R/calculate_parameters.R

In InsightRX/PKPDsim: Tools for Performing Pharmacokinetic-Pharmacodynamic Simulations

#' Calculate model-specific variables using a dummy call to sim_ode()
#'
#' This is a convenience function for PKPDsim users, it is not used inside the
#' `sim_ode()`` function in any way. This function is useful for converting from
#' an estimated parameter to actual parameter, e.g. when clearance is specified
#' as `CLi = CL * (WT/70) * (1/CR)` it can be used to calculate `CLi` without
#' having to write that function a second time in R.
#'
#' @param ode PKPDsim model object
#' @param parameters parameter list
#' @param covariates covariate list. Make sure to include covariates at the
#'   right time point, since only last observed covariate values are used.
#' @param regimen optional, provide a `regimen` object for the computation of
#'   the effective parameters. This is only relevant for models for which
#'   parameters depend on the dose or administration type, which is rare.
#' @param t_obs optional, provide timepoint(s) at which to computate effective
#'   parameters. This is only relevant for models with time-varying
#'   fixed-effects. If unspecified, will evaluate parameters at `t=0`.
#' @param include_parameters boolean, include parameters?
#' @param include_variables boolean, include variables?
#' @param ... arguments to pass on to simulation function
#' @export
#' @return List of model-specific variables
calculate_parameters <- function(
  ode = NULL,
  parameters = NULL,
  covariates = NULL,
  include_parameters = TRUE,
  include_variables = TRUE,
  regimen = NULL,
  t_obs = NULL,
  ...
) {
  if(is.null(regimen)) { # then just use dummy regimen.
    regimen <- new_regimen(amt = 0, times = 0, cmt = 1, type = "bolus")
  }
  if(is.null(t_obs)) {
    t_obs_sim <- c(0, 1)
  } else {
    t_obs_sim <- t_obs
  }
  t_obs_sim <- round(t_obs_sim, 3)
  incl <- list(parameters = FALSE, variables = FALSE)
  if(!is.null(covariates)) covariates <- covariate_last_obs_only(covariates) # make sure only single value!
  if(include_parameters) incl$parameters <- TRUE
  if(include_variables) incl$variables <- TRUE
  res <- sim_ode(
    ode = ode,
    parameters = parameters,
    regimen = regimen,
    omega = NULL,
    covariates = covariates,
    output_include = incl,
    t_obs = t_obs_sim,
    only_obs = TRUE,
    ...
  )
  if(!is.null(t_obs)) {
    res <- res[res$t %in% t_obs_sim, ]
  } else {
    res <- res[nrow(res),]
  }
  return(as.list(res[, -c(1:4)]))
}