waic

Widely Application Information Criteria (WAIC) for model comparison

Fits time trend models for routine disease surveillance tasks and returns probability distributions for a variety of quantities of interest, including age-standardized rates, period and cumulative percent change, and measures of health inequality. The models are appropriate for count data such as disease incidence and mortality data, employing a Poisson or binomial likelihood and the first-difference (random-walk) prior for unknown risk. Optionally add a covariance matrix for multiple, correlated time series models. Inference is completed using Markov chain Monte Carlo via the Stan modeling language. References: Donegan, Hughes, and Lee (2022) <doi:10.2196/34589>; Stan Development Team (2021) <https://mc-stan.org>; Theil (1972, ISBN:0-444-10378-3).

Connor Donegan

surveil

Time Series Models for Disease Surveillance

waic function

<dl><dt>fit</dt>
<dd>An <code>surveil</code> object</dd>
<dt>pointwise</dt>
<dd>Logical (defaults to <code>FALSE</code>); if <code>pointwise = TRUE</code>, a vector of values for each observation will be returned.</dd>
<dt>digits</dt>
<dd>Round results to this many digits.</dd></dl>

Arguments

Widely Applicable Information Criteria — waic

<dl>

<dt>fit</dt>
<dd>An <code>surveil</code> object</dd>


<dt>pointwise</dt>
<dd>Logical (defaults to <code>FALSE</code>); if <code>pointwise = TRUE</code>, a vector of values for each observation will be returned.</dd>


<dt>digits</dt>
<dd>Round results to this many digits.</dd>

</dl>

waic: Widely Applicable Information Criteria

Description

Usage

Value

Arguments

Examples