Skip to contents

Plot Data from a Stochastic Individual Contact Epidemic Model

Source: R/plot.icm.R
plot.icm.Rd

Plots epidemiological data from a stochastic individual contact model simulated with icm.

Usage

# S3 method for class 'icm'
plot(
  x,
  y = NULL,
  popfrac = FALSE,
  sim.lines = FALSE,
  sims = NULL,
  sim.col = NULL,
  sim.lwd = NULL,
  sim.alpha = NULL,
  mean.line = TRUE,
  mean.smooth = TRUE,
  mean.col = NULL,
  mean.lwd = 2,
  mean.lty = 1,
  qnts = 0.5,
  qnts.col = NULL,
  qnts.alpha = 0.5,
  qnts.smooth = TRUE,
  legend = TRUE,
  leg.cex = 0.8,
  grid = FALSE,
  add = FALSE,
  xlim = NULL,
  ylim = NULL,
  main = "",
  xlab = "Time",
  ylab = NULL,
  ...
)

Arguments

x

An EpiModel model object of class icm.

y

Output compartments or flows from icm object to plot. ——-

popfrac

If TRUE, plot prevalence of values rather than numbers (see details).

sim.lines

If TRUE, plot individual simulation lines. Default is to plot lines for one-group models but not for two-group models.

sims

A vector of simulation numbers to plot.

sim.col

Vector of any standard R color format for simulation lines.

sim.lwd

Line width for simulation lines.

sim.alpha

Transparency level for simulation lines, where 0 = transparent and 1 = opaque (see adjustcolor function).

mean.line

If TRUE, plot mean of simulations across time.

mean.smooth

If TRUE, use a loess smoother on the mean line.

mean.col

Vector of any standard R color format for mean lines.

mean.lwd

Line width for mean lines.

mean.lty

Line type for mean lines.

qnts

If numeric, plot polygon of simulation quantiles based on the range implied by the argument (see details). If FALSE, suppress polygon from plot.

qnts.col

Vector of any standard R color format for polygons.

qnts.alpha

Transparency level for quantile polygons, where 0 = transparent and 1 = opaque (see adjustcolor function).

qnts.smooth

If TRUE, use a loess smoother on quantile polygons.

legend

If TRUE, plot default legend.

leg.cex

Legend scale size.

grid

If TRUE, a grid is added to the background of plot (see grid for details), with default of nx by ny.

add

If TRUE, new plot window is not called and lines are added to existing plot window.

xlim

the x limits (x1, x2) of the plot. Note that x1 > x2 is allowed and leads to a ‘reversed axis’.

The default value, NULL, indicates that the range of the finite values to be plotted should be used.

ylim

the y limits of the plot.

main

a main title for the plot, see also title.

xlab

a label for the x axis, defaults to a description of x.

ylab

a label for the y axis, defaults to a description of y.

...

Additional arguments to pass.

Details

This plotting function will extract the epidemiological output from a model object of class icm and plot the time series data of disease prevalence and other results. The summary statistics that the function calculates and plots are individual simulation lines, means of the individual simulation lines, and quantiles of those individual simulation lines. The mean line, toggled on with mean.line=TRUE, is calculated as the row mean across simulations at each time step.

Compartment prevalences are the size of a compartment over some denominator. To plot the raw numbers from any compartment, use popfrac=FALSE; this is the default for any plots of flows. The popfrac parameter calculates and plots the denominators of all specified compartments using these rules: 1) for one-group models, the prevalence of any compartment is the compartment size divided by the total population size; 2) for two-group models, the prevalence of any compartment is the compartment size divided by the group population size. For any prevalences that are not automatically calculated, the mutate_epi function may be used to add new variables to the icm object to plot or analyze.

The quantiles show the range of outcome values within a certain specified quantile range. By default, the interquartile range is shown: that is the middle 50\ middle 95\ where they are plotted by default, specify qnts=FALSE.

See also

icm

Examples

## Example 1: Plotting multiple compartment values from SIR model
param <- param.icm(inf.prob = 0.5, act.rate = 0.5, rec.rate = 0.02)
init <- init.icm(s.num = 500, i.num = 1, r.num = 0)
control <- control.icm(type = "SIR", nsteps = 100,
                       nsims = 3, verbose = FALSE)
mod <- icm(param, init, control)
plot(mod, grid = TRUE)


## Example 2: Plot only infected with specific output from SI model
param <- param.icm(inf.prob = 0.25, act.rate = 0.25)
init <- init.icm(s.num = 500, i.num = 10)
control <- control.icm(type = "SI", nsteps = 100,
                       nsims = 3, verbose = FALSE)
mod2 <- icm(param, init, control)

# Plot prevalence
plot(mod2, y = "i.num", mean.line = FALSE, sim.lines = TRUE)


# Plot incidence
par(mfrow = c(1, 2))
plot(mod2, y = "si.flow", mean.smooth = TRUE, grid = TRUE)
plot(mod2, y = "si.flow", qnts.smooth = FALSE, qnts = 1)