rk4 {deSolve}R Documentation

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,
  forcings=NULL, initforc = NULL, fcontrol=NULL, ...)
rk4(y, times, func, parms, verbose = FALSE, ynames = TRUE,
  dllname = NULL, initfunc = dllname, initpar = parms,
  rpar = NULL, ipar = NULL, nout = 0, outnames = NULL,
  forcings=NULL, initforc = NULL, fcontrol=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 func is an R-function, it must be defined as: func <- function(t, y, parms,...). t is the current time point in the integration, y is the current estimate of the variables in the ODE system. If the initial values y has a names attribute, the names will be available inside func. parms is a vector or list of parameters; ... (optional) are any other arguments passed to the function.
The return value of func should be a list, whose first element is a vector containing the derivatives of y with respect to time, and whose next elements are global values that are required at each point in times. The derivatives should be specified in the same order as the state variables y.
If func is a string, then dllname must give the name of the shared library (without extension) which must be loaded before rk4 is called. See package vignette "compiledCode" for more details.
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 checked whether this is indeed the number of output variables calculed in the dll - you have to perform this check in the code. See package vignette "compiledCode".
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.
forcings only used if ‘dllname’ is specified: a list with the forcing function data sets, each present as a two-columned matrix, with (time,value); interpolation outside the interval [min(times), max(times)] is done by taking the value at the closest data extreme.
See forcings or package vignette "compiledCode".
initforc if not NULL, the name of the forcing function initialisation function, as provided in ‘dllname’. It MUST be present if forcings has been given a value. See forcings or package vignette "compiledCode".
fcontrol A list of control parameters for the forcing functions. See forcings or vignette compiledCode.
... 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.

Value

A matrix of class deSolve with up to as many rows as elements in times and as many columns as elements in y plus the number of "global" values returned in the next elements of the return from func, plus and additional column for the time value. There will be a row for each element in times unless the integration routine returns with an unrecoverable error. If y has a names attribute, it will be used to label the columns of the output value.

Note

For most practical cases, solvers with flexible timestep (e.g. rk(method="ode45") and especially solvers of the Livermore family (ODEPACK, e.g. lsoda) are superior.

Author(s)

Thomas Petzoldt thomas.petzoldt@tu-dresden.de

See Also

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"))

[Package deSolve version 1.5 Index]