ode.1D {deSolve}R Documentation

Solver For Multicomponent 1-D Ordinary Differential Equations


Solves a system of ordinary differential equations resulting from 1-Dimensional partial differential equations that have been converted to ODEs by numerical differencing.


ode.1D(y, times, func, parms, nspec = NULL, dimens = NULL, 
   method= c("lsoda", "lsode", "lsodes", "lsodar", "vode", "daspk",
   "euler", "rk4", "ode23", "ode45", "radau", "bdf", "adams", "impAdams",
   names = NULL, bandwidth = 1, restructure = FALSE, ...)



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


time sequence for which output is wanted; the first value of times must be the initial time.


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 must be specified in the same order as the state variables y.

If func is a character string then integrator lsodes will be used. See details.


parameters passed to func.


the number of species (components) in the model. If NULL, then dimens should be specified.


the number of boxes in the model. If NULL, then nspec should be specified.


the integrator. Use "vode", "lsode", "lsoda", "lsodar", "daspk", or "lsodes" if the model is very stiff; "impAdams" or "radau" may be best suited for mildly stiff problems; "euler", "rk4", "ode23", "ode45", "adams" are most efficient for non-stiff problems. Also allowed is to pass an integrator function. Use one of the other Runge-Kutta methods via rkMethod. For instance, method = rkMethod("ode45ck") will trigger the Cash-Karp method of order 4(5).

Method "iteration" is special in that here the function func should return the new value of the state variables rather than the rate of change. This can be used for individual based models, for difference equations, or in those cases where the integration is performed within func)


the names of the components; used for plotting.


the number of adjacent boxes over which transport occurs. Normally equal to 1 (box i only interacts with box i-1, and i+1). Values larger than 1 will not work with method = "lsodes". Ignored if the method is explicit.


whether or not the Jacobian should be restructured. Only used if the method is an integrator function. Should be TRUE if the method is implicit, FALSE if explicit.


additional arguments passed to the integrator.


This is the method of choice for multi-species 1-dimensional models, that are only subjected to transport between adjacent layers.

More specifically, this method is to be used if the state variables are arranged per species:

A[1], A[2], A[3],.... B[1], B[2], B[3],.... (for species A, B))

Two methods are implemented.

If the model is specified in compiled code (in a DLL), then option 2, based on lsodes is the only solution method.

For single-species 1-D models, you may also use ode.band.

See the selected integrator for the additional options.


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 second element of the return from func, plus an additional column (the first) for the time value. There will be one row for each element in times unless the integrator returns with an unrecoverable error. If y has a names attribute, it will be used to label the columns of the output value.

The output will have the attributes istate, and rstate, two vectors with several useful elements. The first element of istate returns the conditions under which the last call to the integrator returned. Normal is istate = 2. If verbose = TRUE, the settings of istate and rstate will be written to the screen. See the help for the selected integrator for details.


It is advisable though not mandatory to specify both nspec and dimens. In this case, the solver can check whether the input makes sense (i.e. if nspec * dimens == length(y)).


Karline Soetaert <karline.soetaert@nioz.nl>

See Also

diagnostics to print diagnostic messages.


## =======================================================================
## example 1
## a predator and its prey diffusing on a flat surface
## in concentric circles
## 1-D model with using cylindrical coordinates
## Lotka-Volterra type biology
## =======================================================================

## ================
## Model equations
## ================

lvmod <- function (time, state, parms, N, rr, ri, dr, dri) {
  with (as.list(parms), {
    PREY <- state[1:N]
    PRED <- state[(N+1):(2*N)]

    ## Fluxes due to diffusion
    ## at internal and external boundaries: zero gradient
    FluxPrey <- -Da * diff(c(PREY[1], PREY, PREY[N]))/dri
    FluxPred <- -Da * diff(c(PRED[1], PRED, PRED[N]))/dri

    ## Biology: Lotka-Volterra model
    Ingestion     <- rIng  * PREY * PRED
    GrowthPrey    <- rGrow * PREY * (1-PREY/cap)
    MortPredator  <- rMort * PRED

    ## Rate of change = Flux gradient + Biology
    dPREY    <- -diff(ri * FluxPrey)/rr/dr   +
                GrowthPrey - Ingestion
    dPRED    <- -diff(ri * FluxPred)/rr/dr   +
                Ingestion * assEff - MortPredator

    return (list(c(dPREY, dPRED)))

