Basic, simple simulation from model object

This is just a lighter version of mrgsim(), with fewer options but with better efficiency in certain cases. See Details.

Usage

qsim(
  x,
  data,
  idata = no_idata_set(),
  obsonly = FALSE,
  tgrid = NULL,
  recsort = 1,
  tad = FALSE,
  Req = NULL,
  outvars = Req,
  skip_init_calc = FALSE,
  output = "mrgsims"
)

Arguments

x

the model object.

data

can be either event object or data set.

idata

a matrix or data frame of model parameters, one parameter per row (see idata_set()).

obsonly

if TRUE, dosing records are not included in the output.

tgrid

a tgrid object; or a numeric vector of simulation times or another object with an stime method.

recsort

record sorting flag. 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.

tad

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.

Req

synonym for outvars.

outvars

output items to request; if missing, then only captured items will be returned in the output.

skip_init_calc

don't use $MAIN to calculate initial conditions.

output

output data type; the default is mrgsims, which returns the default output object; other options include df (for data.frame) or matrix.

Details

qsim() mainly cuts some of the overhead from the simulation. So, the primary efficiency gain from using qsim() comes when the simulation executes very quickly. It is unlikely you will see a big performance difference between qsim() and mrgsim() when the model is difficult to solve or if there is a large input data set.

There is no pipeline interface for this function; all configuration options (see Arguments) must be passed as formal arguments to the function. You can't carry_out, Request specific columns, or pass items in for update. Some other limitations, but only convenience-related. See Arguments for available options. Specifically, there is no ... argument for this function. Use the update() method to update the model object.

See also

mrgsim_q(), mrgsim(), mrgsim_variants

Examples

mod <- mrgsolve::house()

dose <- ev(amt = 100)

out <- qsim(mod,dose)