R2_bayes

Bayesian version of R-squared for flexible regression models for continuous bounded or binomial responses

Functions to fit regression models for bounded (e.g., proportions and rates) and binomial responses. In case of bounded responses, available models are the flexible beta (Migliorati, S., Di Brisco, A. M., Ongaro, A. (2018) <doi:10.1214/17-BA1079>), the variance-inflated beta (Di Brisco, A. M., Migliorati, S., Ongaro, A. (2020) <doi:10.1177/1471082X18821213>), the beta (Ferrari, S.L.P., Cribari-Neto, F. (2004) <doi:10.1080/0266476042000214501>), and their augmented versions to handle the presence of zero/one values (Di Brisco, A. M., Migliorati, S. (2020) <doi:10.1002/sim.8406>). In case of binomial responses, available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial. Inference is dealt with a Bayesian approach based on the Hamiltonian Monte Carlo (HMC) algorithm (Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2014) <doi:10.1201/b16018>). Besides, functions to compute residuals, posterior predictives, goodness-of-fit measures, convergence diagnostics, and graphical representations are provided.

Roberto Ascari

FlexReg

Regression Models for Bounded and Binomial Responses

R2_bayes function

<dl><dt>model</dt>
<dd>an object of class <code>`flexreg`</code>, usually the result of <code>flexreg</code> or <code>flexreg_binom</code>.</dd></dl>

Arguments

Bayesian R-squared for flexreg Objects — R2_bayes

<dl>

<dt>model</dt>
<dd>an object of class <code>`flexreg`</code>, usually the result of <code>flexreg</code> or <code>flexreg_binom</code>.</dd>

</dl>

R2_bayes: Bayesian R-squared for flexreg Objects

Description

Usage

Arguments

Details

References

Examples