warpDLM

This function outputs posterior quantities and forecasts from a univariate
warpDLM model. Currently two latent DLM specifications are supported:
local level and the local linear trend.

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

warpDLM function

<dl><dt>y</dt>
<dd>the count-valued time series</dd>
<dt>type</dt>
<dd>the type of latent DLM (must be either level or trend)</dd>
<dt>transformation</dt>
<dd>transformation to use for the latent process (default is np);
must be one of<ul>
<li>"identity" (identity transformation)</li>
<li>"log" (log transformation)</li>
<li>"sqrt" (square root transformation)</li>
<li>"np" (nonparametric transformation estimated from empirical CDF)</li>
<li>"pois" (transformation for moment-matched marginal Poisson CDF)</li>
<li>"neg-bin" (transformation for moment-matched marginal Negative Binomial CDF)</li>
</ul></dd>
<dt>y_max</dt>
<dd>a fixed and known upper bound for all observations; default is <code>Inf</code></dd>
<dt>R0</dt>
<dd>the variance for the initial state theta_0; default is 10</dd>
<dt>nsave</dt>
<dd>number of MCMC iterations to save</dd>
<dt>nburn</dt>
<dd>number of MCMC iterations to discard</dd>
<dt>nskip</dt>
<dd>number of MCMC iterations to skip between saving iterations,
i.e., save every (nskip + 1)th draw</dd>
<dt>n.ahead</dt>
<dd>number of steps to forecast ahead</dd></dl>

Arguments

Posterior Inference for warpDLM model with latent structural DLM — warpDLM

<dl>

<dt>y</dt>
<dd>the count-valued time series</dd>


<dt>type</dt>
<dd>the type of latent DLM (must be either level or trend)</dd>


<dt>transformation</dt>
<dd>transformation to use for the latent process (default is np);
must be one of<ul>
<li>"identity" (identity transformation)</li>
<li>"log" (log transformation)</li>
<li>"sqrt" (square root transformation)</li>
<li>"np" (nonparametric transformation estimated from empirical CDF)</li>
<li>"pois" (transformation for moment-matched marginal Poisson CDF)</li>
<li>"neg-bin" (transformation for moment-matched marginal Negative Binomial CDF)</li>
</ul></dd>


<dt>y_max</dt>
<dd>a fixed and known upper bound for all observations; default is <code>Inf</code></dd>


<dt>R0</dt>
<dd>the variance for the initial state theta_0; default is 10</dd>


<dt>nsave</dt>
<dd>number of MCMC iterations to save</dd>


<dt>nburn</dt>
<dd>number of MCMC iterations to discard</dd>


<dt>nskip</dt>
<dd>number of MCMC iterations to skip between saving iterations,
i.e., save every (nskip + 1)th draw</dd>


<dt>n.ahead</dt>
<dd>number of steps to forecast ahead</dd>

</dl>

warpDLM: Posterior Inference for warpDLM model with latent structural DLM

Description

Usage

Value

Arguments