6.29 G02 Correlation and Regression Analysis

g02 - Correlation and Regression Analysis

g02 Chapter Introduction

Routine Name Mark of Introduction Purpose
g02aac 9 nag_nearest_correlation
Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun
g02abc 23 nag_nearest_correlation (g02aac) to incorporate weights and bounds
g02aec 23 nag_nearest_correlation_k_factor
Computes the nearest correlation matrix with k-factor structure to a real square matrix
g02ajc 24 nag_nearest_correlation_h_weight
Computes the nearest correlation matrix to a real square matrix, using element-wise weighting
g02anc 25 nag_nearest_correlation_shrinking
Computes a correlation matrix from an approximate matrix with fixed submatrix
g02brc 3 nag_ken_spe_corr_coeff
Kendall and/or Spearman non-parametric rank correlation coefficients, allows variables and observations to be selectively disregarded
g02btc 7 nag_sum_sqs_update
Update a weighted sum of squares matrix with a new observation
g02buc 7 nag_sum_sqs
Computes a weighted sum of squares matrix
g02bwc 7 nag_cov_to_corr
Computes a correlation matrix from a sum of squares matrix
g02bxc 3 nag_corr_cov
Product-moment correlation, unweighted/weighted correlation and covariance matrix, allows variables to be disregarded
g02byc 6 nag_partial_corr
Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by g02bxc
g02bzc 24 nag_sum_sqs_combine
Combines two sums of squares matrices, for use after nag_sum_sqs (g02buc)
g02cac 3 nag_simple_linear_regression
Simple linear regression with or without a constant term, data may be weighted
g02cbc 3 nag_regress_confid_interval
Simple linear regression confidence intervals for the regression line and individual points
g02dac 1 nag_regsn_mult_linear
Fits a general (multiple) linear regression model
g02dcc 2 nag_regsn_mult_linear_addrem_obs
Add/delete an observation to/from a general linear regression model
g02ddc 2 nag_regsn_mult_linear_upd_model
Estimates of regression parameters from an updated model
g02dec 2 nag_regsn_mult_linear_add_var
Add a new independent variable to a general linear regression model
g02dfc 2 nag_regsn_mult_linear_delete_var
Delete an independent variable from a general linear regression model
g02dgc 1 nag_regsn_mult_linear_newyvar
Fits a general linear regression model to new dependent variable
g02dkc 2 nag_regsn_mult_linear_tran_model
Estimates of parameters of a general linear regression model for given constraints
g02dnc 2 nag_regsn_mult_linear_est_func
Estimate of an estimable function for a general linear regression model
g02eac 7 nag_all_regsn
Computes residual sums of squares for all possible linear regressions for a set of independent variables
g02ecc 7 nag_cp_stat
Calculates R2 and CP values from residual sums of squares
g02eec 7 nag_step_regsn
Fits a linear regression model by forward selection
g02efc 8 nag_full_step_regsn
Stepwise linear regression
g02ewc 8 nag_full_step_regsn_monit
Monitor function for full stepwise regression
g02fac 1 nag_regsn_std_resid_influence
Calculates standardized residuals and influence statistics
g02fcc 7 nag_durbin_watson_stat
Computes Durbin-Watson test statistic
g02gac 4 nag_glm_normal
Fits a generalized linear model with Normal errors
g02gbc 4 nag_glm_binomial
Fits a generalized linear model with binomial errors
g02gcc 4 nag_glm_poisson
Fits a generalized linear model with Poisson errors
g02gdc 4 nag_glm_gamma
Fits a generalized linear model with gamma errors
g02gkc 4 nag_glm_tran_model
Estimates and standard errors of parameters of a general linear model for given constraints
g02gnc 4 nag_glm_est_func
Estimable function and the standard error of a generalized linear model
g02gpc 9 nag_glm_predict
Computes a predicted value and its associated standard error based on a previously fitted generalized linear model.
g02hac 4 nag_robust_m_regsn_estim
Robust regression, standard M-estimates
g02hbc 7 nag_robust_m_regsn_wts
Robust regression, compute weights for use with g02hdc
g02hdc 7 nag_robust_m_regsn_user_fn
Robust regression, compute regression with user-supplied functions and weights
g02hfc 7 nag_robust_m_regsn_param_var
Robust regression, variance-covariance matrix following g02hdc
g02hkc 4 nag_robust_corr_estim
Robust estimation of a correlation matrix, Huber's weight function
g02hlc 7 nag_robust_m_corr_user_fn
Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
g02hmc 7 nag_robust_m_corr_user_fn_no_derr
Calculates a robust estimation of a correlation matrix, user-supplied weight function
g02jac 8 nag_reml_mixed_regsn
Linear mixed effects regression using Restricted Maximum Likelihood (REML)
g02jbc 8 nag_ml_mixed_regsn
Linear mixed effects regression using Maximum Likelihood (ML)
g02jcc 9 nag_hier_mixed_init
Hierarchical mixed effects regression, initialization
g02jdc 9 nag_reml_hier_mixed_regsn
Hierarchical mixed effects regression using restricted maximum likelihood
g02jec 9 nag_ml_hier_mixed_regsn
Hierarchical mixed effects regression using maximum likelihood
g02kac 9 nag_regsn_ridge_opt
Ridge regression, optimizing a ridge regression parameter
g02kbc 9 nag_regsn_ridge
Ridge regression using a number of supplied ridge regression parameters
g02lac 9 nag_pls_orth_scores_svd
Partial least-squares (PLS) regression using singular value decomposition
g02lbc 9 nag_pls_orth_scores_wold
Partial least-squares (PLS) regression using Wold's iterative method
g02lcc 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 9 nag_pls_orth_scores_pred
PLS predictions based on parameter estimates from nag_pls_orth_scores_fit (g02lcc)
g02mac 25 nag_lars
Least angle regression (LARS), least absolute shrinkage and selection operator (LASSO) and forward stagewise regression
g02mbc 25 nag_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_lars_param
Additional parameter calculate following Least Angle Regression (LARS), Least Absolute Shrinkage and Selection Operator (LASSO) or forward stagewise regression
g02qfc 23 nag_regsn_quant_linear_iid
Linear quantile regression, simple interface, independent, identically distributed (IID) errors
g02qgc 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 setting function for nag_regsn_quant_linear (g02qgc)