These functions are called by mrgsim()
and have
explicit input requirements written into the function name. The motivation
behind these variants is to give the user a clear workflow with specific,
required inputs as indicated by the function name. Use
mrgsim_q()
instead to benchmark mrgsolve or to do repeated quick
simulation for tasks like parameter optimization, sensitivity analyses,
or optimal design.
Usage
mrgsim_e(x, events, idata = NULL, data = NULL, ...)
mrgsim_d(x, data, idata = NULL, events = NULL, ...)
mrgsim_ei(x, events, idata, data = NULL, ...)
mrgsim_di(x, data, idata, events = NULL, ...)
mrgsim_i(x, idata, data = NULL, events = NULL, ...)
mrgsim_0(x, idata = NULL, data = NULL, events = NULL, ...)
Arguments
- x
the model object
- events
an event object
- idata
a matrix or data frame of model parameters, one parameter per row (see
idata_set()
)- data
NMTRAN-like data set (see
data_set()
)- ...
passed to
update()
anddo_mrgsim()
Details
Important: all of these functions require that
data
, idata
, and/or events
be pass directly to the functions. They
will not recognize these inputs from a pipeline.
mrgsim_e
simulate using an event objectmrgsim_ei
simulate using an event object andidata_set
mrgsim_d
simulate using adata_set
mrgsim_di
simulate using adata_set
andidata_set
mrgsim_i
simulate using aidata_set
mrgsim_0
simulate using just the modelmrgsim_q
simulate from a data set with quicker turnaround (seemrgsim_q()
)