deSolve (version 1.3)

rk4: Solve System of ODE (Ordinary Differential Equation)s by Euler's Method or Classical Runge-Kutta 4th Order Integration.

Description

Solving initial value problems for systems of first-order ordinary differential equations (ODEs) using Euler's method or the classical Runge-Kutta 4th order integration.

Usage

euler(y, times, func, parms, verbose = FALSE, ynames = TRUE,
  dllname = NULL, initfunc = dllname, initpar = parms,
  rpar = NULL, ipar = NULL, nout = 0, outnames = NULL, ...)
rk4(y, times, func, parms, verbose = FALSE, ynames = TRUE,
  dllname = NULL, initfunc = dllname, initpar = parms,
  rpar = NULL, ipar = NULL, nout = 0, outnames = NULL, ...)

Arguments

y

the initial (state) values for the ODE system. If y has a name attribute, the names will be used to label the output matrix.

times

times at which explicit estimates for y are desired. The first value in times must be the initial time.

func

either an R-function that computes the values of the derivatives in the ODE system (the model definition) at time t, or a character string giving the name of a compiled function in a dynamically loaded shared library.

If fu

parms

vector or list of parameters used in func

verbose

a logical value that, when TRUE, triggers more verbose output from the ODE solver.

ynames

if FALSE: names of state variables are not passed to function func ; this may speed up the simulation especially for large models.

dllname

a string giving the name of the shared library (without extension) that contains all the compiled function or subroutine definitions refered to in func and jacfunc. See package vignette "compiledCode".

initfunc

if not NULL, the name of the initialisation function (which initialises values of parameters), as provided in dllname. See package vignette "compiledCode".

initpar

only when dllname is specified and an initialisation function initfunc is in the dll: the parameters passed to the initialiser, to initialise the common blocks (fortran) or global variables (C, C++).

rpar

only when dllname is specified: a vector with double precision values passed to the dll-functions whose names are specified by func and jacfunc.

ipar

only when dllname is specified: a vector with integer values passed to the dll-functions whose names are specified by func and jacfunc.

nout

only used if dllname is specified and the model is defined in compiled code: the number of output variables calculated in the compiled function func, present in the shared library. Note: it is not automatically checke

outnames

only used if dllname is specified and nout > 0: the names of output variables calculated in the compiled function func, present in the shared library.

...

additional arguments passed to func allowing this to be a generic function.

Details

rk4 and euler are special versions of the two fixed step solvers with less overhead and less functionality (e.g. no interpolation) compared to the generic Runge-Kutta codes called by rk. If you need different internal and external time steps, you may use rk(y, times, func, parms, method="rk4") or rk(y, times, func, parms, method="euler").

See help pages of rk and rkMethod for details.

See Also

• rkMethodfor a list of available Runge-Kutta parameter sets,
• rkandeuler
• lsoda,lsode,lsodes,lsodar,vode,daspkfor solvers of the Livermore family,
• odefor a general interface to most of the ODE solvers,
• ode.bandfor solving models with a banded Jacobian,
• ode.1Dfor integrating 1-D models,
• ode.2Dfor integrating 2-D models,
• ode.3Dfor integrating 3-D models,

diagnostics to print diagnostic messages.

Examples

## ===============================================================
## Example: Analytical and numerical solutions of logistic growth
## ===============================================================

## the derivative of the logistic
logist <- function(t, x, parms) {
  with(as.list(parms), {
    dx <- r * x[1] * (1 - x[1]/K)
    list(dx)
  })
}

time  <- 0:100
N0    <- 0.1; r <- 0.5; K <- 100
parms <- c(r = r, K = K)
x <- c(N = N0)

## analytical solution
plot(time, K/(1+(K/N0-1) * exp(-r*time)), ylim = c(0, 120),
  type = "l", col = "red", lwd = 2)

## reasonable numerical solution
time <- seq(0, 100, 2)
out <- as.data.frame(rk4(x, time, logist, parms))
points(out$time, out$N, pch = 16, col = "blue", cex = 0.5)

## same time step, systematic under-estimation
time <- seq(0, 100, 2)
out <- as.data.frame(euler(x, time, logist, parms))
points(out$time, out$N, pch = 1)

## unstable result
time <- seq(0, 100, 4)
out <- as.data.frame(euler(x, time, logist, parms))
points(out$time, out$N, pch = 8, cex = 0.5)

## method with automatic time step
out <- as.data.frame(lsoda(x, time, logist, parms))
points(out$time, out$N, pch = 1, col = "green")

legend("bottomright",
  c("analytical","rk4, h=2", "euler, h=2",
    "euler, h=4", "lsoda"),
  lty = c(1, NA, NA, NA, NA), lwd = c(2, 1, 1, 1, 1),
  pch = c(NA, 16, 1, 8, 1),
  col = c("red", "blue", "black", "black", "green"))