SSModelDynamicGompertz-class

SSModelDynamicGompertz

<p>Class for dynamic Gompertz curve state space model object.</p>
<div class="section" id="methods">
<h3>Methods</h3>
<p><code>get_model(y, q = NULL, sea.type = 'trigonometric', sea.period = 7)</code>
Retrieves the model object.</p><div class="section" id="parameters">
<h4>Parameters</h4>
<ul>
<li><p><code>y</code> The cumulated variable.</p></li>
<li><p><code>q</code> The signal-to-noise ratio (ratio of slope to irregular
variance). Defaults to <code>'NULL'</code>, in which case no signal-to-noise ratio
will be imposed. Instead, it will be estimated.</p></li>
<li><p><code>sea.type</code> Seasonal type. Options are <code>'trigonometric'</code> and
<code>'none'</code>. <code>'trigonometric'</code> will yield a model with a trigonometric
seasonal component and <code>'none'</code> will yield a model with no seasonal
component.</p></li>
<li><p><code>sea.period</code>  The period of seasonality. For a day-of-the-week
effect with daily data, this would be 7. Not required if
<code>sea.type = 'none'</code>.</p></li>
</ul>
</div>
<div class="section" id="return-value">
<h4>Return Value</h4>
<p><code>KFS</code> model object.</p>
</div><p></p><p></p></div>


The 'tsgc' package provides comprehensive tools for the analysis and forecasting of epidemic trajectories.
It is designed to model the progression of an epidemic over time while accounting for the various uncertainties
inherent in real-time data. Underpinned by a dynamic Gompertz model, the package adopts a state space approach,
using the Kalman filter for flexible and robust estimation of the non-linear growth pattern commonly observed in
epidemic data. The reinitialization feature enhances the model’s ability to adapt to the emergence of new waves.
The forecasts generated by the package are of value to public health officials and researchers who need to
understand and predict the course of an epidemic to inform decision-making. Beyond its application in public
health, the package is also a useful resource for researchers and practitioners in fields where the trajectories
of interest resemble those of epidemics, such as innovation diffusion. The package includes functionalities for
data preprocessing, model fitting, and forecast visualization, as well as tools for evaluating forecast accuracy.
The core methodologies implemented in 'tsgc' are based on well-established statistical techniques as described in
Harvey and Kattuman (2020) <doi:10.1162/99608f92.828f40de>, Harvey and Kattuman (2021)
<doi:10.1098/rsif.2021.0179>, and Ashby, Harvey, Kattuman, and Thamotheram (2024)
<https://www.jbs.cam.ac.uk/wp-content/uploads/2024/03/cchle-tsgc-paper-2024.pdf>.

Craig Thamotheram

tsgc

Time Series Methods Based on Growth Curves

SSModelDynamicGompertz-class function

<p>Class for dynamic Gompertz curve state space model object.</p>
<div class='section' id='methods'>
<h3>Methods</h3>
<p><code>get_model(y, q = NULL, sea.type = 'trigonometric', sea.period = 7)</code>
Retrieves the model object.</p><div class='section' id='parameters'>
<h4>Parameters</h4>
<ul>
<li><p><code>y</code> The cumulated variable.</p></li>
<li><p><code>q</code> The signal-to-noise ratio (ratio of slope to irregular
variance). Defaults to <code>'NULL'</code>, in which case no signal-to-noise ratio
will be imposed. Instead, it will be estimated.</p></li>
<li><p><code>sea.type</code> Seasonal type. Options are <code>'trigonometric'</code> and
<code>'none'</code>. <code>'trigonometric'</code> will yield a model with a trigonometric
seasonal component and <code>'none'</code> will yield a model with no seasonal
component.</p></li>
<li><p><code>sea.period</code>  The period of seasonality. For a day-of-the-week
effect with daily data, this would be 7. Not required if
<code>sea.type = 'none'</code>.</p></li>
</ul>
</div>
<div class='section' id='return-value'>
<h4>Return Value</h4>
<p><code>KFS</code> model object.</p>
</div>

</div>


SSModelDynamicGompertz-class: Class for dynamic Gompertz curve state space model object.

Description

Methods

Parameters

Return Value

Arguments

Examples