g02 Chapter Contents (PDF version)
g02 Chapter Introduction
NAG C Library Manual

NAG Library Chapter Contents

g02 – Correlation and Regression Analysis

g02 Chapter Introduction

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 k-factor structure to a real square matrix
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
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 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)
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
Quantile linear regression, simple interface, independent, identically distributed (IID) errors
g02qgc
Example Text
Example Data
Example Plot
23 nag_regsn_quant_linear
Quantile linear 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)

g02 Chapter Contents (PDF version)
g02 Chapter Introduction
NAG C Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2012