ccl4model {deSolve} | R Documentation

## The CCl4 Inhalation Model

### Description

The CCl4 inhalation model implemented in `.Fortran`

### Usage

    ccl4model(times, y, parms, ...)

### Arguments

`times` | time sequence for which the model has to be integrated.
`y` | the initial values for the state variables ("AI", "AAM", "AT", "AF", "AL", "CLT" and "AM"), in that order.
`parms` | vector or list holding the ccl4 model parameters; see the example for the order in which these have to be defined.
`...` | any other parameters passed to the integrator `ode` (which solves the model).

### Details

The model is implemented primarily to demonstrate the linking of FORTRAN with R-code.

The source can be found in the ‘/doc/examples/dynload’ subdirectory of the package.

### Author(s)

R. Woodrow Setzer <setzer.woodrow@epa.gov>

### See Also

Try `demo(CCL4model)` for how this model has been fitted to the dataset `ccl4data,`

`aquaphy`, another FORTRAN model, describing growth in aquatic phytoplankton.

### Examples

    ## =================
    ## Parameter values
    ## =================

    Pm <- c(
      ## Physiological parameters
      BW = 0.182,   # Body weight (kg)
      QP = 4.0  ,   # Alveolar ventilation rate (hr^-1)
      QC = 4.0  ,   # Cardiac output (hr^-1)
      VFC = 0.08,   # Fraction fat tissue (kg/(kg/BW))
      VLC = 0.04,   # Fraction liver tissue (kg/(kg/BW))
      VMC = 0.74,   # Fraction of muscle tissue (kg/(kg/BW))
      QFC = 0.05,   # Fractional blood flow to fat ((hr^-1)/QC
      QLC = 0.15,   # Fractional blood flow to liver ((hr^-1)/QC)
      QMC = 0.32,   # Fractional blood flow to muscle ((hr^-1)/QC)

      ## Chemical specific parameters for chemical
      PLA = 16.17,  # Liver/air partition coefficient
      PFA = 281.48, # Fat/air partition coefficient
      PMA = 13.3,   # Muscle/air partition coefficient
      PTA = 16.17,  # Viscera/air partition coefficient
      PB = 5.487,   # Blood/air partition coefficient
      MW = 153.8,   # Molecular weight (g/mol)
      VMAX = 0.04321671, # Max. velocity of metabolism (mg/hr) -calibrated
      KM = 0.4027255,    # Michaelis-Menten constant (mg/l) -calibrated

      ## Parameters for simulated experiment
      CONC = 1000,  # Inhaled concentration
      KL = 0.02,    # Loss rate from empty chamber /hr
      RATS = 1.0,   # Number of rats enclosed in chamber
      VCHC = 3.8    # Volume of closed chamber (l)
    )

    ## ================
    ## State variables
    ## ================
    y <- c(
      AI  = 21, # total mass , mg
      AAM = 0,
      AT  = 0,
      AF  = 0,
      AL  = 0,
      CLT = 0,  # area under the conc.-time curve in the liver
      AM  = 0   # the amount metabolized (AM)
    )

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

    times <- seq(0, 6, by = 0.1)

    ## initial inhaled concentration-calibrated
    conc <- c(26.496, 90.197, 245.15, 951.46)

    plot(ChamberConc ~ time, data = ccl4data, xlab = "Time (hours)",
           xlim = range(c(0, ccl4data$time)),
           ylab = "Chamber Concentration (ppm)",
           log = "y", main = "ccl4model")

    for (cc in conc) {
      Pm["CONC"] <- cc

      VCH <- Pm[["VCHC"]] - Pm[["RATS"]] * Pm[["BW"]]
      AI0 <- VCH * Pm[["CONC"]] * Pm[["MW"]]/24450
      y["AI"] <- AI0

      ## run the model:
      out <- as.data.frame(ccl4model(times, y, Pm))
      lines(out$time, out$CP, lwd = 2)
    }

    legend("topright", lty = c(NA, 1), pch = c(1, NA), lwd = c(NA, 2),
           legend = c("data", "model"))

    ## ==================================
    ## An example with tracer injection
    ## ==================================
    ## every day, a conc of 2 is added to AI.
    ## 1. implemented as a data.frame
    eventdat <- data.frame(var = rep("AI", 6), time = 1:6 ,
      value = rep(1, 6), method = rep("add", 6))

    eventdat

    print(system.time(
      out <-ccl4model(times, y, Pm, events = list(data = eventdat))
    ))

    plot(out, mfrow = c(3, 4), type = "l", lwd = 2)

    # 2. implemented as a function in a DLL!
    print(system.time(
    out2 <-ccl4model(times, y, Pm, events = list(func = "eventfun", time = 1:6))
    ))

    plot(out2, mfrow=c(3, 4), type = "l", lwd = 2)

[Package deSolve version 1.24 Index]