| ssa.d {GillespieSSA} | R Documentation |
Direct method implementation of the SSA as described by Gillespie (1977). It is usually called from within ssa, but can be invoked directly.
ssa.d(a = stop("missing propensity vector (a)"),
nu = stop("missing state-change matrix (nu)"))
a |
vector of evaluated propensity functions. |
nu |
state-change matrix. |
Performs one time step using the Direct method.
A list with two elements, 1) the time leap (tau) and 2) the realized state change vector (nu_j).
Gillespie (1977)
## Logistic growth model
a = function(parms,x){
b <- parms[1]
d <- parms[2]
K <- parms[3]
N <- x[1]
return(c(b*N , N*b + (b-d)*N/K))
}
parms <- c(2,1,1000,500)
x <- 500
nu <- matrix(c(+1, -1),ncol=2)
t <- 0
for (i in seq(100)) {
out <- ssa.d(a(parms,x),nu)
x <- x + out$nu_j
t <- t + 1
cat("t:",t,", x:",x,"\n")
}