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_esimulate using an event objectmrgsim_eisimulate using an event object andidata_setmrgsim_dsimulate using adata_setmrgsim_disimulate using adata_setandidata_setmrgsim_isimulate using aidata_setmrgsim_0simulate using just the modelmrgsim_qsimulate from a data set with quicker turnaround (seemrgsim_q())