EpiModel (version 2.5.0)

plot.dcm: Plot Data from a Deterministic Compartmental Epidemic Model

Description

Plots epidemiological data from a deterministic compartment epidemic model solved with dcm.

Usage

# S3 method for dcm
plot(
  x,
  y = NULL,
  popfrac = FALSE,
  run = NULL,
  col = NULL,
  lwd = NULL,
  lty = NULL,
  alpha = 0.9,
  legend = NULL,
  leg.name = NULL,
  leg.cex = 0.8,
  grid = FALSE,
  add = FALSE,
  main = "",
  xlim = NULL,
  ylim = NULL,
  xlab = "Time",
  ylab = NULL,
  ...
)

Arguments

x

An EpiModel object of class dcm.

y

Output compartments or flows from dcm object to plot.

popfrac

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

run

Run number to plot, for models with multiple runs (default is run 1).

col

Color for lines, either specified as a single color in a standard R color format, or alternatively as a color palette from RColorBrewer::RColorBrewer (see details).

lwd

Line width for output lines.

lty

Line type for output lines.

alpha

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

legend

Type of legend to plot. Values are "n" for no legend, "full" for full legend, and "lim" for limited legend (see details).

leg.name

Character string to use for legend, with the default determined automatically based on the y input.

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.

main

a main title for the plot, see also title.

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.

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 to main plot window (see plot.default).

The <code>popfrac</code> Argument

Compartment prevalence is the size of a compartment over some denominator. To plot the raw numbers from any compartment, use popfrac=FALSE; this is the default. 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 size.

Color Palettes

Since dcm supports multiple run sensitivity models, plotting the results of such models uses a complex color scheme for distinguishing runs. This is accomplished using the RColorBrewer::RColorBrewer color palettes, which include a range of linked colors using named palettes. For plot.dcm, one may either specify a brewer color palette listed in RColorBrewer::brewer.pal.info, or, alternatively, a vector of standard R colors (named, hexidecimal, or positive integers; see col2rgb).

Plot Legends

There are three automatic legend types available, and the legend is added by default for plots. To turn off the legend, use legend="n". To plot a legend with values for every line in a sensitivity analysis, use legend="full". With models with many runs, this may be visually overwhelming. In those cases, use legend="lim" to plot a legend limited to the highest and lowest values of the varying parameter in the model. In cases where the default legend names are not helpful, one may override those names with the leg.name argument.

Details

This function plots epidemiological outcomes from a deterministic compartmental model solved with dcm. Depending on the number of model runs (sensitivity analyses) and number of groups, the default plot is the fractional proportion of each compartment in the model over time. The specific compartments or flows to plot may be set using the y parameter, and in multiple run models the specific run may also be specified.

See Also

dcm, RColorBrewer::brewer.pal.info

Examples

# Deterministic SIR model with varying act rate
param <- param.dcm(inf.prob = 0.2, act.rate = 1:10,
                   rec.rate = 1/3, a.rate = 0.011, ds.rate = 0.01,
                   di.rate = 0.03, dr.rate = 0.01)
init <- init.dcm(s.num = 1000, i.num = 1, r.num = 0)
control <- control.dcm(type = "SIR", nsteps = 100, dt = 0.25)
mod <- dcm(param, init, control)

# Plot disease prevalence by default
plot(mod)

# Plot prevalence of susceptibles
plot(mod, y = "s.num", popfrac = TRUE, col = "Greys")

# Plot number of susceptibles
plot(mod, y = "s.num", popfrac = FALSE, col = "Greys", grid = TRUE)

# Plot multiple runs of multiple compartments together
plot(mod, y = c("s.num", "i.num"),
     run = 5, xlim = c(0, 50), grid = TRUE)
plot(mod, y = c("s.num", "i.num"),
     run = 10, lty = 2, legend = "n", add = TRUE)