Core iterative simulation and MAP estimation function that loops over an individual's dataset

Core iterative simulation and MAP estimation function that loops over an individual's dataset

run_eval_core(
  mod_obj,
  data,
  weights = NULL,
  weight_prior = 1,
  censor_covariates = TRUE,
  incremental = FALSE,
  progress_function = function() {
 },
  .fit_options = NULL
)

Arguments

mod_obj

list object with model information

data

NONMEM-style data.frame, or path to CSV file with NONMEM data

weights

vector of weights for error. Length of vector should be same as length of observation vector. If NULL (default), all weights are equal. Used in both MAP and NP methods. Note that `weights` argument will also affect residuals (residuals will be scaled too).

weight_prior

weighting of priors in relationship to observed data, default = 1

censor_covariates

with the proseval tool in PsN, there is “data leakage” (of future covariates data): since the NONMEM dataset in each step contains the covariates for the future, this is technically data leakage, and could result in an over-optimistic estimate of predictive performance. In mipdeval, covariate censoring of future covariate data is switched on by default (so no data leakage), but it can be switched off if we want to match the behavior of PsN::proseval exactly.

incremental

should MAP Bayesian do incremental fits in the iterative loop? I.e. in this case it would use the first iterations MAP Bayesian estimates as input for the second iteration, and so forth. The uncertainty around the MAP estimates would be used as the new omega matrix. This approach has been called "model predictive control (MPC)" (www.page-meeting.org/?abstract=9076) and may be more predictive than "regular" MAP in some scenarios. Default is FALSE.

progress_function

function to increment progress bar

.fit_options

Options for controlling MAP Bayesian fit. This must be the result from a call to fit_options().

Value

a data.frame with individual predictions