## ==================
## Model application
## ==================

## model parameters:

R  <- 20                        # total radius of surface, m
N  <- 100                       # 100 concentric circles
dr <- R/N                       # thickness of each layer
r  <- seq(dr/2,by = dr,len = N) # distance of center to mid-layer
ri <- seq(0,by = dr,len = N+1)  # distance to layer interface
dri <- dr                       # dispersion distances

parms <- c(Da     = 0.05,       # m2/d, dispersion coefficient
           rIng   = 0.2,        # /day, rate of ingestion
           rGrow  = 1.0,        # /day, growth rate of prey
           rMort  = 0.2 ,       # /day, mortality rate of pred
           assEff = 0.5,        # -, assimilation efficiency
           cap    = 10)         # density, carrying capacity

## Initial conditions: both present in central circle (box 1) only
state    <- rep(0, 2 * N)
state[1] <- state[N + 1] <- 10
## RUNNING the model:
times  <- seq(0, 200, by = 1)   # output wanted at these time intervals

## the model is solved by the two implemented methods:
## 1. Default: banded reformulation
  out <- ode.1D(y = state, times = times, func = lvmod, parms = parms,
                nspec = 2, names = c("PREY", "PRED"),
                N = N, rr = r, ri = ri, dr = dr, dri = dri)

## 2. Using sparse method
  out2 <- ode.1D(y = state, times = times, func = lvmod, parms = parms,
                 nspec = 2, names = c("PREY","PRED"), 
                 N = N, rr = r, ri = ri, dr = dr, dri = dri,
                 method = "lsodes")

## ================
## Plotting output
## ================
# the data in 'out' consist of: 1st col times, 2-N+1: the prey
# N+2:2*N+1: predators

PREY   <- out[, 2:(N + 1)]

filled.contour(x = times, y = r, PREY, color = topo.colors,
               xlab = "time, days", ylab = "Distance, m",
               main = "Prey density")
# similar:
image(out, which = "PREY", grid = r, xlab = "time, days", 
      legend = TRUE, ylab = "Distance, m", main = "Prey density")

image(out2, grid = r)

# summaries of 1-D variables

# 1-D plots:
matplot.1D(out, type = "l", subset = time == 10)
matplot.1D(out, type = "l", subset = time > 10 & time < 20)

## =======================================================================
## Example 2.
## Biochemical Oxygen Demand (BOD) and oxygen (O2) dynamics
## in a river
## =======================================================================

## ================
## Model equations
## ================
O2BOD <- function(t, state, pars) {
  BOD <- state[1:N]
  O2  <- state[(N+1):(2*N)]

  ## BOD dynamics
  FluxBOD <- v * c(BOD_0, BOD)   # fluxes due to water transport
  FluxO2  <- v * c(O2_0, O2)
  BODrate <- r * BOD             # 1-st order consumption

  ## rate of change = flux gradient  - consumption + reaeration (O2)
  dBOD         <- -diff(FluxBOD)/dx - BODrate
  dO2          <- -diff(FluxO2)/dx  - BODrate      +  p * (O2sat-O2)

  return(list(c(dBOD = dBOD, dO2 = dO2)))
## ==================
## Model application
## ==================
## parameters
dx      <- 25        # grid size of 25 meters
v       <- 1e3       # velocity, m/day
x       <- seq(dx/2, 5000, by = dx)  # m, distance from river
N       <- length(x)
r       <- 0.05      # /day, first-order decay of BOD
p       <- 0.5       # /day, air-sea exchange rate 
O2sat   <- 300       # mmol/m3 saturated oxygen conc
O2_0    <- 200       # mmol/m3 riverine oxygen conc
BOD_0   <- 1000      # mmol/m3 riverine BOD concentration

## initial conditions:
state <- c(rep(200, N), rep(200, N))
times <- seq(0, 20, by = 0.1)

## running the model
##  step 1  : model spinup
out <- ode.1D(y = state, times, O2BOD, parms = NULL, 
              nspec = 2, names = c("BOD", "O2"))

## ================
## Plotting output
## ================
## select oxygen (first column of out:time, then BOD, then O2
O2   <- out[, (N + 2):(2 * N + 1)]
color = topo.colors

filled.contour(x = times, y = x, O2, color = color, nlevels = 50,
               xlab = "time, days", ylab = "Distance from river, m",
               main = "Oxygen")
## or quicker plotting:
image(out, grid = x,  xlab = "time, days", ylab = "Distance from river, m")               

[Package deSolve version 1.40 Index]