plot.sir: Plotting the results on multiple SIR model runs

In igraph: Network Analysis and Visualization

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)

igraph documentation built on Oct. 20, 2024, 1:06 a.m.