Description Usage Arguments Details
This function sets the control settings for the stochastic
network models in the epimethods
package.
1 2 3 4 5 6 7 8 9 | control_het(simno = 1, nsteps = 100, start = 1, nsims = 1, ncores = 1,
par.type = "single", initialize.FUN = initialize_het,
aging.FUN = aging_het, cd4.FUN = cd4_het, vl.FUN = vl_het,
dx.FUN = dx_het, tx.FUN = tx_het, deaths.FUN = deaths_het,
births.FUN = births_het, resim_nets.FUN = simnet_het,
infection.FUN = infect_het, prev.FUN = prevalence_het,
verbose.FUN = verbose_het, module.order = NULL, save.nwstats = FALSE,
save.other = c("el", "attr"), verbose = TRUE, verbose.int = 1,
skip.check = TRUE, ...)
|
simno |
Simulation ID number. |
nsteps |
Number of time steps to simulate the model over in whatever unit
implied by |
start |
Starting time step for simulation |
nsims |
Number of simulations. |
ncores |
Number of parallel cores to use for simulation jobs, if using
the |
par.type |
Parallelization type, either of |
initialize.FUN |
Module to initialize the model at time 1. |
aging.FUN |
Module to age active nodes. |
cd4.FUN |
CD4 progression module. |
vl.FUN |
HIV viral load progression module. |
dx.FUN |
HIV diagnosis module. |
tx.FUN |
HIV treatment module. |
deaths.FUN |
Module to simulate death or exit. |
births.FUN |
Module to simulate births or entries. |
resim_nets.FUN |
Module to resimulate the network at each time step. |
infection.FUN |
Module to simulate disease infection. |
prev.FUN |
Module to calculate disease prevalence at each time step,
with the default function of |
verbose.FUN |
Module to print simulation progress to screen, with the
default function of |
module.order |
A character vector of module names that lists modules the
order in which they should be evaluated within each time step. If
|
save.nwstats |
Save out network statistics. |
save.other |
Other list elements of dat to save out. |
verbose |
If |
verbose.int |
Interval for printing progress to console. |
skip.check |
If |
... |
Additional arguments passed to the function. |
This function sets the parameters for the models.
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