R Index Page
Keyword Index for the NAG Library Manual
NAG Library Manual

Keyword : Regression analysis

G02AAF   Computes the nearest correlation matrix to a real square matrix, using the method of Qi and Sun
G02ABF   Computes the nearest correlation matrix to a real square matrix, augmented G02AAF to incorporate weights and bounds
G02AEF   Computes the nearest correlation matrix with k-factor structure to a real square matrix
G02AJF   Computes the nearest correlation matrix to a real square matrix, using element-wise weighting
G02BAF   Pearson product-moment correlation coefficients, all variables, no missing values
G02BBF   Pearson product-moment correlation coefficients, all variables, casewise treatment of missing values
G02BCF   Pearson product-moment correlation coefficients, all variables, pairwise treatment of missing values
G02BDF   Correlation-like coefficients (about zero), all variables, no missing values
G02BEF   Correlation-like coefficients (about zero), all variables, casewise treatment of missing values
G02BFF   Correlation-like coefficients (about zero), all variables, pairwise treatment of missing values
G02BGF   Pearson product-moment correlation coefficients, subset of variables, no missing values
G02BHF   Pearson product-moment correlation coefficients, subset of variables, casewise treatment of missing values
G02BJF   Pearson product-moment correlation coefficients, subset of variables, pairwise treatment of missing values
G02BKF   Correlation-like coefficients (about zero), subset of variables, no missing values
G02BLF   Correlation-like coefficients (about zero), subset of variables, casewise treatment of missing values
G02BMF   Correlation-like coefficients (about zero), subset of variables, pairwise treatment of missing values
G02BNF   Kendall/Spearman non-parametric rank correlation coefficients, no missing values, overwriting input data
G02BPF   Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, overwriting input data
G02BQF   Kendall/Spearman non-parametric rank correlation coefficients, no missing values, preserving input data
G02BRF   Kendall/Spearman non-parametric rank correlation coefficients, casewise treatment of missing values, preserving input data
G02BSF   Kendall/Spearman non-parametric rank correlation coefficients, pairwise treatment of missing values
G02BTF   Update a weighted sum of squares matrix with a new observation
G02BUF   Computes a weighted sum of squares matrix
G02BWF   Computes a correlation matrix from a sum of squares matrix
G02BXF   Computes (optionally weighted) correlation and covariance matrices
G02BYF   Computes partial correlation/variance-covariance matrix from correlation/variance-covariance matrix computed by G02BXF
G02BZF   Combines two sums of squares matrices, for use after G02BUF
G02CAF   Simple linear regression with constant term, no missing values
G02CBF   Simple linear regression without constant term, no missing values
G02CCF   Simple linear regression with constant term, missing values
G02CDF   Simple linear regression without constant term, missing values
G02CEF   Service routine for multiple linear regression, select elements from vectors and matrices
G02CFF   Service routine for multiple linear regression, re-order elements of vectors and matrices
G02CGF   Multiple linear regression, from correlation coefficients, with constant term
G02CHF   Multiple linear regression, from correlation-like coefficients, without constant term
G02DAF   Fits a general (multiple) linear regression model
G02DCF   Add/delete an observation to/from a general linear regression model
G02DDF   Estimates of linear parameters and general linear regression model from updated model
G02DEF   Add a new independent variable to a general linear regression model
G02DFF   Delete an independent variable from a general linear regression model
G02DGF   Fits a general linear regression model to new dependent variable
G02DKF   Estimates and standard errors of parameters of a general linear regression model for given constraints
G02DNF   Computes estimable function of a general linear regression model and its standard error
G02EAF   Computes residual sums of squares for all possible linear regressions for a set of independent variables
G02ECF   Calculates R2 and CP values from residual sums of squares
G02EEF   Fits a linear regression model by forward selection
G02EFF   Stepwise linear regression
G02FAF   Calculates standardized residuals and influence statistics
G02FCF   Computes Durbin–Watson test statistic
G02GAF   Fits a generalized linear model with Normal errors
G02GBF   Fits a generalized linear model with binomial errors
G02GCF   Fits a generalized linear model with Poisson errors
G02GDF   Fits a generalized linear model with gamma errors
G02GKF   Estimates and standard errors of parameters of a general linear model for given constraints
G02GNF   Computes estimable function of a generalized linear model and its standard error
G02GPF   Computes a predicted value and its associated standard error based on a previously fitted generalized linear model
G02HAF   Robust regression, standard M-estimates
G02HBF   Robust regression, compute weights for use with G02HDF
G02HDF   Robust regression, compute regression with user-supplied functions and weights
G02HFF   Robust regression, variance-covariance matrix following G02HDF
G02HKF   Calculates a robust estimation of a correlation matrix, Huber's weight function
G02HLF   Calculates a robust estimation of a correlation matrix, user-supplied weight function plus derivatives
G02HMF   Calculates a robust estimation of a correlation matrix, user-supplied weight function
G02JAF   Linear mixed effects regression using Restricted Maximum Likelihood (REML)
G02JBF   Linear mixed effects regression using Maximum Likelihood (ML)
G02JCF   Hierarchical mixed effects regression, initialization routine for G02JDF and G02JEF
G02JDF   Hierarchical mixed effects regression using Restricted Maximum Likelihood (REML)
G02JEF   Hierarchical mixed effects regression using Maximum Likelihood (ML)
G02KAF   Ridge regression, optimizing a ridge regression parameter
G02KBF   Ridge regression using a number of supplied ridge regression parameters
G02LAF   Partial least squares (PLS) regression using singular value decomposition
G02LBF   Partial least squares (PLS) regression using Wold's iterative method
G02LCF   PLS parameter estimates following partial least squares regression by G02LAF or G02LBF
G02LDF   PLS predictions based on parameter estimates from G02LCF
G02QFF   Linear quantile regression, simple interface, independent, identically distributed (IID) errors
G02QGF   Linear quantile regression, comprehensive interface
G02ZKF   Option setting routine for G02QGF
G02ZLF   Option getting routine for G02QGF

R Index Page
Keyword Index for the NAG Library Manual
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford UK. 2013