| Function Name |
Mark of Introduction |
Purpose |
| g02aac
Example Text |
9 | nag_nearest_correlation Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun |
| g02abc
Example Text Example Data |
23 | nag_nearest_correlation_bounded Computes the nearest correlation matrix to a real square matrix, augmented nag_nearest_correlation (g02aac) to incorporate weights and bounds |
| g02aec
Example Text Example Data |
23 | nag_nearest_correlation_k_factor Computes the nearest correlation matrix with -factor structure to a real square matrix |
| g02ajc
Example Text Example Data |
24 | nag_nearest_correlation_h_weight Computes the nearest correlation matrix to a real square matrix, using element-wise weighting |
| g02brc
Example Text Example Data |
3 | nag_ken_spe_corr_coeff Kendall and/or Spearman non-parametric rank correlation coefficients, allows variables and observations to be selectively disregarded |
| g02btc
Example Text Example Data |
7 | nag_sum_sqs_update Update a weighted sum of squares matrix with a new observation |
| g02buc
Example Text Example Data |
7 | nag_sum_sqs Computes a weighted sum of squares matrix |
| g02bwc
Example Text Example Data |
7 | nag_cov_to_corr Computes a correlation matrix from a sum of squares matrix |
| g02bxc
Example Text Example Data |
3 | nag_corr_cov Product-moment correlation, unweighted/weighted correlation and covariance matrix, allows variables to be disregarded |
| g02byc
Example Text Example Data |
6 | nag_partial_corr Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by nag_corr_cov (g02bxc) |
| g02bzc
Example Text Example Data |
24 | nag_sum_sqs_combine Combines two sums of squares matrices, for use after nag_sum_sqs (g02buc) |
| g02cac
Example Text Example Data |
3 | nag_simple_linear_regression Simple linear regression with or without a constant term, data may be weighted |
| g02cbc
Example Text Example Data |
3 | nag_regress_confid_interval Simple linear regression confidence intervals for the regression line and individual points |
| g02dac
Example Text Example Data |
1 | nag_regsn_mult_linear Fits a general (multiple) linear regression model |
| g02dcc
Example Text Example Data |
2 | nag_regsn_mult_linear_addrem_obs Add/delete an observation to/from a general linear regression model |
| g02ddc
Example Text Example Data |
2 | nag_regsn_mult_linear_upd_model Estimates of regression parameters from an updated model |
| g02dec
Example Text Example Data |
2 | nag_regsn_mult_linear_add_var Add a new independent variable to a general linear regression model |
| g02dfc
Example Text Example Data |
2 | nag_regsn_mult_linear_delete_var Delete an independent variable from a general linear regression model |
| g02dgc
Example Text Example Data |
1 | nag_regsn_mult_linear_newyvar Fits a general linear regression model to new dependent variable |
| g02dkc
Example Text Example Data |
2 | nag_regsn_mult_linear_tran_model Estimates of parameters of a general linear regression model for given constraints |
| g02dnc
Example Text Example Data |
2 | nag_regsn_mult_linear_est_func Estimate of an estimable function for a general linear regression model |
| g02eac
Example Text Example Data |
7 | nag_all_regsn Computes residual sums of squares for all possible linear regressions for a set of independent variables |
| g02ecc
Example Text Example Data |
7 | nag_cp_stat Calculates and values from residual sums of squares |
| g02eec
Example Text Example Data |
7 | nag_step_regsn Fits a linear regression model by forward selection |
| g02efc
Example Text Example Data |
8 | nag_full_step_regsn Stepwise linear regression |
| g02ewc | 8 | nag_full_step_regsn_monit Monitor function for full stepwise regression Note: this function is scheduled for withdrawal at Mark 25, see Advice on Replacement Calls for Withdrawn/Superseded Functions for further information. |
| g02fac
Example Text Example Data |
1 | nag_regsn_std_resid_influence Calculates standardized residuals and influence statistics |
| g02fcc
Example Text Example Data |
