rFBB

The function generates random values from the flexible beta-binomial distribution.

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

rFBB function

<dl><dt>n</dt>
<dd>the number of values to generate. If <code>length(n) &gt; 1</code>, the length is taken to be the number required.</dd>
<dt>size</dt>
<dd>the total number of trials.</dd>
<dt>mu</dt>
<dd>the mean parameter. It must lie in (0, 1).</dd>
<dt>theta</dt>
<dd>the overdispersion parameter. It must lie in (0, 1).</dd>
<dt>phi</dt>
<dd>the precision parameter, an alternative way to specify the overdispersion parameter <code>theta</code>. It must be a real positive value.</dd>
<dt>p</dt>
<dd>the mixing weight. It must lie in (0, 1).</dd>
<dt>w</dt>
<dd>the normalized distance among clusters. It must lie in (0, 1).</dd></dl>

Arguments

Random generator from the flexible beta-binomial distribution — rFBB

<dl>

<dt>n</dt>
<dd>the number of values to generate. If <code>length(n) &gt; 1</code>, the length is taken to be the number required.</dd>


<dt>size</dt>
<dd>the total number of trials.</dd>


<dt>mu</dt>
<dd>the mean parameter. It must lie in (0, 1).</dd>


<dt>theta</dt>
<dd>the overdispersion parameter. It must lie in (0, 1).</dd>


<dt>phi</dt>
<dd>the precision parameter, an alternative way to specify the overdispersion parameter <code>theta</code>. It must be a real positive value.</dd>


<dt>p</dt>
<dd>the mixing weight. It must lie in (0, 1).</dd>


<dt>w</dt>
<dd>the normalized distance among clusters. It must lie in (0, 1).</dd>

</dl>

rFBB: Random generator from the flexible beta-binomial distribution

Description

Usage

Value

Arguments

References

Examples