# NOT RUN {
## =======================================================================
## Example 1:
## Various ways to solve the same model.
## =======================================================================
## the model, 5 state variables
f1 <- function (t, y, parms) {
ydot <- vector(len = 5)
ydot[1] <- 0.1*y[1] -0.2*y[2]
ydot[2] <- -0.3*y[1] +0.1*y[2] -0.2*y[3]
ydot[3] <- -0.3*y[2] +0.1*y[3] -0.2*y[4]
ydot[4] <- -0.3*y[3] +0.1*y[4] -0.2*y[5]
ydot[5] <- -0.3*y[4] +0.1*y[5]
return(list(ydot))
}
## the Jacobian, written as a full matrix
fulljac <- function (t, y, parms) {
jac <- matrix(nrow = 5, ncol = 5, byrow = TRUE,
data = c(0.1, -0.2, 0 , 0 , 0 ,
-0.3, 0.1, -0.2, 0 , 0 ,
0 , -0.3, 0.1, -0.2, 0 ,
0 , 0 , -0.3, 0.1, -0.2,
0 , 0 , 0 , -0.3, 0.1))
return(jac)
}
## the Jacobian, written in banded form
bandjac <- function (t, y, parms) {
jac <- matrix(nrow = 3, ncol = 5, byrow = TRUE,
data = c( 0 , -0.2, -0.2, -0.2, -0.2,
0.1, 0.1, 0.1, 0.1, 0.1,
-0.3, -0.3, -0.3, -0.3, 0))
return(jac)
}
## initial conditions and output times
yini <- 1:5
times <- 1:20
## default: stiff method, internally generated, full Jacobian
out <- lsode(yini, times, f1, parms = 0, jactype = "fullint")
## stiff method, user-generated full Jacobian
out2 <- lsode(yini, times, f1, parms = 0, jactype = "fullusr",
jacfunc = fulljac)
## stiff method, internally-generated banded Jacobian
## one nonzero band above (up) and below(down) the diagonal
out3 <- lsode(yini, times, f1, parms = 0, jactype = "bandint",
bandup = 1, banddown = 1)
## stiff method, user-generated banded Jacobian
out4 <- lsode(yini, times, f1, parms = 0, jactype = "bandusr",
jacfunc = bandjac, bandup = 1, banddown = 1)
## non-stiff method
out5 <- lsode(yini, times, f1, parms = 0, mf = 10)
## =======================================================================
## Example 2:
## diffusion on a 2-D grid
## partially specified Jacobian
## =======================================================================
diffusion2D <- function(t, Y, par) {
y <- matrix(nrow = n, ncol = n, data = Y)
dY <- r*y # production
## diffusion in X-direction; boundaries = 0-concentration
Flux <- -Dx * rbind(y[1,],(y[2:n,]-y[1:(n-1),]),-y[n,])/dx
dY <- dY - (Flux[2:(n+1),]-Flux[1:n,])/dx
## diffusion in Y-direction
Flux <- -Dy * cbind(y[,1],(y[,2:n]-y[,1:(n-1)]),-y[,n])/dy
dY <- dY - (Flux[,2:(n+1)]-Flux[,1:n])/dy
return(list(as.vector(dY)))
}
## parameters
dy <- dx <- 1 # grid size
Dy <- Dx <- 1 # diffusion coeff, X- and Y-direction
r <- 0.025 # production rate
times <- c(0, 1)
n <- 50
y <- matrix(nrow = n, ncol = n, 0)
pa <- par(ask = FALSE)
## initial condition
for (i in 1:n) {
for (j in 1:n) {
dst <- (i - n/2)^2 + (j - n/2)^2
y[i, j] <- max(0, 1 - 1/(n*n) * (dst - n)^2)
}
}
filled.contour(y, color.palette = terrain.colors)
## =======================================================================
## jacfunc need not be estimated exactly
## a crude approximation, with a smaller bandwidth will do.
## Here the half-bandwidth 1 is used, whereas the true
## half-bandwidths are equal to n.
## This corresponds to ignoring the y-direction coupling in the ODEs.
## =======================================================================
print(system.time(
for (i in 1:20) {
out <- lsode(func = diffusion2D, y = as.vector(y), times = times,
parms = NULL, jactype = "bandint", bandup = 1, banddown = 1)
filled.contour(matrix(nrow = n, ncol = n, out[2,-1]), zlim = c(0,1),
color.palette = terrain.colors, main = i)
y <- out[2, -1]
}
))
par(ask = pa)
# }
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