plot_gy_ci

Plots the smoothed/filtered growth rate of the difference in the
cumulated variable (\(g_y\)) and the associated confidence intervals.

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

plot_gy_ci function

<dl><dt>res</dt>
<dd>Results object estimated using the <code>estimate()</code> method.</dd>
<dt>plt.start.date</dt>
<dd>Plot start date. Default is <code>NULL</code> which is the
start of the estimation sample.</dd>
<dt>smoothed</dt>
<dd>Logical value indicating whether to used the smoothed
estimates of \(\delta\) and \(\gamma\). Default is <code>FALSE</code>, in
which case the filtered estimates are returned.</dd>
<dt>title</dt>
<dd>Title for plot. Enter as text string. <code>NULL</code>
(i.e. no title) by default.</dd>
<dt>series.name</dt>
<dd>The name of the series the growth rate is being computed
for. E.g. <code>'New cases'</code>.</dd>
<dt>pad.right</dt>
<dd>Numerical value for the amount of time periods of blank
space you wish to leave on the right of the graph. Extends the horizontal
axis by the given number of time periods.</dd></dl>

Arguments

Plots the growth rates and slope of the log cumulative growth rate — plot_gy_ci

<dl>

<dt>res</dt>
<dd>Results object estimated using the <code>estimate()</code> method.</dd>


<dt>plt.start.date</dt>
<dd>Plot start date. Default is <code>NULL</code> which is the
start of the estimation sample.</dd>


<dt>smoothed</dt>
<dd>Logical value indicating whether to used the smoothed
estimates of \(\delta\) and \(\gamma\). Default is <code>FALSE</code>, in
which case the filtered estimates are returned.</dd>


<dt>title</dt>
<dd>Title for plot. Enter as text string. <code>NULL</code>
(i.e. no title) by default.</dd>


<dt>series.name</dt>
<dd>The name of the series the growth rate is being computed
for. E.g. <code>'New cases'</code>.</dd>


<dt>pad.right</dt>
<dd>Numerical value for the amount of time periods of blank
space you wish to leave on the right of the graph. Extends the horizontal
axis by the given number of time periods.</dd>

</dl>

plot_gy_ci: Plots the growth rates and slope of the log cumulative growth rate

Description

Usage

Value

Arguments

Examples