The <code>plot</code> method for class <code>"epidataCS"</code> either plots the
number of events along the time axis (<code>epidataCSplot_time</code>) as a
<code><a href="/link/hist()?package=surveillance&version=1.25.0" data-mini-rdoc="surveillance::hist()">hist()</a></code>, or the locations of the events in the observation region
<code>W</code> (<code>epidataCSplot_space</code>).
The spatial plot can be enriched with tile-specific color levels to
indicate attributes such as the population (using <code><a href="https://edzer.github.io/sp/reference/spplot.html">spplot</a></code>).

epidataCS_plot

Statistical methods for the modeling and monitoring of time series
of counts, proportions and categorical data, as well as for the modeling
of continuous-time point processes of epidemic phenomena.
The monitoring methods focus on aberration detection in count data time
series from public health surveillance of communicable diseases, but
applications could just as well originate from environmetrics,
reliability engineering, econometrics, or social sciences. The package
implements many typical outbreak detection procedures such as the
(improved) Farrington algorithm, or the negative binomial GLR-CUSUM
method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>.
A novel CUSUM approach combining logistic and multinomial logistic
modeling is also included. The package contains several real-world data
sets, the ability to simulate outbreak data, and to visualize the
results of the monitoring in a temporal, spatial or spatio-temporal
fashion. A recent overview of the available monitoring procedures is
given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>.
For the retrospective analysis of epidemic spread, the package provides
three endemic-epidemic modeling frameworks with tools for visualization,
likelihood inference, and simulation. hhh4() estimates models for
(multivariate) count time series following Paul and Held (2011)
<doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>.
twinSIR() models the susceptible-infectious-recovered (SIR) event
history of a fixed population, e.g, epidemics across farms or networks,
as a multivariate point process as proposed by Hoehle (2009)
<doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point
process models for a spatio-temporal point pattern of infective events,
e.g., time-stamped geo-referenced surveillance data, as proposed by
Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>.
A recent overview of the implemented space-time modeling frameworks
for epidemic phenomena is given by Meyer et al. (2017)
<doi:10.18637/jss.v077.i11>.

Sebastian Meyer

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
Phenomena

Sophie Reichert

Andrea Riebler

Daniel Sabanes Bove

Stefan Steiner

Mikko Virtanen

Wei Wei

Valentin Wimmer

R Core Team 

Howard Burkom

Thais Correa

Mathias Hofmann

Christian Lang

Juliane Manitz

Dirk Schumacher

Leonhard Held

Michael Hoehle

Michaela Paul

Maelle Salmon

epidataCS_plot function

The <code>plot</code> method for class <code>"epidataCS"</code> either plots the
number of events along the time axis (<code>epidataCSplot_time</code>) as a
<code><a href='https://rdrr.io/r/graphics/hist.html'>hist()</a></code>, or the locations of the events in the observation region
<code>W</code> (<code>epidataCSplot_space</code>).
The spatial plot can be enriched with tile-specific color levels to
indicate attributes such as the population (using <code><a href='https://edzer.github.io/sp/reference/spplot.html'>spplot</a></code>).

Plotting the Events of an Epidemic over Time and Space — epidataCS_plot

<dl>

 <dt>x</dt>
<dd>an object of class <code>"epidataCS"</code>.</dd>

 <dt>aggregate</dt>
<dd>character, one of <code>"time"</code> and <code>"space"</code>, referring to the
 specific plot functions <code>epidataCSplot_time</code> and
 <code>epidataCSplot_time</code>, respectively.
 For <code>"time"</code>, the number of events over time is plotted as
 <code><a href='https://rdrr.io/r/graphics/hist.html'>hist</a></code> (or <code><a href='https://rdrr.io/r/graphics/hist.POSIXt.html'>hist.Date</a></code>).
 For <code>"space"</code>, the observation region <code>x$W</code> (or the
 <code>tiles</code>) and the locations of the events therein are plotted.</dd>

