Beta Regression in R

By Francisco Cribari‐Neto, Achim Zeileis. 2010

2,543 citations recorded by the source corpus.

The class of beta regression models is commonly used by practitioners to model variables that assume values in the standard unit interval (0, 1). It is based on the assumption that the dependent variable is beta-distributed and that its mean is related to a set of regressors through a linear predictor with unknown coefficients and a link function. The model also includes a precision parameter which may be constant or depend on a (potentially different) set of regressors through a link function as well. This approach naturally incorporates features such as heteroskedasticity or skewness which are commonly observed in data taking values in the standard unit interval, such as rates or proportions. This paper describes the betareg package which provides the class of beta regressions in the R system for statistical computing. The underlying theory is briefly outlined, the implementation discussed and illustrated in various replication exercises.

Subfields: Plant Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/39129add37dcc96b2ba9b1a3dff05ee684289a93

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

In Statistics and Probability

Citations in the corpus

A quantile parametric mixed regression model for bounded response variables (2017, methodology): “The beta regression model is sufficiently documented in several publications such as [8], [9], [12], and [6] and in several applications like in [22] and [37].”
Climate change in the Eastern Amazon: crop-pollinator and occurrence-restricted bees are potentially more affected (2020, methodology): “The betareg function in the betareg package (Cribari-Neto and Zeileis 2010) in R was used to model the potential loss of occurrence area in response to species traits.”
Homophony and morphology: The acoustics of word-final S in English 1 (2015, methodology): “We used the R package betareg (Cribari-Neto & Zeileis 2010) for this analysis.”
A New Robust Regression Model for Proportions (2012, neutral): “…International Society for Bayesian Analysis DOI:10.1214/12-BA728 is sufficiently documented by several publications such as Espinheira et al. (2008a,b); Ferrari et al. (2011); Cribari-Neto and Zeileis (2010) and several applications as, for example, Kelly et al. (2007) and Wallis et al. (2009).”
Various Versatile Variances: An Object-Oriented Implementation of Clustered Covariances in R (2020, methodology): “, for packages MASS (Venables and Ripley 2002), pscl/countreg (Zeileis, Kleiber, and Jackman 2008), and betareg (Cribari-Neto and Zeileis 2010; Grün, Kosmidis, and Zeileis 2012), among many others.”
Diagnostic tools in beta regression with varying dispersion (2011, methodology): “Practitioners can use the betareg package, which is available for the R statistical software (http://www.r-project.org), for fitting beta regressions; see Cribari-Neto and Zeileis (2010).”
Environmental conditions and prey-switching by a seabird predator impact juvenile salmon survival (2017, methodology): “…þ Upwelling in Marchþ Upwelling in Marchð Þ2 þ Interaction½ln Rockfish CPUEþ 1ð Þ ln Common Murre populationþ 1ð Þ : Because response variables in our regressions were proportions, we used beta-regression (Cribari-Neto and Zeilis, 2010) with a logit link to fitmodels in ProgramR (RCore Team, 2015).”
Characterizing forest structural types and shelterwood dynamics from Lorenz-based indicators predicted by airborne laser scanning (2013, methodology): “The implementation of the method was carried out using the betareg package (Cribari-Neto and Zeileis, 2010) in the R environment (version 2.”
Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees (2016, methodology): “…the real line R, xi xi1, … … …, xik T is a vector of k explanatory variables, 1, … … …, k T is a vector of k unknown regression parameters (k n), and i is a linear predictor (i.e., i = 1xi1 + … + kxik, usually xi1 = 1 for all i so that the model has an intercept) (Cribari-Neto and Zeileis 2010).”
Beta regression for time series analysis of bounded data, with application to Canada Google Flu Trends. (2014, methodology): “See Kieschnick and McCullough (2003) and Cribari-Neto and Zeileis (2010) for detailed discussions.”
Demographics and Dynamics of Mechanical Turk Workers (2018, methodology): “Therefore, to examine if there is a correlation of the propensities with the demographic variables, we use a Beta regression [11, 31], which is explicitly designed to handle dependent variables that are Beta distributed and exhibit heteroskedasticity and skewness.”
The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation (2012, methodology): “…context of the Bayesian approach (van Dorp and Mazzuchi 2004), and to develop and estimate regression models (Cribari-Neto and Souza Forthcoming; Cribari-Neto and Zeileis 2010; Espinheira et al 2008a,b; Ferrari and Cribari-Neto 2004; Ferrari et al. 2011; Kieschnick and McCullough 2003; Ospina…”
Half-Normal Plots and Overdispersed Models in R: The hnp Package (2017, methodology): “Package betareg includes a plot method (argument which = 5) that produces a half-normal plot with simulated envelope for residuals of a beta regression model (Cribari-Neto and Zeileis 2010).”