| Routine Name |
Mark of Introduction |
Purpose |
| 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) |
| 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 R2 and CP 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 |
| 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 |
| g02hac Example Text Example Data |
4 | nag_robust_m_regsn_estim Robust regression, standard M-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) |