 <dt>subset</dt>
<dd>logical expression indicating a subset of events to consider for
 plotting: missing values are taken as false. Note that the
 expression is evaluated in the data frame of event marks
 (<code>marks(x)</code>), which means that column names can be referred to
 by name (like in <code><a href='https://rdrr.io/r/base/subset.html'>subset.data.frame</a></code>).</dd>

 <dt>...</dt>
<dd>in the basic <code>plot</code>-method further arguments are passed to the
 <code>aggregate</code>-specific plot function.
 In <code>epidataCSplot_time</code>, further graphical parameters are
 passed to <code><a href='https://rdrr.io/r/graphics/hist.html'>hist</a></code> or <code><a href='https://rdrr.io/r/graphics/hist.POSIXt.html'>hist.Date</a></code>,
 respectively. In <code>epidataCSplot_space</code>, further arguments are
 passed to the <code>plot</code>-method for
 <code>"<a href='https://edzer.github.io/sp/reference/SpatialPolygons-class.html'>SpatialPolygons</a>"</code>, which draws <code>tiles</code>.</dd>


 <dt>by</dt>
<dd>an expression evaluated in <code>marks(x)</code>, defining how
 events should be stratified in the plot (the result is converted to
 a factor), or <code>NULL</code> to disregard event types.
 By default (<code>by = type</code>) the plot distinguishes between event
 types, i.e., the bars of the temporal plot are stacked by type, and
 the point colors in the spatial plot differ by type, respectively. 
 Note: to select specific event types for plotting use the
 <code>subset</code> argument, e.g., <code>subset=(type=="B")</code>.</dd>

 <dt>t0.Date</dt>
<dd>the beginning of the observation period
 <code>t0 = x$stgrid$start[1]</code> as a <code>"<a href='https://rdrr.io/r/base/Dates.html'>Date</a>"</code> (or
 anything coercible by <code>as.Date</code> without further arguments),
 enabling a nice x-axis using <code><a href='https://rdrr.io/r/graphics/hist.POSIXt.html'>hist.Date</a></code> and sensible
 <code>breaks</code> of the histogram, e.g., <code>breaks="months"</code>.
 The event times then equal
 <code>t0.Date + as.integer(x$events$time - t0)</code>, i.e. possible
 fractional parts of the event times are removed (which ensures that
 using <code>breaks = "months"</code> or other automatic types always
 works).</dd>

 <dt>breaks</dt>
<dd>a specification of the histogram break points, see
 <code><a href='https://rdrr.io/r/graphics/hist.html'>hist</a></code> (or <code><a href='https://rdrr.io/r/graphics/hist.POSIXt.html'>hist.Date</a></code> if <code>t0.Date</code> is
 used). The default value <code>"stgrid"</code> is special and means to use
 the temporal grid points
 <code>with(x$stgrid, c(start[1L], unique.default(stop)))</code>
 as breaks (or their <code>"Date"</code> equivalents).</dd>

 <dt>freq</dt>
<dd>see <code><a href='https://rdrr.io/r/graphics/hist.html'>hist</a></code>, defaults to <code>TRUE</code>.</dd>

 <dt>col</dt>
<dd>fill colour for the bars of the histogram, defaults to
 the vector of <code><a href='https://rdrr.io/r/grDevices/palettes.html'>rainbow</a></code> colours.</dd>

 <dt>cumulative</dt>
<dd>if a list (of style options),
 lines for the cumulative number of events (per type) will be
 added to the plot. Possible options are <code>axis</code> (logical),
 <code>lab</code> (axis label), <code>maxat</code> (single integer affecting
 the axis range), <code>lwd</code>, <code>col</code>, and <code>offset</code> (a
 numeric vector of length the number of types).</dd>

 <dt>add</dt>
<dd>logical (default: <code>FALSE</code>) indicating if the plot
 should be added to an existing window.
 Ignored if an <code><a href='https://edzer.github.io/sp/reference/spplot.html'>spplot</a></code> is created (if <code>pop</code> is
 non-<code>NULL</code>).</dd>

