print.flexreg

Functions to fit regression models for bounded continuous and discrete responses. In case of bounded continuous responses (e.g., proportions and rates), 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>) are implemented. In case of bounded discrete responses (e.g., bounded counts, such as the number of successes in n trials), available models are the flexible beta-binomial (Ascari, R., Migliorati, S. (2021) <doi:10.1002/sim.9005>), the beta-binomial, and the binomial are implemented. 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 Continuous and Discrete Responses

Sonia Migliorati

Andrea Ongaro

print.flexreg function

<dl><dt>x</dt>
<dd>an object of class <code>`flexreg`</code>.</dd>
<dt>...</dt>
<dd>additional arguments. Currently not used.</dd></dl>

Arguments

Print Methods for flexreg Objects — print.flexreg

<dl>

<dt>x</dt>
<dd>an object of class <code>`flexreg`</code>.</dd>


<dt>...</dt>
<dd>additional arguments. Currently not used.</dd>

</dl>

print.flexreg: Print Methods for flexreg Objects

Description

Usage

Arguments