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Use posterior estimation

Source: R/use_posterior.R
use_posterior.Rd

Use posterior estimation

Usage

use_posterior(
  x,
  update_omega = FALSE,
  update_cov = TRUE,
  update_eta = TRUE,
  .zero_re = NULL,
  simplify = TRUE
)

Arguments

x

A mapbayests object.

update_omega

Update the OMEGA matrix with the variance-covariance matrix of estimation (a logical, default is FALSE).

update_cov

Update the values of covariates with the individual values (a logical, default is TRUE).

update_eta

Update the values of ETA with the final estimates (a logical, default is TRUE).

.zero_re

Set all elements of the OMEGA or SIGMA matrix to zero. Default is "both" if update_omega is FALSE, "sigma" otherwise. (possible values are "both", "sigma", "omega", "none")

simplify

a logical. If TRUE (the default) and only one ID, one mrgmod is returned instead of a list of length 1

Value

a mrgmod, or a list of mrgmod if there is more than 1 ID

Details

This function takes the results of an estimation (i.e. a mapbayests object) and return a modified mrgmod in order to perform a posteriori simulations. Modifications are:

  • If update_eta is TRUE, the values of ETA are updated to the estimated values (instead of 0) in $PARAM.

  • If update_cov is TRUE, the covariates values are updated to the values of the individual (instead of default model values) in $PARAM.

  • If update_omega is TRUE, the values of OMEGA are updated with the variance-covariance matrix of estimation (i.e. an approximation of the a posteriori distribution) instead of the inter-individual variability (i.e. the a priori distribution). Use this command in order to derive a confidence interval of concentrations that reflects the uncertainty about parameter estimation when a large number of profiles are simulated. Note that if inter-individual variability was initially defined in multiple $OMEGA blocks in the model, they will be collapsed to a single full matrix (this is irreversible).

  • Depending on the values of .zero_re, the elements of $OMEGA or $SIGMA can be set to zero, whether you want to simulate one profile, or several in order to derive confidence/prediction intervals. It does not handle time-varying covariates: only the first value will be used as the individual value.

Examples

library(magrittr)
est <- mapbayest(exmodel())
est %>%
  use_posterior() %>%
  mrgsolve::ev(amt = 50000) %>%
  mrgsolve::mrgsim()
#> Model:  mrg_001 
#> Dim:    26 x 5 
#> Time:   0 to 24 
#> ID:     1 
#>     ID time   DEPOT CENTRAL    DV
#> 1:   1    0     0.0       0   0.0
#> 2:   1    0 50000.0       0   0.0
#> 3:   1    1 19771.4   28880 383.6
#> 4:   1    2  7818.2   38088 505.9
#> 5:   1    3  3091.5   39688 527.1
#> 6:   1    4  1222.5   38434 510.5
#> 7:   1    5   483.4   36198 480.8
#> 8:   1    6   191.1   33705 447.7