rVIB

The function generates random values from the variance-inflated beta distribution, or from the augmented variance-inflated beta 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

rVIB 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>mu</dt>
<dd>the mean parameter. It must lie in (0, 1).</dd>
<dt>phi</dt>
<dd>the precision parameter. It must be a real positive value.</dd>
<dt>p</dt>
<dd>the mixing weight. It must lie in (0, 1).</dd>
<dt>k</dt>
<dd>the extent of the variance inflation. It must lie in (0, 1).</dd>
<dt>q0</dt>
<dd>the probability of augmentation in zero. It must lie in (0, 1). In case of no augmentation, it is <code>NULL</code> (default).</dd>
<dt>q1</dt>
<dd>the probability of augmentation in one. It must lie in (0, 1). In case of no augmentation, it is <code>NULL</code> (default).</dd></dl>

Arguments

Random generation from the variance-inflated beta distribution — rVIB

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


<dt>phi</dt>
<dd>the precision parameter. It must be a real positive value.</dd>


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


<dt>k</dt>
<dd>the extent of the variance inflation. It must lie in (0, 1).</dd>


<dt>q0</dt>
<dd>the probability of augmentation in zero. It must lie in (0, 1). In case of no augmentation, it is <code>NULL</code> (default).</dd>


<dt>q1</dt>
<dd>the probability of augmentation in one. It must lie in (0, 1). In case of no augmentation, it is <code>NULL</code> (default).</dd>

</dl>

rVIB: Random generation from the variance-inflated beta distribution

Description

Usage

Value

Arguments

References

Examples