events {deSolve} R Documentation

## Implementing Events and Roots in Differential Equation Models.

### Description

An event occurs when the value of a state variable is suddenly changed, e.g. because a value is added, subtracted, or multiplied. The integration routines cannot deal easily with such state variable changes. Typically these events occur only at specific times. In deSolve, events can be imposed by means of an input data.frame, that specifies at which time and how a certain state variable is altered, or via an event function.

Roots occur when a root function becomes zero. By default when a root is found, the simulation either stops (no event), or triggers an event.

### Details

The events are specified by means of argument events passed to the integration routines.

events should be a list that contains one of the following:

1. func: an R-function or the name of a function in compiled code that specifies the event,

2. data: a data.frame that specifies the state variables, times, values and types of the events. Note that the event times must also be part of the integration output times, else the event will not take place. As from version 1.9.1, this is checked by the solver, and a warning message is produced if event times are missing in times; see also cleanEventTimes for utility functions to check and solve such issues.

3. time: when events are specified by an event function: the times at which the events take place. Note that these event times must also be part of the integration output times exactly, else the event would not take place. As from version 1.9.1 this is checked by the solver, and an error message produced if event times are missing in times; see also cleanEventTimes for utility functions to check and solve such issues.

4. root: when events are specified by a function and triggered by a root, this logical should be set equal to TRUE

5. terminalroot when events are triggered by a root, the default is that the simulation continues after the event is executed. In terminalroot, we can specify which roots should terminate the simulation.

6. maxroot: when root = TRUE, the maximal number of times at with a root is found and that are kept; defaults to 100. If the number of roots > maxroot, then only the first maxroot will be outputted.

7. ties: if events, as specified by a data.frame are "ordered", set to "ordered", the default is "notordered". This will save some computational time.

In case the events are specified by means of an R function (argument events$func), it must be defined as: function(t, y, parms, ...). t is the current time point in the integration, y is the current estimate of the variables in the ODE system. If the initial values y has a names attribute, the names will be available inside events$func. parms is a vector or list of parameters; ... (optional) are any other arguments passed to the function via the call to the integration method. The event function should return the y-values (some of which modified), as a vector.

If events$func is a string, this indicates that the events are specified by a function in compiled code. This function has as arguments, the number of state variables, the time, and the state variable vector. See package vignette "compiledCode" for more details. In case events are specified by an R-function, this requires either: input of the time of the events, a vector as defined in events$time OR the specification of a root function. In the latter case, the model must be solved with an integration routine with root-finding capability

The root function itself should be specified with argument rootfunc. In this case, the integrator is informed that the simulation it to be continued after a root is found by setting events$root equal to TRUE. If the events are specified by a data frame (argument events$data), this should contain the following columns (and in that order):

1. var the state variable name or number that is affected by the event

2. time the time at which the event is to take place; the solvers will check if the time is embraced by the simulation time

3. value the value, magnitude of the event

4. method which event is to take place; should be one of ("replace", "add", "multiply"); also allowed is to specify the number (1 = replace, 2 = add, 3 = multiply)

For instance, the following line

"v1" 10 2 "add"

will cause the value 2 to be added to a state variable, called "v1" at time = 10.

From deSolve version 1.9.1 the following routines have root-finding capability: lsoda, lsode, lsodes, and radau. For the first 3 integration methods, the root finding algorithm is based on the algorithm in solver LSODAR, and is implemented in FORTRAN. For radau, the root solving algorithm is written in C-code, and it works slightly different. Thus, some problems involving roots may be more efficiently solved with either lsoda, lsode, or lsodes, while other problems are more efficiently solved with radau.

If a root function is defined, but not an event function, then by default the solver will stop at a root. If this is not desirable, e.g. because we want to record the position of many roots, then a dummy "event" function can be defined which returns the values of the state variables - unaltered.