7 | nag_durbin_watson_stat Computes Durbin–Watson test statistic |
| g02gac
Example Text Example Data |
4 | nag_glm_normal Fits a generalized linear model with Normal errors |
| g02gbc
Example Text Example Data |
4 | nag_glm_binomial Fits a generalized linear model with binomial errors |
| g02gcc
Example Text Example Data |
4 | nag_glm_poisson Fits a generalized linear model with Poisson errors |
| g02gdc
Example Text Example Data |
4 | nag_glm_gamma Fits a generalized linear model with gamma errors |
| g02gkc
Example Text Example Data |
4 | nag_glm_tran_model Estimates and standard errors of parameters of a general linear model for given constraints |
| g02gnc
Example Text Example Data |
4 | nag_glm_est_func Estimable function and the standard error of a generalized linear model |
| g02gpc
Example Text Example Data |
9 | nag_glm_predict Computes a predicted value and its associated standard error based on a previously fitted generalized linear model |
| g02hac
Example Text Example Data |
4 | nag_robust_m_regsn_estim Robust regression, standard -estimates |
| g02hbc
Example Text Example Data |
7 | nag_robust_m_regsn_wts Robust regression, compute weights for use with nag_robust_m_regsn_user_fn (g02hdc) |
| g02hdc
Example Text Example Data |
7 | nag_robust_m_regsn_user_fn Robust regression, compute regression with user-supplied functions and weights |
| g02hfc
Example Text Example Data |
7 | nag_robust_m_regsn_param_var Robust regression, variance-covariance matrix following nag_robust_m_regsn_user_fn (g02hdc) |
| g02hkc
Example Text Example Data |
4 | nag_robust_corr_estim Robust estimation of a correlation matrix, Huber's weight function |
| g02hlc
Example Text Example Data |
7 | nag_robust_m_corr_user_fn Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives |
| g02hmc
Example Text Example Data |
7 | nag_robust_m_corr_user_fn_no_derr Calculates a robust estimation of a correlation matrix, user-supplied weight function |
| g02jac
Example Text Example Data |
8 | nag_reml_mixed_regsn Linear mixed effects regression using Restricted Maximum Likelihood (REML) |
| g02jbc
Example Text Example Data |
8 | nag_ml_mixed_regsn Linear mixed effects regression using Maximum Likelihood (ML) |
| g02jcc | 9 | nag_hier_mixed_init Hierarchical mixed effects regression, initialization function for nag_reml_hier_mixed_regsn (g02jdc) and nag_ml_hier_mixed_regsn (g02jec) |
| g02jdc
Example Text Example Data |
9 | nag_reml_hier_mixed_regsn Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML) |
| g02jec
Example Text Example Data |
9 | nag_ml_hier_mixed_regsn Hierarchical mixed effects regression using Maximum Likelihood (ML) |
| g02kac
Example Text Example Data |
9 | nag_regsn_ridge_opt Ridge regression, optimizing a ridge regression parameter |
| g02kbc
Example Text Example Data |
9 | nag_regsn_ridge Ridge regression using a number of supplied ridge regression parameters |
| g02lac
Example Text Example Data |
9 | nag_pls_orth_scores_svd Partial least squares (PLS) regression using singular value decomposition |
| g02lbc
Example Text Example Data |
9 | nag_pls_orth_scores_wold Partial least squares (PLS) regression using Wold's iterative method |
| g02lcc
Example Text Example Data |
9 | nag_pls_orth_scores_fit PLS parameter estimates following partial least squares regression by nag_pls_orth_scores_svd (g02lac) or nag_pls_orth_scores_wold (g02lbc) |
| g02ldc
Example Text Example Data |
9 | nag_pls_orth_scores_pred PLS predictions based on parameter estimates from nag_pls_orth_scores_fit (g02lcc) |
| g02qfc
Example Text Example Data Example Plot |
23 | nag_regsn_quant_linear_iid Linear quantile regression, simple interface, independent, identically distributed (IID) errors |
| g02qgc
Example Text Example Data Example Plot |
23 | nag_regsn_quant_linear Linear quantile regression, comprehensive interface |
| g02zkc | 23 | nag_g02_opt_set Option setting function for nag_regsn_quant_linear (g02qgc) |
| g02zlc | 23 | nag_g02_opt_get Option getting function for nag_regsn_quant_linear (g02qgc) |