R: Time Lagged Values of State Variables and Derivatives.

timelags {deSolve} | R Documentation

## Time Lagged Values of State Variables and Derivatives.

### Description

Functions `lagvalue` and `lagderiv` provide access to past (lagged) values of state variables and derivatives.

They are to be used with function `dede`, to solve delay differential equations.

### Usage

    lagvalue(t, nr)
    lagderiv(t, nr)

### Arguments

`t` | the time for which the lagged value is wanted; this should be no larger than the current simulation time and no smaller than the initial simulation time.
`nr` | the number of the lagged value; if `NULL` then all state variables or derivatives are returned.

### Details

The `lagvalue` and `lagderiv` can only be called during the integration, the lagged time should not be smaller than the initial simulation time, nor should it be larger than the current simulation time.

Cubic Hermite interpolation is used to obtain an accurate interpolant at the requested lagged time.

### Value

a scalar (or vector) with the lagged value(s).

### Author(s)

Karline Soetaert <karline.soetaert@nioz.nl>

### See Also

dede, for how to implement delay differential equations.

### Examples

    ## =============================================================================
    ## exercise 6 from Shampine and Thompson, 2000
    ## solving delay differential equations with dde23
    ##
    ## two lag values
    ## =============================================================================

    ##-----------------------------
    ## the derivative function
    ##-----------------------------
    derivs <- function(t, y, parms) {
      History <- function(t) c(cos(t), sin(t))
      if (t < 1)
        lag1 <- History(t - 1)[1]
      else
        lag1 <- lagvalue(t - 1)[1] # returns a vector; select first element

      if (t < 2)
        lag2 <- History(t - 2)[2]
      else
        lag2 <- lagvalue(t - 2,2) # faster than lagvalue(t - 2)[2]

      dy1 <- lag1 * lag2
      dy2 <- -y[1] * lag2

      list(c(dy1, dy2), lag1 = lag1, lag2 = lag2)
    }

    ##-----------------------------
    ## parameters
    ##-----------------------------

    r <- 3.5; m <- 19

    ##-----------------------------
    ## initial values and times
    ##-----------------------------

    yinit <- c(y1 = 0, y2 = 0)
    times <- seq(0, 20, by = 0.01)

    ##-----------------------------
    ## solve the model
    ##-----------------------------

    yout <- dede(y = yinit, times = times, func = derivs,
      parms = NULL, atol = 1e-9)

    ##-----------------------------
    ## plot results
    ##-----------------------------

    plot(yout, type = "l", lwd = 2)

    ## =============================================================================
    ## The predator-prey model with time lags, from Hale
    ## problem 1 from Shampine and Thompson, 2000
    ## solving delay differential equations with dde23
    ##
    ## a vector with lag valuess
    ## =============================================================================

    ##-----------------------------
    ## the derivative function
    ##-----------------------------
    predprey <- function(t, y, parms) {
      tlag <- t - 1
      if (tlag < 0)
        ylag <- c(80, 30)
      else
        ylag <- lagvalue(tlag)  # returns a vector

      dy1 <- a * y[1] * (1 - y[1]/m) + b * y[1] * y[2]
      dy2 <- c * y[2] + d * ylag[1] * ylag[2]
      list(c(dy1, dy2))
    }

    ##-----------------------------
    ## parameters
    ##-----------------------------

    a <- 0.25; b <- -0.01; c <- -1 ; d <- 0.01; m <- 200

    ##-----------------------------
    ## initial values and times
    ##-----------------------------

    yinit <- c(y1 = 80, y2 = 30)
    times <- seq(0, 100, by = 0.01)

    #-----------------------------
    # solve the model
    #-----------------------------

    yout <- dede(y = yinit, times = times, func = predprey, parms = NULL)

    ##-----------------------------
    ## display, plot results
    ##-----------------------------

    plot(yout, type = "l", lwd = 2, main = "Predator-prey model", mfrow = c(2, 2))
    plot(yout[,2], yout[,3], xlab = "y1", ylab = "y2", type = "l", lwd = 2)

    diagnostics(yout)

    ## =============================================================================
    ##
    ## A neutral delay differential equation (lagged derivative)
    ##  y't = -y'(t-1), y(t) t < 0 = 1/t
    ##
    ## =============================================================================

    ##-----------------------------
    ## the derivative function
    ##-----------------------------
    derivs <- function(t, y, parms) {
      tlag <- t - 1
      if (tlag < 0)
        dylag <- -1
      else
        dylag <- lagderiv(tlag)

      list(c(dy = -dylag), dylag = dylag)
    }

    ##-----------------------------
    ## initial values and times
    ##-----------------------------

    yinit <- 0
    times <- seq(0, 4, 0.001)

    ##-----------------------------
    ## solve the model
    ##-----------------------------

    yout <- dede(y = yinit, times = times, func = derivs, parms = NULL)

    ##-----------------------------
    ## display, plot results
    ##-----------------------------

    plot(yout, type = "l", lwd = 2)

[Package deSolve version 1.21 Index]