Simulate from a NLME model

Simulates observations from a parsed model with between-subject variability and residual error. When a fit is supplied, the fitted theta, omega, and sigma replace the model file's initial values — which is the usual flow after ferx_fit (e.g. for posterior-predictive checks or VPCs).

ferx_simulate(model, data, n_sim = 1L, seed = 42L, fit = NULL)

Arguments

model

Path to a .ferx model file

data

Path to a NONMEM-format CSV (provides population structure: doses, obs times)

n_sim

Number of simulation replicates

seed

Random seed for reproducibility

fit

Optional ferx_fit result. When provided, simulation uses fit$theta, fit$omega, and fit$sigma instead of the model file's initial values.

Value

A data.frame with columns: SIM, ID, TIME, IPRED, DV_SIM

Examples

ex <- ferx_example("warfarin")
fit <- ferx_fit(ex$model, ex$data, method = "gn", covariance = FALSE)
#> Warning: Model file [fit_options] sets `method = foce` but ferx_fit() argument overrides it with `gn`. The call-time value will be used.
#> Warning: Model file [fit_options] sets `covariance = true` but ferx_fit() argument overrides it with `false`. The call-time value will be used.
#> Mu-referencing detected for: ETA_CL, ETA_KA, ETA_V
#> Negative IWRES autocorrelation detected (Durbin-Watson = 2.61, lag-1 r = -0.37).
#> Possible over-parameterisation or misspecified residual error model.
sim <- ferx_simulate(ex$model, ex$data, n_sim = 10L, seed = 1L, fit = fit)
head(sim)
#>   DRAW SIM ID TIME     IPRED    DV_SIM
#> 1    1   1  1  0.5  3.563045  3.539832
#> 2    1   1  1  1.0  6.032609  5.974273
#> 3    1   1  1  2.0  8.892847  9.074113
#> 4    1   1  1  4.0 10.703127 10.685702
#> 5    1   1  1  8.0 10.474217 10.522614
#> 6    1   1  1 12.0  9.659228  9.664573