init_bam_thin

Initialize the parameters for an additive model, which may contain
both linear and nonlinear predictors. The nonlinear terms are modeled
using low-rank thin plate splines.

internal

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

init_bam_thin function

<dl><dt>y</dt>
<dd><code>n x 1</code> vector of data</dd>
<dt>X_lin</dt>
<dd><code>n x pL</code> matrix of predictors to be modelled as linear</dd>
<dt>X_nonlin</dt>
<dd><code>n x pNL</code> matrix of predictors to be modelled as nonlinear</dd>
<dt>B_all</dt>
<dd>optional <code>pNL</code>-dimensional list of <code>n x L[j]</code> dimensional
basis matrices for each nonlinear term j=1,...,pNL; if NULL, compute internally</dd></dl>

Arguments

Initialize the parameters for an additive model — init_bam_thin

<dl>

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


<dt>X_lin</dt>
<dd><code>n x pL</code> matrix of predictors to be modelled as linear</dd>


<dt>X_nonlin</dt>
<dd><code>n x pNL</code> matrix of predictors to be modelled as nonlinear</dd>


<dt>B_all</dt>
<dd>optional <code>pNL</code>-dimensional list of <code>n x L[j]</code> dimensional
basis matrices for each nonlinear term j=1,...,pNL; if NULL, compute internally</dd>

</dl>

init_bam_thin: Initialize the parameters for an additive model

Description

Usage

Value

Arguments