 <dt>mar</dt>
<dd>see <code><a href='https://rdrr.io/r/graphics/par.html'>par</a></code>. The default (<code>NULL</code>) is
 <code>mar &lt;- par("mar")</code>, with <code>mar[4] &lt;- mar[2]</code> if an
 axis is requested for the <code>cumulative</code> numbers.</dd>

 <dt>xlim,ylim</dt>
<dd><code>NULL</code> provides automatic axis limits.</dd>

 <dt>xlab,ylab</dt>
<dd>axis labels (with sensible defaults).</dd>

 <dt>main</dt>
<dd>main title of the plot (defaults to no title).</dd>

 <dt>panel.first</dt>
<dd>expression that should be evaluated after the
 plotting window has been set up but before the histogram is plotted.
 Defaults to adding horizontal grid lines.</dd>

 <dt>legend.types</dt>
<dd>if a list (of arguments for <code><a href='https://rdrr.io/r/graphics/legend.html'>legend</a></code>),
 a legend for the event types is added to the plot in case there is
 more than one type.</dd>


 <dt>tiles</dt>
<dd>the observation region <code>x$W</code> (default) or,
 alternatively, a <code>"<a href='https://edzer.github.io/sp/reference/SpatialPolygons-class.html'>SpatialPolygons</a>"</code>
 representation of the tiles of <code>x$stgrid</code>.</dd>

 <dt>pop</dt>
<dd>if <code>tiles</code> is a
 <code>"<a href='https://edzer.github.io/sp/reference/SpatialPolygonsDataFrame-class.html'>SpatialPolygonsDataFrame</a>"</code>, <code>pop</code> can
 specify an attribute to be displayed in a <code>levelplot</code> behind the
 point pattern, see <code><a href='https://edzer.github.io/sp/reference/spplot.html'>spplot</a></code>. By default (<code>NULL</code>),
 the conventional graphics system is used to display the <code>tiles</code>
 and event locations, otherwise the result is a
 <code><a href='https://rdrr.io/pkg/lattice/man/trellis.object.html'>trellis.object</a></code>.</dd>

 <dt>cex.fun</dt>
<dd>function which takes a vector of counts of events
 at each unique location and returns a (vector of) <code>cex</code>
 value(s) for the sizes of the corresponding <code>points</code>.
 Defaults to the <code><a href='https://rdrr.io/r/base/MathFun.html'>sqrt()</a></code> function, which for the default
 circular <code>pch=1</code> means that the area of each point is
 proportional to the number of events at its location.</dd>

 <dt>points.args</dt>
<dd>a list of (type-specific) graphical parameters
 for <code><a href='https://rdrr.io/r/graphics/points.html'>points</a></code>, specifically <code>pch</code>, <code>lwd</code>,
 and <code>col</code>, which are all recycled to give the length
 <code>nlevels(x$events$type)</code>. In contrast, a possible
 <code>cex</code> element should be scalar (default: 0.5) and 
 multiplies the sizes obtained from <code>cex.fun</code>.</dd>

 <dt>legend.counts</dt>
<dd>if a list (of arguments for
 <code><a href='https://rdrr.io/r/graphics/legend.html'>legend</a></code>), a legend illustrating the effect of 
 <code>cex.fun</code> is added to the plot. This list may contain a
 special element <code>counts</code>, which is an integer vector
 specifying the counts to illustrate.</dd>

 <dt>sp.layout</dt>
<dd>optional list of additional layout items in case
 <code>pop</code> is non-<code>NULL</code>, see <code><a href='https://edzer.github.io/sp/reference/spplot.html'>spplot</a></code>.</dd>

</dl>

Arguments

Author

Plotting the Events of an Epidemic over Time and Space

epidataCS_plot: Plotting the Events of an Epidemic over Time and Space

Description

Usage

Arguments

Value

See Also

Examples