If roots and events are combined, and roots are found, then the output will have attribute troot which will contain the times at which a root was found (and the event trigerred). There will be at most events$maxroot such values. The default is 100. See two last examples; also see example of ccl4model. ### Author(s) Karline Soetaert ### See Also forcings, for how to implement forcing functions. lsodar, for more examples of roots ### Examples ## ============================================================================= ## 1. EVENTS in a data.frame ## ============================================================================= ## derivative function: derivatives set to 0 derivs <- function(t, var, parms) { list(dvar = rep(0, 2)) } yini <- c(v1 = 1, v2 = 2) times <- seq(0, 10, by = 0.1) eventdat <- data.frame(var = c("v1", "v2", "v2", "v1"), time = c(1, 1, 5, 9) , value = c(1, 2, 3, 4), method = c("add", "mult", "rep", "add")) eventdat out <- vode(func = derivs, y = yini, times = times, parms = NULL, events = list(data = eventdat)) plot(out) ## eventdat <- data.frame(var = c(rep("v1", 10), rep("v2", 10)), time = c(1:10, 1:10), value = runif(20), method = rep("add", 20)) eventdat out <- ode(func = derivs, y = yini, times = times, parms = NULL, events = list(data = eventdat)) plot(out) ## ============================================================================= ## 2. EVENTS in a function ## ============================================================================= ## derivative function: rate of change v1 = 0, v2 reduced at first-order rate derivs <- function(t, var, parms) { list(c(0, -0.5 * var[2])) } # events: add 1 to v1, multiply v2 with random number eventfun <- function(t, y, parms){ with (as.list(y),{ v1 <- v1 + 1 v2 <- 5 * runif(1) return(c(v1, v2)) }) } yini <- c(v1 = 1, v2 = 2) times <- seq(0, 10, by = 0.1) out <- ode(func = derivs, y = yini, times = times, parms = NULL, events = list(func = eventfun, time = c(1:9, 2.2, 2.4)) ) plot(out, type = "l") ## ============================================================================= ## 3. EVENTS triggered by a root function ## ============================================================================= ## derivative: simple first-order decay derivs <- function(t, y, pars) { return(list(-0.1 * y)) } ## event triggered if state variable = 0.5 rootfun <- function (t, y, pars) { return(y - 0.5) } ## sets state variable = 1 eventfun <- function(t, y, pars) { return(y = 1) } yini <- 2 times <- seq(0, 100, 0.1) ## uses ode to solve; root = TRUE specifies that the event is ## triggered by a root. out <- ode(times = times, y = yini, func = derivs, parms = NULL, events = list(func = eventfun, root = TRUE), rootfun = rootfun) plot(out, type = "l") ## time of the root: troot <- attributes(out)$troot
points(troot, rep(0.5, length(troot)))

## =============================================================================
## 4. More ROOT examples: Rotation function
## =============================================================================
Rotate <- function(t, x, p )
list(c( x[2],
-x[1]  ))

## Root = when second state variable = 0
rootfun <- function(t, x, p) x[2]

## "event" returns state variables unchanged
eventfun <- function(t, x, p) x
times <- seq(from = 0, to = 15, by = 0.1)

## 1. No event: stops at first root
out1 <- ode(func = Rotate, y = c(5, 5), parms = 0,
times = times, rootfun = rootfun)
tail(out1)

## 2. Continues till end of times and records the roots
out <- ode(func = Rotate, y = c(5, 5), parms = 0,
times = times, rootfun = rootfun,
events = list(func = eventfun, root = TRUE) )

plot(out)
troot <- attributes(out)$troot # time of roots points(troot,rep(0, length (troot))) ## Multiple roots: either one of the state variables = 0 root2 <- function(t, x, p) x out2 <- ode(func = Rotate, y = c(5, 5), parms = 0, times = times, rootfun = root2, events = list(func = eventfun, root = TRUE) ) plot(out2, which = 2) troot <- attributes(out2)$troot
indroot <- attributes(out2)\$indroot  # which root was found
points(troot, rep(0, length (troot)), col = indroot, pch = 18, cex = 2)

## Multiple roots and stop at first time root 1.
out3 <- ode(func = Rotate, y = c(5, 5), parms = 0,
times = times, rootfun = root2,
events = list(func = eventfun, root = TRUE, terminalroot = 1))

## =============================================================================
## 5. Stop at 5th root - only works with radau.
## =============================================================================
Rotate <- function(t, x, p )
list(c( x[2],
-x[1],
0  ))

## Root = when second state variable = 0
root3  <- function(t, x, p)  c(x[2], x[3] - 5)
event3 <- function (t, x, p) c(x[1:2], x[3]+1)
times <- seq(0, 15, 0.1)
out3 <- ode(func = Rotate, y = c(x1 = 5, x2 = 5, nroot = 0),
parms = 0, method = "radau",
times = times, rootfun = root3,
events = list(func = event3, root = TRUE, terminalroot = 2))
plot(out3)
attributes(out3)[c("troot", "nroot", "indroot")]

## =============================================================================
## 6 Event in R-code, model function in compiled code - based on vode example
## =============================================================================

times <- 1:365
Flux <- cbind(times, sin(pi*times/365)^2) # forcing function

# run without events
out <- ode(y = c(C = 1), times, func = "scocder", parms = c(k=0.01),
dllname = "deSolve", initforc = "scocforc", forcings = Flux,
initfunc = "scocpar", nout = 2, outnames = c("Mineralisation", "Depo"))

# Event halves the concentration
EventMin <- function(t, y , p) y/2

out2 <- ode(y = c(C = 1), times, func = "scocder", parms = c(k=0.01),
dllname = "deSolve", initforc = "scocforc", forcings = Flux,
initfunc = "scocpar", nout = 2, outnames = c("Mineralisation", "Depo"),
events = list (func = EventMin, time = c(50.1, 200, 210.5)))

plot(out, out2)



[Package deSolve version 1.35 Index]