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

## 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.

## 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.sir()`

## Author

Eric Kolaczyk (http://math.bu.edu/people/kolaczyk/) and Gabor Csardi csardi.gabor@gmail.com.

## Examples

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