surveillance: twinSIR_intensityplot – R documentation

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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. 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"` (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 λ(t) or its epidemic or endemic proportions e(t)/lambda(t) or 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"`, then `theta = c(alpha, beta)`, where `beta` consists of the coefficients of the piecewise constant log-baseline function and the coefficients of the endemic (`cox`) predictor. If `x` is of class `"simEpidata"`, then `theta = c(alpha, 1, betarest)`, where 1 refers to the (true) log-baseline used in the simulation and `betarest` 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 with `aggregate = 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, 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 to 0.02 and `quiet` is set to `TRUE`. Note that the argument `x` of the `rug()` function, which contains the locations for the `rug` 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 to `intensityplot.twinSIR`. For the `intensityplot` methods, further graphical parameters passed to the function `matplot`, e.g. `lty`, `lwd`, `col`, `xlab`, `ylab` and `main`. Note that the `matplot` arguments `x`, `y`, `type` and `add` 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.

Author(s)

Sebastian Meyer

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

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)

surveillance

Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

v1.19.1

GPL-2

Authors

Michael H<f6>hle [aut, ths] (<https://orcid.org/0000-0002-0423-6702>), Sebastian Meyer [aut, cre] (<https://orcid.org/0000-0002-1791-9449>), Michaela Paul [aut], Leonhard Held [ctb, ths], Howard Burkom [ctb], Thais Correa [ctb], Mathias Hofmann [ctb], Christian Lang [ctb], Juliane Manitz [ctb], Andrea Riebler [ctb], Daniel Saban<e9>s Bov<e9> [ctb], Ma<eb>lle Salmon [ctb], Dirk Schumacher [ctb], Stefan Steiner [ctb], Mikko Virtanen [ctb], Wei Wei [ctb], Valentin Wimmer [ctb], R Core Team [ctb] (A few code segments are modified versions of code from base R)

Initial release

2021-03-30