surveillance (version 1.4-2)

twinSIR_intensityplot: Plotting Paths of Infection Intensities for twinSIR Models

Description

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.

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" (simulated twinSIR 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 propor

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", then theta = c(alpha, beta), where beta consists of the coefficients of the piecewise constant log-baseline function and the

plot

logical indicating if a plot is desired, defaults to TRUE. Otherwise, only the data of the plot will be returned. Especially with aggregate = FALSE and many individuals one might e.g. consider to plot a subset of th

add

logical. If TRUE, paths are added to the current plot, using lines.

rug.opts

either a list of arguments passed to the function rug, or NULL (or NA), in which case no rug will be plotted. By default, the argument ticksize is set

...

For the plot.twinSIR methode, arguments passed to intensityplot.twinSIR. For the intensityplot methods, further graphical parameters passed to the function matplot

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.

encoding

latin1

References

H�hle{Hoehle}, M. (2009), Additive-Multiplicative Regression Models for Spatio-Temporal Epidemics, Biometrical Journal, 51(6):961-978.

See Also

twinSIR or H�hle{Hoehle} (2009) for a more detailed description of the intensity model. simulate.twinSIR for the simulation of epidemic data according to a twinSIR specification.

Examples

data("fooepidata")
data("foofit")

# an overview of the evolution of the epidemic
plot(fooepidata)

# overall total intensity
plot(foofit, which="total")

# overall epidemic proportion
epi <- plot(foofit, which="epidemic")

# look at returned values
head(epi)

# add the inverse overall endemic proportion = 1 - epidemic proportion
ende <- plot(foofit, which="endemic", add=TRUE, col=2)
legend("right", legend="endemic proportion 
(= 1 - epidemic proportion)",
       lty=1, col=2, bty="n")

# individual intensities
tmp <- plot(foofit, which="total", aggregate=FALSE,
    col=rgb(0,0,0,alpha=if(getRversion() < "2.7.0") 1 else 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=c(1,2), lty=c(1,2))