Solving Ordinary Differential Equations (ODE) in R with diffeqr

Chris Rackauckas


1D Linear ODEs

Let’s solve the linear ODE u'=1.01u. First setup the package:

de <- diffeqr::diffeq_setup()

Define the derivative function f(u,p,t).

f <- function(u,p,t) {

Then we give it an initial condition and a time span to solve over:

u0 <- 1/2
tspan <- c(0., 1.)

With those pieces we define the ODEProblem and solve the ODE:

prob = de$ODEProblem(f, u0, tspan)
sol = de$solve(prob)

This gives back a solution object for which sol$t are the time points and sol$u are the values. We can treat the solution as a continuous object in time via

and a high order interpolation will compute the value at t=0.2. We can check the solution by plotting it:


Systems of ODEs

Now let’s solve the Lorenz equations. In this case, our initial condition is a vector and our derivative functions takes in the vector to return a vector (note: arbitrary dimensional arrays are allowed). We would define this as:

f <- function(u,p,t) {
  du1 = p[1]*(u[2]-u[1])
  du2 = u[1]*(p[2]-u[3]) - u[2]
  du3 = u[1]*u[2] - p[3]*u[3]

Here we utilized the parameter array p. Thus we use diffeqr::ode.solve like before, but also pass in parameters this time:

u0 <- c(1.0,0.0,0.0)
tspan <- list(0.0,100.0)
p <- c(10.0,28.0,8/3)
prob <- de$ODEProblem(f, u0, tspan, p)
sol <- de$solve(prob)

The returned solution is like before except now sol$u is an array of arrays, where sol$u[i] is the full system at time sol$t[i]. It can be convenient to turn this into an R matrix through sapply:

mat <- sapply(sol$u,identity)

This has each row as a time series. t(mat) makes each column a time series. It is sometimes convenient to turn the output into a data.frame which is done via:

udf <-

Now we can use matplot to plot the timeseries together:


Now we can use the Plotly package to draw a phase plot:

plotly::plot_ly(udf, x = ~V1, y = ~V2, z = ~V3, type = 'scatter3d', mode = 'lines')