plot.icm: Plot Data from a Stochastic Individual Contact Epidemic Model

In statnet/EpiModel: Mathematical Modeling of Infectious Disease Dynamics

Plot Data from a Stochastic Individual Contact Epidemic Model

Description

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

Usage

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

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.
axs	Plot axis type (see par for details), with default of "r".
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.
...	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)

statnet/EpiModel documentation built on March 30, 2024, 11:26 a.m.