ssa.plot: Simple plotting of ssa output

In GillespieSSA: Gillespie's Stochastic Simulation Algorithm (SSA)

Simple plotting of ssa output

Description

Provides basic functionally for simple and quick time series plot of simulation output from ssa().

Usage

ssa.plot(
  out = stop("requires simulation output object"),
  file = "ssaplot",
  by = 1,
  plot.from = 2,
  plot.to = ncol(out$data),
  plot.by = 1,
  show.title = TRUE,
  show.legend = TRUE
)

Arguments

out	data object returned from ssa().
file	name of the output file (only applicable if plot.device!="x11".
by	time increment in the plotted time series
plot.from	first population to plot the time series for (see note)
plot.to	last population to plot the time series for (see note)
plot.by	increment in the sequence of populations to plot the time series for (see note)
show.title	boolean object indicating if the plot should display a title
show.legend	boolean object indicating if the legend is displayed

Note

The options by, plot.from, plot.to, and plot.by can be used to plot a sparser sequence of data points. To plot the population sizes using a larger time interval the by option can be set, e.g. to plot only every 10th time point by=10. To plot only specific populations the plot.from, plot.to, and plot.by options can be set to subset the state vector. Note that the indexing of the populations is based on the (t,X) vector, i.e. the first column is the time vector while the first population is index by 2 and the last population by N+1. Display of a plot title above the plot and legend is optional (and are set with the arguments show.title and show.legend. Above the plot panel miscellaneous information for the simulation are displayed, i.e. method, elapsed wall time, number of time steps executed, and the number of time steps per data point.

See Also

GillespieSSA-package, ssa()

Examples

## Define the Kermack-McKendrick SIR model and run once using the Direct method
parms <- c(beta=.001, gamma=.100)
x0 <- c(S=500, I=1, R=0)                         # Initial state vector
nu <- matrix(c(-1,0,1,-1,0,1),nrow=3,byrow=TRUE) # State-change matrix
a  <- c("beta*S*I", "gamma*I")                   # Propensity vector
tf <- 100                                        # Final time
simName <- "Kermack-McKendrick SIR"
out <- ssa(x0,a,nu,parms,tf,method="D",simName,verbose=TRUE,consoleInterval=1)

## Basic ssa plot
ssa.plot(out)

# Plot only the infectious class
ssa.plot(out,plot.from=3,plot.to=3)

GillespieSSA documentation built on March 18, 2022, 7:55 p.m.