Function |
Mark of Introduction |
Purpose |
|---|---|---|
| g02aac | 9 | nag_correg_corrmat_nearest Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun |
| g02abc | 23 | nag_correg_corrmat_nearest_bounded Computes the nearest correlation matrix to a real square matrix, augmenting g02aac to incorporate weights and bounds |
| g02aec | 23 | nag_correg_corrmat_nearest_kfactor Computes the nearest correlation matrix with -factor structure to a real square matrix |
| g02ajc | 24 | nag_correg_corrmat_h_weight Computes the nearest correlation matrix to a real square matrix, using element-wise weighting |
| g02akc | 27 | nag_correg_corrmat_nearest_rank Computes the rank-constrained nearest correlation matrix to a real square matrix, using the method of Qi and Sun |
| g02anc | 25 | nag_correg_corrmat_shrinking Computes a correlation matrix from an approximate matrix with fixed submatrix |
| g02apc | 26 | nag_correg_corrmat_target Computes a correlation matrix from an approximate one using a specified target matrix |
| g02asc | 27 | nag_correg_corrmat_fixed Computes the nearest correlation matrix to a real square matrix, with fixed elements |
| g02brc | 3 | nag_correg_coeffs_kspearman_miss_case Kendall and/or Spearman non-parametric rank correlation coefficients, allows variables and observations to be selectively disregarded |
| g02btc | 7 | nag_correg_ssqmat_update Update a weighted sum of squares matrix with a new observation |
| g02buc | 7 | nag_correg_ssqmat Computes a weighted sum of squares matrix |
| g02bwc | 7 | nag_correg_ssqmat_to_corrmat Computes a correlation matrix from a sum of squares matrix |
| g02bxc | 3 | nag_correg_corrmat Product-moment correlation, unweighted/weighted correlation and covariance matrix, allows variables to be disregarded |
| g02byc | 6 | nag_correg_corrmat_partial Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bxc |
| g02bzc | 24 | nag_correg_ssqmat_combine Combines two sums of squares matrices, for use after g02buc |
| g02cac | 3 | nag_correg_linregs_const Simple linear regression with or without a constant term, data may be weighted |
| g02cbc | 3 | nag_correg_linregs_noconst Simple linear regression confidence intervals for the regression line and individual points |
| g02dac | 1 | nag_correg_linregm_fit Fits a general (multiple) linear regression model |
| g02dcc | 2 | nag_correg_linregm_obs_edit Add/delete an observation to/from a general linear regression model |
| g02ddc | 2 | nag_correg_linregm_update Estimates of regression parameters from an updated model |
| g02dec | 2 | nag_correg_linregm_var_add Add a new independent variable to a general linear regression model |
| g02dfc | 2 | nag_correg_linregm_var_del Delete an independent variable from a general linear regression model |
| g02dgc | 1 | nag_correg_linregm_fit_newvar Fits a general linear regression model to new dependent variable |
| g02dkc | 2 | nag_correg_linregm_constrain Estimates of parameters of a general linear regression model for given constraints |
| g02dnc | 2 | nag_correg_linregm_estfunc Estimate of an estimable function for a general linear regression model |
| g02eac | 7 | nag_correg_linregm_rssq Computes residual sums of squares for all possible linear regressions for a set of independent variables |
| g02ecc | 7 | nag_correg_linregm_rssq_stat Calculates and values from residual sums of squares |
| g02eec | 7 | nag_correg_linregm_fit_onestep Fits a linear regression model by forward selection |
| g02efc | 8 | nag_correg_linregm_fit_stepwise Stepwise linear regression |
| g02fac | 1 | nag_correg_linregm_stat_resinf Calculates standardized residuals and influence statistics |
| g02fcc | 7 | nag_correg_linregm_stat_durbwat Computes Durbin–Watson test statistic |
| g02gac | 4 | nag_correg_glm_normal Fits a generalized linear model with Normal errors |
| g02gbc | 4 | nag_correg_glm_binomial Fits a generalized linear model with binomial errors |
