# ssa.plot: Simple plotting of ssa output In GillespieSSA: Gillespie's Stochastic Simulation Algorithm (SSA)

## Description

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

## Usage

 ```1 2 3 4``` ```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.

GillespieSSA-package, `ssa()`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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) ```