mrgsim: Simulate from a model object

Description

This function sets up the simulation run from data stored in the model object as well as arguments passed in. Note that there are several non-formal arguments to this function that can be used to customize the simulation run and it's output. Use mrgsim_df to return a data frame rather than mrgsims object.

Usage

mrgsim(x, data = NULL, idata = NULL, events = NULL, nid = 1, ...)
mrgsim_df(...)

Value

an object of class mrgsims

Additional arguments

Request a vector of compartment or table names to take in simulated output; if this is specified, request is ignored
obsonly omit records with evid != 0 from simulated output
obsaug logical; when TRUE and a full data set is used, the simulated output is augmented with an observation at each time in stime(). When using obsaug, a flag indicating augmented observations can be requested by including a.u.g in carry.out
recsort Default value is 1. Possible values are 1,2,3,4: 1 and 2 put doses in a data set after padded observations at the same time; 3 and 4 put those doses before padded observations at the same time. 2 and 4 will put doses scheduled through addl after observations at the same time; 1 and 3 put doses scheduled through addl before observations at the same time. recsort will not change the order of your input data set if both doses and observations are given.
filbak For each ID, carry the first record data backward to start of the simulation
tad logical; when TRUE a column is added to simulated output is added showing the time since the last dose. Only data records with evid == 1 will be considered doses for the purposes of tad calculation. The tad can be properly calculated with a dosing lag time in the model as long as the dosing lag time (specified in $MAIN) is always appropriate for any subsequent doses scheduled through addl. This will always be true if the lag time doesn't change over time. But it might (possibly) not hold if the lag time changes prior to the last dose in the addl sequence. This known limitation shouldn't affect tad calculation in most common dosing lag time implementations.
nocb if TRUE (default), time-varying items in a data set will be implemented as next observation carried back; if FALSE time-varying items in a data set will be implemented as last observation carried forward.

Details

Both data and idata will be coreced to numeric matrix
carry.out can be used to insert data columns into the output data set. This is partially dependent on the nature of the data brought into the problem.
When using data and idata together, an error is generated if an ID occurs in data but not idata. Also, when looking up data in idata, ID in idata is assumed to be uniquely keyed to ID in data. No error is generated if ID is duplicated in data; parameters will be used from the first occurrence found in idata.
carry.out: idata is assumed to be individual-level and variables that are carried from idata are repeated throughout the invidivual's simulated data. Variables carried from data are carried via last-observation carry forward. NA is returned from observations that are inserted into simulated output that occur prior to the first record in data.

Examples

Run this code

# NOT RUN {
## example("mrgsim")

e <- ev(amt = 1000)
mod <- mrgsolve:::house() 
out <- mod %>% ev(e) %>% mrgsim()
plot(out)

out <- mod %>% ev(e) %>% mrgsim(end=22)
out

data(exTheoph)

out <- mod %>% data_set(exTheoph) %>% mrgsim()
out

out <- mod %>% mrgsim(data=exTheoph)

out <- mrgsim(mod, data=exTheoph, obsonly=TRUE)
out

out <- mod %>% mrgsim(data=exTheoph, obsaug=TRUE, carry.out="a.u.g")
out

out <- mod %>% ev(e) %>% mrgsim(req="CENT")
out

out <- mrgsim(mod, Req="CP,RESP", events = e)
out


# }