The VGAM Package for Categorical Data Analysis

By Thomas W. Yee. 2010

627 citations recorded by the source corpus.

Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such as reduced-rank VGLMs for dimension reduction, and allowing covariates that have values specific to each linear/additive predictor, e.g., for consumer choice modeling. This article describes some of the framework behind the VGAM R package, its usage and implementation details.

Subfields: Economics and Econometrics, Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4cd49db46dc39b70ee6c00bcf5425f43a591e5d0

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Citations in the corpus

Total loss estimation using copula-based regression models (2012, background): “pproach does not depend on the particular choice of the marginal distribution or copula family, and it can be extended to other parametric distributions and families (see e.g. Yee and Wild (1996) and Yee (2010) for an overview on appropriate marginal distributions). Moreover, in the case of higher-d”
mvabund– an R package for model‐based analysis of multivariate abundance data (2012, methodology): “approaches to the analysis of multivariate abundance data in ecology (Yee 2010; Ives & Helmus 2011; Ovaskainen & Soininen 2011).”
On the Folded Normal Distribution (2014, methodology): “The R-package VGAM[16] offers algorithms for obtaining maximum likelihood estimates of the folded normal, but we have not used it here.”
Multivariate Regression Analysis for the Item Count Technique (2011, methodology): “For fitting the weighted beta-binomial regression, I use the algorithm as implemented in the VGAM package (Yee and Hastie 2003; Yee 2010).”
Ordinal Regression Analysis: Using Generalized Ordinal Logistic Regression Models to Estimate Educational Data (2012, positive): “The latest VGAM package (v. 0.8.3) for R (Yee, 2010, 2011) includes the functions of fitting the nonproportional odds and the partial proportional odds models, which is similar to Stata gologit and gologit2.”
Distance‐based multivariate analyses confound location and dispersion effects (2012, methodology): “…of ‘constrained ordination’, where the ordination axes are functions of environmental variables, has been proposed (Yee 2006) and software is currently available in the R package VGAM (Yee 2010) which can handle the most common forms of mean–variance relationship that are observed in practice.”
The estimation of item response models with the lmer function from the lme4 package in R (2011, methodology): “First, models for ordered categories or nominal categories, with a number of categories larger than two, rely on multivariate extensions of the GLMM framework (Fahrmeir and Tutz 2001), also called vector GLMMs (Yee and Wild 1996; Yee 2010).”
Native and alien floras in urban habitats: a comparison across 32 cities of central Europe (2012, methodology): “8-1 package (Yee, 2010) of R, with multinomial family and the proportion of native species as common denominator.”
Estimation of a semiparametric recursive bivariate probit model in the presence of endogeneity (2011, background): “This is crucial given that √ W and W−1 (via z), obtained by eigen- decomposition, are needed in (10); Yee (2010) found the same result in the context of vector categorical data models.”
Half-Normal Plots and Overdispersed Models in R: The hnp Package (2017, methodology): “Package mgcv’s (Wood 2006) function rig was used for inverse Gaussian models; package gamlss’s (Rigby and Stasinopoulos 2005; Stasinopoulos and Rigby 2007) functions rBB, rZIBI and rZIBB were used for beta-binomial, zero-inflated binomial and zero-inflated beta-binomial models fitted using gamlss; p”
Ordinal regression: A review and a taxonomy of models (2021, methodology): “The VGAM package (Yee, 2010) allows to fit multinomial and ordinal additive models.”
Sparse and Compositionally Robust Inference of Microbial Ecological Networks (2014, methodology): “In practice, SPIEC-EASI relies on routines from base R and VGAM packages [54, 55] to estimate the uniform quantiles of the normal data and to fit the desired CDF with estimated parameters.”
Reversed graph embedding resolves complex single-cell developmental trajectories (2017, background): “Trajectory-conditioned test can also run by considering both of the pseudotime and the grouping, similarly to the previously deﬁned BEAM test, where the full model is In each of the model, E ( Y ) represents the expected value for the transcript counts data Y where Y is negative binomial distributed”