Evaluates a Derivative Function Represented in a DLL
Calls a function, defined in a compiled language as a DLL
DLLfunc(func, times, y, parms, dllname, initfunc = dllname, rpar = NULL, ipar = NULL, nout = 0, outnames = NULL, forcings = NULL, initforc = NULL, fcontrol = NULL)
func |
the name of the function in the dynamically loaded shared library, |
times |
first value = the time at which the function needs to be evaluated, |
y |
the values of the dependent variables for which the function needs to be evaluated, |
parms |
the parameters that are passed to the initialiser function, |
dllname |
a string giving the name of the shared library (without
extension) that contains the compiled function or subroutine definitions
referred to in |
initfunc |
if not |
rpar |
a vector with double precision values passed to the
DLL-function |
ipar |
a vector with integer values passed to the dll-function
|
nout |
the number of output variables. |
outnames |
only used if ‘dllname’ is specified and
|
forcings |
only used if ‘dllname’ is specified: a list with
the forcing function data sets, each present as a two-columned matrix,
with (time, value); interpolation outside the interval
[min( See package vignette |
initforc |
if not |
fcontrol |
A list of control parameters for the forcing functions.
See package vignette |
This function is meant to help developing FORTRAN or C models that are
to be used to solve ordinary differential equations (ODE) in packages
deSolve and/or rootSolve.
a list containing:
dy |
the rate of change estimated by the function, |
var |
the ordinary output variables of the function. |
Karline Soetaert <karline.soetaert@nioz.nl>
ode for a general interface to most of the ODE solvers
## ==========================================================================
## ex. 1
## ccl4model
## ==========================================================================
## Parameter values and initial conditions
## see example(ccl4model) for a more comprehensive implementation
Parms <- c(0.182, 4.0, 4.0, 0.08, 0.04, 0.74, 0.05, 0.15, 0.32,
16.17, 281.48, 13.3, 16.17, 5.487, 153.8, 0.04321671,
0.4027255, 1000, 0.02, 1.0, 3.8)
yini <- c(AI = 21, AAM = 0, AT = 0, AF = 0, AL = 0, CLT = 0, AM = 0)
## the rate of change
DLLfunc(y = yini, dllname = "deSolve", func = "derivsccl4",
initfunc = "initccl4", parms = Parms, times = 1,
nout = 3, outnames = c("DOSE", "MASS", "CP") )
## ==========================================================================
## ex. 2
## SCOC model
## ==========================================================================
## Forcing function "data"
Flux <- matrix(ncol = 2, byrow = TRUE, data = c(1, 0.654, 2, 0.167))
parms <- c(k = 0.01)
Yini <- 60
DLLfunc(y=Yini, times=1, func = "scocder",
parms = parms, dllname = "deSolve",
initforc = "scocforc", forcings = Flux,
initfunc = "scocpar", nout = 2,
outnames = c("Mineralisation","Depo"))
## correct value = dy = flux - k * y = 0.654 - 0.01 * 60
DLLfunc(y = Yini, times = 2, func = "scocder",
parms = parms, dllname = "deSolve",
initforc = "scocforc", forcings = Flux,
initfunc = "scocpar", nout = 2,
outnames = c("Mineralisation", "Depo"))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.