| g02gcc | 4 | nag_correg_glm_poisson Fits a generalized linear model with Poisson errors |
| g02gdc | 4 | nag_correg_glm_gamma Fits a generalized linear model with gamma errors |
| g02gkc | 4 | nag_correg_glm_constrain Estimates and standard errors of parameters of a general linear model for given constraints |
| g02gnc | 4 | nag_correg_glm_estfunc Estimable function and the standard error of a generalized linear model |
| g02gpc | 9 | nag_correg_glm_predict Computes a predicted value and its associated standard error based on a previously fitted generalized linear model |
| g02hac | 4 | nag_correg_robustm Robust regression, standard -estimates |
| g02hbc | 7 | nag_correg_robustm_wts Robust regression, compute weights for use with g02hdc |
| g02hdc | 7 | nag_correg_robustm_user Robust regression, compute regression with user-supplied functions and weights |
| g02hfc | 7 | nag_correg_robustm_user_varmat Robust regression, variance-covariance matrix following g02hdc |
| g02hkc | 4 | nag_correg_robustm_corr_huber Robust estimation of a covariance matrix, Huber's weight function |
| g02hlc | 7 | nag_correg_robustm_corr_user_deriv Calculates a robust estimation of a covariance matrix, user-supplied weight function plus derivatives |
| g02hmc | 7 | nag_correg_robustm_corr_user Calculates a robust estimation of a covariance matrix, user-supplied weight function |
| g02jfc | 27 | nag_correg_lmm_init Linear mixed effects regression, initialization function for g02jhc |
| g02jgc | 27 | nag_correg_lmm_init_combine Linear mixed effects regression, initialization function for g02jgc and g02jhc |
| g02jhc | 27 | nag_correg_lmm_fit Linear mixed effects regression using either Restricted Maximum Likelihood (REML) or Maximum Likelihood (ML) |
| g02kac | 9 | nag_correg_ridge_opt Ridge regression, optimizing a ridge regression parameter |
| g02kbc | 9 | nag_correg_ridge Ridge regression using a number of supplied ridge regression parameters |
| g02lac | 9 | nag_correg_pls_svd Partial least squares (PLS) regression using singular value decomposition |
| g02lbc | 9 | nag_correg_pls_wold Partial least squares (PLS) regression using Wold's iterative method |
| g02lcc | 9 | nag_correg_pls_fit PLS parameter estimates following partial least squares regression by g02lac or g02lbc |
| g02ldc | 9 | nag_correg_pls_pred PLS predictions based on parameter estimates from g02lcc |
| g02mac | 25 | nag_correg_lars Least angle regression (LARS), least absolute shrinkage and selection operator (LASSO) and forward stagewise regression |
| g02mbc | 25 | nag_correg_lars_xtx Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) and forward stagewise regression using the cross-products matrix |
| g02mcc | 25 | nag_correg_lars_param Calculates additional parameter estimates following Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) or forward stagewise regression |
| g02qfc | 23 | nag_correg_quantile_linreg_easy Linear quantile regression, simple interface, independent, identically distributed (IID) errors |
| g02qgc | 23 | nag_correg_quantile_linreg Linear quantile regression, comprehensive interface |
| g02zkc | 23 | nag_correg_optset Option setting function for g02qgc |
| g02zlc | 23 | nag_correg_optget Option getting function for g02qgc |
| g02jac | 8 (Deprecated) | nag_correg_mixeff_reml Linear mixed effects regression using Restricted Maximum Likelihood (REML) |
| g02jbc | 8 (Deprecated) | nag_correg_mixeff_ml Linear mixed effects regression using Maximum Likelihood (ML) |
| g02jcc | 9 (Deprecated) | nag_correg_mixeff_hier_init Hierarchical mixed effects regression, initialization function for g02jdc and g02jec |
| g02jdc | 9 (Deprecated) | nag_correg_mixeff_hier_reml Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML) |
| g02jec | 9 (Deprecated) | nag_correg_mixeff_hier_ml Hierarchical mixed effects regression using Maximum Likelihood (ML) |