modavg_xpdb: Create a model-averaged xpose data object

Description

This function is a helper for plotting functions where models in an xpose_set can be averaged together. The implementation attempts to match and extend from the cited prior work.

Usage

modavg_xpdb(
  xpdb_s,
  ...,
  .lineage = FALSE,
  avg_cols = NULL,
  avg_by_type = NULL,
  algorithm = c("maa", "msa"),
  weight_type = c("individual", "population"),
  auto_backfill = FALSE,
  weight_basis = c("ofv", "aic", "res"),
  res_col = "RES",
  quiet
)

Value

Weight-averaged <xpose_data> object.

Arguments

xpdb_s: <xpose_set> object
...: <tidyselect> of models in set. If empty, all models are used in order of their position in the set. May also use a formula, which will just be processed with all.vars().
.lineage: <logical> where if TRUE, ... is processed
avg_cols: <tidyselect> columns in data to average
avg_by_type: <character> Mainly for use in wrapper functions. Column type to average, but resulting column names must be valid for avg_cols (ie, same across all objects in the set). avg_cols will be overwritten.
algorithm: <character> Model selection or model averaging
weight_type: <character> Individual-level averaging or by full dataset.
auto_backfill: <logical> If true, <backfill_iofv> is automatically applied.
weight_basis: <character> Weigh by OFV (default), AIC or residual.
res_col: <character> Column to weight by if "res" weight basis.
quiet: <logical> Minimize extra output.

References

Uster, D.W., Stocker, S.L., Carland, J.E., Brett, J., Marriott, D.J.E., Day, R.O. and Wicha, S.G. (2021), A Model Averaging/Selection Approach Improves the Predictive Performance of Model-Informed Precision Dosing: Vancomycin as a Case Study. Clin. Pharmacol. Ther., 109: 175-183. https://doi.org/10.1002/cpt.2065

Examples

Run this code


pheno_set %>%
  modavg_xpdb(
    avg_cols = IPRED,
    auto_backfill = TRUE,
    algorithm = "maa",
    weight_basis = "aic"
  )