Package: SGB 1.0.1.1

SGB: Simplicial Generalized Beta Regression

Main properties and regression procedures using a generalization of the Dirichlet distribution called Simplicial Generalized Beta distribution. It is a new distribution on the simplex (i.e. on the space of compositions or positive vectors with sum of components equal to 1). The Dirichlet distribution can be constructed from a random vector of independent Gamma variables divided by their sum. The SGB follows the same construction with generalized Gamma instead of Gamma variables. The Dirichlet exponents are supplemented by an overall shape parameter and a vector of scales. The scale vector is itself a composition and can be modeled with auxiliary variables through a log-ratio transformation. Graf, M. (2017, ISBN: 978-84-947240-0-8). See also the vignette enclosed in the package.

Authors:Monique Graf

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SGB.pdf |SGB.html✨
SGB/json (API)

# Install 'SGB' in R:
install.packages('SGB', repos = c('https://grafmoni.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3.00 score 2 scripts 152 downloads 10 mentions 42 exports 4 dependencies

Last updated 11 months agofrom:b43901ad45. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 01 2024
R-4.5-winOKNov 01 2024
R-4.5-linuxOKNov 01 2024
R-4.4-winOKNov 01 2024
R-4.4-macOKNov 01 2024
R-4.3-winOKNov 01 2024
R-4.3-macOKNov 01 2024

Exports:B2ibvalcoefmatcompushape2condshape2covest.SGBcvm.SGBdggammadSGBEZ.SGBfn.SGBgr.SGBheqa.SGBheqa.SGB.jacheqab.SGBheqab.SGB.jacheqb.SGBheqb.SGB.jachin.SGBhin.SGB.jachzbetaimpute.regSGBinitpar.SGBks.SGBMeanA.SGBMeanAobj.SGBModeA.SGBModeAobj.SGBprint.regSGBprint.testSGBpzbetaqzbetaregSGBregSGB.defaultregSGB.formularggammarSGBstepSGBstepSGB.defaultsummary.regSGBtable.regSGBzval

Dependencies:alabamaFormulaMASSnumDeriv

SGB multivariate regression

Rendered fromvignette.Rnwusingknitr::knitron Nov 01 2024.

Last update: 2019-05-13
Started: 2019-05-13

Readme and manuals

Help Manual

Help pageTopics
Package SGBSGB-package SGB
arc datasetarc
Balances to isometric log-ratioB2i
carseg datasetcarseg
Classical and robust asymptotic covariance matrixcovest.SGB
Equality constraints for overall shape and/or regression parameters and jacobianEqualityConstr heqa.SGB heqa.SGB.jac heqab.SGB heqab.SGB.jac heqb.SGB heqb.SGB.jac
Expectations of Z under the SGB distributionEZ.SGB
Generalized Gamma distributiondggamma GenGammaDistrib rggamma
Goodness of fit tests on the marginal distributions of each part in a SGB modelcvm.SGB GoodnessFit ks.SGB print.testSGB
Imputation of missing parts in compositions from a SGB modelImputation impute.regSGB
Inequality constraints and jacobianhin.SGB hin.SGB.jac InequalityConstr
Initial parameters estimates and comparisoncompushape2 condshape2 InitialParameters initpar.SGB
Histograms, quantile and probability plots for the z(u)-transforms of partshzbeta MarginPlots pzbeta qzbeta
ocar data setocar
oilr data setoilr
Regression for compositions following a SGB distributionprint.regSGB regSGB regSGB.default regSGB.formula summary.regSGB
Density and random generator for the SGB distributiondSGB rSGB SGBdistrib
SGB log-likelihood and gradientfn.SGB gr.SGB SGBLik
Computation of scales and z-vectorsbval SGButil zval
Stepwise backward elimination for SGB regressionstepSGB stepSGB.default
Aitchison expectation and mode under the SGB distributionMeanA.SGB MeanAobj.SGB ModeA.SGB ModeAobj.SGB summaryA.SGB
Tabulation of overall SGB regression results with AIC and matrix view of regression coefficientscoefmat table.regSGB Tabulation