xpose.xtras (version 0.1.2)

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. tools:::Rd_expr_doi("doi:10.1002/cpt.2065")

Examples

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