – Fits a linear regression model by forward selection
- nag_correg_linregm_fit_onestep
- g02ef – Stepwise linear regression
- nag_correg_linregm_fit_stepwise
- g02fa – Calculates standardized residuals and influence statistics
- nag_correg_linregm_stat_resinf
- g02fc – Computes Durbin–Watson test statistic
- nag_correg_linregm_stat_durbwat
- g02ga – Fits a generalized linear model with Normal errors
- nag_correg_glm_normal
- g02gb – Fits a generalized linear model with binomial errors
- nag_correg_glm_binomial
- g02gc – Fits a generalized linear model with Poisson errors
- nag_correg_glm_poisson
- g02gd – Fits a generalized linear model with gamma errors
- nag_correg_glm_gamma
- g02gk – Estimates and standard errors of parameters of a general linear model for given constraints
- nag_correg_glm_constrain
- g02gn – Computes estimable function of a generalized linear model and its standard error
- nag_correg_glm_estfunc
- g02gp – Computes a predicted value and its associated standard error based on a previously fitted generalized linear model
- nag_correg_glm_predict
- g02ha – Robust regression, standard M-estimates
- nag_correg_robustm
- g02hb – Robust regression, compute weights for use with g02hd
- nag_correg_robustm_wts
- g02hd – Robust regression, compute regression with user-supplied functions and weights
- nag_correg_robustm_user
- g02hf – Robust regression, variance-covariance matrix following g02hd
- nag_correg_robustm_user_varmat
- g02hk – Calculates a robust estimation of a covariance matrix, Huber's weight function
- nag_correg_robustm_corr_huber
- g02hl – Calculates a robust estimation of a covariance matrix, user-supplied weight function plus derivatives
- nag_correg_robustm_corr_user_deriv
- g02hm – Calculates a robust estimation of a covariance matrix, user-supplied weight function
- nag_correg_robustm_corr_user
- g02ja – Linear mixed effects regression using Restricted Maximum Likelihood (REML)
- nag_correg_mixeff_reml
- g02jb – Linear mixed effects regression using Maximum Likelihood (ML)
- nag_correg_mixeff_ml
- g02jc – Hierarchical mixed effects regression, initialization function for g02jd, g02je
- nag_correg_mixeff_hier_init
- g02jd – Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML)
- nag_correg_mixeff_hier_reml
- g02je – Hierarchical mixed effects regression using Maximum Likelihood (ML)
- nag_correg_mixeff_hier_ml
- g02ka – Ridge regression, optimizing a ridge regression parameter
- nag_correg_ridge_opt
- g02kb – Ridge regression using a number of supplied ridge regression parameters
- nag_correg_ridge
- g02la – Partial least squares (PLS) regression using singular value decomposition
- nag_correg_pls_svd
- g02lb – Partial least squares (PLS) regression using Wold's iterative method
- nag_correg_pls_wold
- g02lc – PLS parameter estimates following partial least squares regression by g02la, g02lb
- nag_correg_pls_fit
- g02ld – PLS predictions based on parameter estimates from g02lc
- nag_correg_pls_pred
- g02ma – Least angle regression (LARS), least absolute shrinkage and selection operator (LASSO) and forward stagewise regression
- nag_correg_lars
- g02mb – Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) and forward stagewise regression using
the cross-products matrix
- nag_correg_lars_xtx
- g02mc – Additional parameter calculate following Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO)
or forward stagewise regression
- nag_correg_lars_param
- g02qf – Linear quantile regression, simple interface, independent, identically distributed (IID) errors
- nag_correg_quantile_linreg_easy
- g02qg – Linear quantile regression, comprehensive interface
- nag_correg_quantile_linreg
- g02zk – Option setting function for g02qg
- nag_correg_optset
- g02zl – Option getting function for g02qg
- nag_correg_optget
