2010
DOI: 10.18637/jss.v034.i02
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Beta Regression inR

Abstract: 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 ap… Show more

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Cited by 1,696 publications
(1,054 citation statements)
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References 30 publications
(44 reference statements)
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“…Because the response variable, percentage of introgression, is a real-valued variable bounded to the interval 0−100%, we used beta regression (Ferrari & Cribari-Neto 2004, Cribari-Neto & Zeileis 2010 with a logit link function. We considered all possible model combinations resulting from our 4 explanatory variables, resulting in 16 candidate models.…”
Section: Discussionmentioning
confidence: 99%
“…Because the response variable, percentage of introgression, is a real-valued variable bounded to the interval 0−100%, we used beta regression (Ferrari & Cribari-Neto 2004, Cribari-Neto & Zeileis 2010 with a logit link function. We considered all possible model combinations resulting from our 4 explanatory variables, resulting in 16 candidate models.…”
Section: Discussionmentioning
confidence: 99%
“…Density, biomass, and edge erosion stress measurements from The Netherlands were square root, third root, and log transformed, respectively, to satisfy the assumption of normality for ANOVAs. Beta regression analysis was applied to survivorship data in both sites, where ANOVA assumptions of normality could not be met (65).…”
Section: Methodsmentioning
confidence: 99%
“…To assess the significance of changes in necrotic leaf area, we modeled our response variable-the percentage of necrotic leaf area-using beta regression with a logit link function, an extension of generalized linear modeling (Ferrari and Cribari-Neto 2004), implemented in R in the 'betareg' package (Cribari-Neto and Zeileis 2010). This analysis method is robust to heteroscedasticity and unbalanced designs.…”
Section: Statistical Analysesmentioning
confidence: 99%