Plotting Paths of Infection Intensities for twinSIR
Models
twinSIR_intensityplot.Rd
intensityplot
methods to plot the evolution of the total infection
intensity, its epidemic proportion or its endemic proportion over time.
The default plot
method for objects of class "twinSIR"
is just a wrapper for the intensityplot
method.
The implementation is illustrated in Meyer et al. (2017, Section 4),
see vignette("twinSIR")
.
Usage
# S3 method for class 'twinSIR'
plot(x, which = c("epidemic proportion", "endemic proportion",
"total intensity"), ...)
# S3 method for class 'twinSIR'
intensityplot(x, which = c("epidemic proportion", "endemic proportion",
"total intensity"), aggregate = TRUE, theta = NULL,
plot = TRUE, add = FALSE, rug.opts = list(), ...)
# S3 method for class 'simEpidata'
intensityplot(x, which = c("epidemic proportion", "endemic proportion",
"total intensity"), aggregate = TRUE, theta = NULL,
plot = TRUE, add = FALSE, rug.opts = list(), ...)
Arguments
- x
an object of class
"twinSIR"
(fitted model) or"simEpidata"
(simulatedtwinSIR
epidemic), respectively.- which
"epidemic proportion"
,"endemic proportion"
, or"total intensity"
. Partial matching is applied. Determines whether to plot the path of the total intensity \(\lambda(t)\) or its epidemic or endemic proportions \(\frac{e(t)}{\lambda(t)}\) or \(\frac{h(t)}{\lambda(t)}\).- aggregate
logical. Determines whether lines for all individual infection intensities should be drawn (
FALSE
) or their sum only (TRUE
, the default).- theta
numeric vector of model coefficients. If
x
is of class"twinSIR"
, thentheta = c(alpha, beta)
, wherebeta
consists of the coefficients of the piecewise constant log-baseline function and the coefficients of the endemic (cox
) predictor. Ifx
is of class"simEpidata"
, thentheta = c(alpha, 1, betarest)
, where 1 refers to the (true) log-baseline used in the simulation andbetarest
is the vector of the remaining coefficients of the endemic (cox
) predictor. The default (NULL
) means that the fitted or true parameters, respectively, will be used.- plot
logical indicating if a plot is desired, defaults to
TRUE
. Otherwise, only the data of the plot will be returned. Especially withaggregate = FALSE
and many individuals one might e.g. consider to plot a subset of the individual intensity paths only or do some further calculations/analysis of the infection intensities.- add
logical. If
TRUE
, paths are added to the current plot, usinglines
.- rug.opts
either a list of arguments passed to the function
rug
, orNULL
(orNA
), in which case norug
will be plotted. By default, the argumentticksize
is set to 0.02 andquiet
is set toTRUE
. Note that the argumentx
of therug()
function, which contains the locations for therug
is fixed internally and can not be modified. The locations of the rug are the time points of infections.- ...
For the
plot.twinSIR
method, arguments passed tointensityplot.twinSIR
. For theintensityplot
methods, further graphical parameters passed to the functionmatplot
, e.g.lty
,lwd
,col
,xlab
,ylab
andmain
. Note that thematplot
argumentsx
,y
,type
andadd
are implicit and can not be specified here.
Value
numeric matrix with the first column "stop"
and as many rows as there
are "stop"
time points in the event history x
. The other
columns depend on the argument aggregate
: if TRUE
, there
is only one other column named which
, which contains the values of
which
at the respective "stop"
time points. Otherwise, if
aggregate = FALSE
, there is one column for each individual, each of
them containing the individual which
at the respective "stop"
time points.
References
Meyer, S., Held, L. and Höhle, M. (2017): Spatio-temporal analysis of epidemic phenomena using the R package surveillance. Journal of Statistical Software, 77 (11), 1-55. doi:10.18637/jss.v077.i11
See also
twinSIR
for a description of the intensity model, and
simulate.twinSIR
for the simulation of epidemic data
according to a twinSIR
specification.
Examples
data("hagelloch")
plot(hagelloch)
# a simplistic twinSIR model
fit <- twinSIR(~ household, data = hagelloch)
# overall total intensity
plot(fit, which = "total")
# overall epidemic proportion
epi <- plot(fit, which = "epidemic", ylim = c(0, 1))
head(epi)
# add overall endemic proportion = 1 - epidemic proportion
ende <- plot(fit, which = "endemic", add = TRUE, col = 2)
legend("topleft", legend = "endemic proportion", lty = 1, col = 2, bty = "n")
# individual intensities
tmp <- plot(fit, which = "total", aggregate = FALSE,
col = rgb(0, 0, 0, alpha = 0.1),
main = expression("Individual infection intensities " *
lambda[i](t) == Y[i](t) %.% (e[i](t) + h[i](t))))
# return value: matrix of individual intensity paths
str(tmp)
# plot intensity path only for individuals 3 and 99
matplot(x = tmp[,1], y = tmp[,1+c(3,99)], type = "S",
ylab = "Force of infection", xlab = "time",
main = expression("Paths of the infection intensities " *
lambda[3](t) * " and " * lambda[99](t)))
legend("topright", legend = paste("Individual", c(3,99)),
col = 1:2, lty = 1:2)