R: Plot Data from a Stochastic Individual Contact Epidemic Model

plot.icm {EpiModel}

R Documentation

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.

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)