# +++++++++++++++++++++++++++++++++++++++++++++++++++ application: Logistic.R
# Simulates the logistic equation
importFromExamples("Logistic.R")
# Run the application
LogisticApp <- function(verbose = FALSE) {
x <- 0.1
vx <- 0
r <- 2 # Malthusian parameter (rate of maximum population growth)
K <- 10.0 # carrying capacity of the environment
dt <- 0.01; tol <- 1e-3; tmax <- 10
population <- Logistic() # create a Logistic ODE object
# Two ways of initializing the object
# population <- init(population, c(x, vx, 0), r, K)
init(population) <- list(initState = c(x, vx, 0),
r = r,
K = K)
odeSolver <- Verlet(population) # select the solver
# Two ways of initializing the solver
# odeSolver <- init(odeSolver, dt)
init(odeSolver) <- dt
population@odeSolver <- odeSolver
# setSolver(population) <- odeSolver
rowVector <- vector("list")
i <- 1
while (getTime(population) <= tmax) {
rowVector[[i]] <- list(t = getTime(population),
s1 = getState(population)[1],
s2 = getState(population)[2])
population <- doStep(population)
i <- i + 1
}
DT <- data.table::rbindlist(rowVector)
return(DT)
}
# show solution
solution <- LogisticApp()
plot(solution)
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