FNLStat : Regression
Regression
Routines
2.1 Simple Linear Regression
Straight line fit RLINE
Simple linear regression analysis RONE
Response control by a fitted line RINCF
Inverse prediction by a fitted line RINPF
2.2 Multivariate General Linear Model Analysis
2.2.1 Model Fitting
From raw data for a single dependent variable RLSE
From covariances RCOV
From raw data without classification variables RGIVN
From raw data with classification variables RGLM
With linear equality restrictions RLEQU
2.2.2 Statistical Inference and Diagnostics
Summary statistics for a fitted regression RSTAT
Variance-covariance matrix of the estimated coefficients RCOVB
Construction of a completely testable hypothesis CESTI
Sums of crossproducts for a multivariate hypothesis RHPSS
Tests for the multivariate linear hypothesis RHPTE
Test for lack of fit based on exact replicates RLOFE
Test for lack of fit based on near replicates RLOFN
Intervals and diagnostics for individual cases RCASE
Diagnostics for outliers and influential cases ROTIN
2.2.3 Utilities for Classification Variables
Getting unique values of classification variables GCLAS
Generation of regressors for a general linear model GRGLM
2.3 Variable Selection
All best regressions via leaps-and-bounds algorithm RBEST
Stepwise regression RSTEP
Generalized sweep of a nonnegative definite matrix GSWEP
Retrieval of a symmetric submatrix from a symmetric matrix RSUBM
2.4 Polynomial Regression and Second-Order Models
2.4.1 Polynomial Regression Analysis
Polynomial fit of known degree RCURV
Polynomial regression analysis RPOLY
2.4.2 Second-Order Model Design
Generation of an orthogonal central composite design RCOMP
2.4.3 Utility Routines for Polynomial Models and Second-Order Models
Polynomial regression fit RFORP
Summary statistics for a fitted polynomial model RSTAP
Case statistics for a fitted polynomial model RCASP
Generation of orthogonal polynomials OPOLY
Centering of variables and generation of crossproducts GCSCP
Transforming coefficients for a second order model TCSCP
2.5 Nonlinear Regression Analysis
Nonlinear regression fit RNLIN
2.6 Fitting Linear Models Based on Alternative Criteria
Least absolute value regression RLAV
Least Lp norm regression RLLP
Least maximum value regression RLMV
Partial Least Squares Regression PLSR
Published date: 03/19/2020
Last modified date: 03/19/2020