Sets the controls for deterministic compartmental models
simulated with dcm().
Usage
control.dcm(
type,
nsteps,
dt = 1,
odemethod = "lsoda",
dede = FALSE,
new.mod = NULL,
sens.param = TRUE,
print.mod = FALSE,
verbose = FALSE,
ode.args = NULL,
solver = NULL,
...
)Arguments
- type
Disease type to be modeled, with the choice of
"SI"for Susceptible-Infected diseases,"SIR"for Susceptible-Infected-Recovered diseases, and"SIS"for Susceptible-Infected-Susceptible diseases.- nsteps
Number of time steps to solve the model over or vector of times to solve the model over. If the number of time steps, then this must be a positive integer of length 1.
- dt
Time unit for model solutions, with the default of 1. Model solutions for fractional time steps may be obtained by setting this to a number between 0 and 1.
- odemethod
Ordinary differential equation (ODE) integration method, with the default of
"lsoda"(seedeSolve::odefor other options).- dede
If
TRUE, use the delayed differential equation solver, which allows for time-lagged variables.- new.mod
If not running a base model type, a function with a new model to be simulated (see details).
- sens.param
If
TRUE, evaluate arguments in parameters or initial conditions with length greater than 1 as sensitivity analyses, with one model run per value. IfFALSE, one model will be run with parameters and initial conditions of arbitrary length (the model may error unless the model function is designed to accommodate vectors).- print.mod
If
TRUE, print the model form to the console.- verbose
If
TRUE, print model progress to the console.- ode.args
A named list of further arguments passed to
deSolve::ode(ordeSolve::dedewithdede = TRUE), such as the tolerancesrtolandatolor the maximum stephmax.- solver
An optional function that solves the model in place of
deSolve::ode, called assolver(y, times, func, parms, control)(see details).- ...
additional control settings passed to model.
Details
control.dcm sets the required control settings for any deterministic
compartmental models solved with the dcm() function. Controls are
required for both base model types and original models. For all base models,
the type argument is a necessary parameter and it has no default.
ODE Solver
As of EpiModel 2.5.5, the default ODE solver was changed from "rk4"
(fixed-step Runge-Kutta 4) to "lsoda" (adaptive step-size). The "lsoda"
method automatically adjusts its internal step size, providing numerical
stability across a wide range of parameter values. The fixed-step "rk4"
solver could produce numerical instability (negative compartment sizes, NaN
values) for models with high transmission rates or stiff dynamics. Users who
require the previous behavior can set odemethod = "rk4" explicitly.
New Model Functions
The form of the model function for base models may be displayed with the
print.mod argument set to TRUE. In this case, the model will
not be run. These model forms may be used as templates to write original
model functions.
These new models may be input and solved with dcm() using the
new.mod argument, which requires as input a model function.
Custom Solvers
By default, dcm() solves the model with one call to deSolve::ode
over all time steps, passing odemethod and any ode.args. A model whose
rates change abruptly (an intervention that starts on a given day, for
example) is solved more accurately by restarting the solver at each change.
The solver argument allows this, or any other solver, for base and new
models alike. It is called with the initial state y, the solution times
times, the model function func, the parameters parms, and the control
settings control, and must return a matrix or data frame with time in the
first column and one named column per state variable, as ode does. Any
further columns it returns, such as derived outputs, are kept as model
outputs. Columns whose names contain .flow are converted from cumulative
counts to counts per time step after the solver returns, as for the default
solver.
See also
Use param.dcm() to specify model parameters and
init.dcm() to specify the initial conditions. Run the
parameterized model with dcm().