plot.sir {igraph}

Plotting the results on multiple SIR model runs

Description

This function can conveniently plot the results of multiple SIR model simulations.

Usage

## S3 method for class 'sir'
plot(
  x,
  comp = c("NI", "NS", "NR"),
  median = TRUE,
  quantiles = c(0.1, 0.9),
  color = NULL,
  median_color = NULL,
  quantile_color = NULL,
  lwd.median = 2,
  lwd.quantile = 2,
  lty.quantile = 3,
  xlim = NULL,
  ylim = NULL,
  xlab = "Time",
  ylab = NULL,
  ...
)

Arguments

x

The output of the SIR simulation, coming from the sir() function.

comp

Character scalar, which component to plot. Either ‘NI’ (infected, default), ‘NS’ (susceptible) or ‘NR’ (recovered).

median

Logical scalar, whether to plot the (binned) median.

quantiles

A vector of (binned) quantiles to plot.

color

Color of the individual simulation curves.

median_color

Color of the median curve.

quantile_color

Color(s) of the quantile curves. (It is recycled if needed and non-needed entries are ignored if too long.)

lwd.median

Line width of the median.

lwd.quantile

Line width of the quantile curves.

lty.quantile

Line type of the quantile curves.

xlim

The x limits, a two-element numeric vector. If NULL, then it is calculated from the data.

ylim

The y limits, a two-element numeric vector. If NULL, then it is calculated from the data.

xlab

The x label.

ylab

The y label. If NULL then it is automatically added based on the comp argument.

...

Additional arguments are passed to plot(), that is run before any of the curves are added, to create the figure.

Details

The number of susceptible/infected/recovered individuals is plotted over time, for multiple simulations.

Value

Nothing.

Author(s)

Eric Kolaczyk (http://math.bu.edu/people/kolaczyk/) and Gabor Csardi [e-mail].

References

Bailey, Norman T. J. (1975). The mathematical theory of infectious diseases and its applications (2nd ed.). London: Griffin.

See Also

sir() for running the actual simulation.

Processes on graphs time_bins()

Examples

g <- sample_gnm(100, 100)
sm <- sir(g, beta = 5, gamma = 1)
plot(sm)