plot_pmf

Plot the empirical probability mass function, i.e., the proportion of
data values <code>y</code> that equal <code>j</code> for each <code>j=0,1,...</code>,
together with the model-based estimate of the probability mass function
based on the posterior predictive distribution.

For Bayesian and classical inference and prediction with count-valued data,
Simultaneous Transformation and Rounding (STAR) Models provide a flexible, interpretable,
and easy-to-use approach. STAR models the observed count data using a rounded
continuous data model and incorporates a transformation for greater flexibility.
Implicitly, STAR formalizes the commonly-applied yet incoherent procedure of
(i) transforming count-valued data and subsequently
(ii) modeling the transformed data using Gaussian models.
STAR is well-defined for count-valued data, which is reflected in predictive accuracy,
and is designed to account for zero-inflation, bounded or censored data, and over- or underdispersion.
Importantly, STAR is easy to combine with existing MCMC or point estimation
methods for continuous data, which allows seamless adaptation of continuous data
models (such as linear regressions, additive models, BART, random forests,
and gradient boosting machines) for count-valued data. The package also includes several
methods for modeling count time series data, namely via warped Dynamic Linear Models.
For more details and background on these methodologies, see the works of
Kowal and Canale (2020) <doi:10.1214/20-EJS1707>,
Kowal and Wu (2022) <doi:10.1111/biom.13617>,
King and Kowal (2022) <arXiv:2110.14790>, and
Kowal and Wu (2023) <arXiv:2110.12316>.

Brian King

countSTAR

Flexible Modeling of Count Data

Dan Kowal

plot_pmf function

<dl><dt>y</dt>
<dd><code>n x 1</code> vector of data</dd>
<dt>post.pred</dt>
<dd><code>nsave</code> draws from the posterior predictive distribution of <code>y</code></dd>
<dt>error.bars</dt>
<dd>logical; if TRUE, include errors bars on the model-based PMF</dd>
<dt>alpha</dt>
<dd>confidence level for the credible intervals</dd></dl>

Arguments

Plot the empirical and model-based probability mass functions — plot_pmf

<dl>

<dt>y</dt>
<dd><code>n x 1</code> vector of data</dd>


<dt>post.pred</dt>
<dd><code>nsave</code> draws from the posterior predictive distribution of <code>y</code></dd>


<dt>error.bars</dt>
<dd>logical; if TRUE, include errors bars on the model-based PMF</dd>


<dt>alpha</dt>
<dd>confidence level for the credible intervals</dd>

</dl>

plot_pmf: Plot the empirical and model-based probability mass functions

Description

Usage

Value

Arguments