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If there was no evidence of autocorrelation, the data were evaluated using a beta regression (Simas and Rocha 2010) with proportion wild as the dependent variable and year as the independent variable.
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Basu and Manca used a beta regression model to analyze QALY data and examined raw scale residuals to evaluate goodness of fit [ 3].
Basu & Manca (2012) use a bayesian form of a beta regression (using non informative priors) in a non-mapping context and compared with OLS regression [ 23].
We used a Bayesian beta regression framework to model the proportion of time spent inactive, walking and travelling at vehicle speeds, relative to street connectivity and other environmental attributes measured within a radius of home.
To perform regression using proportions, beta regression was used as described by Ferrari and Cribari-Neto using MATLAB.
We also conducted a second sensitivity analysis using beta regression for each % arsenic species (divided by 100) since these biomarkers are proportion data [ 51] and Dirichlet regression, a multivariate modification of the beta regression that models all % arsenic species as a set that must sum to one [ 52].
Models were tested using beta regression analysis in r with a loglog link and beta distribution error structure.
The best fits are obtained by using GLM Gamma and beta regression.
We have also examined whether there have been changes over time in the fractional contribution of ARs to seasonal rainfall using zero-inflated beta regression.
Percentages were analyzed using beta regression [ 41].
Survival rates were compared using beta regression (Erkut et al., 2013).
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