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.
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 withall.vars()
.- .lineage
<
logical
> where ifTRUE
,...
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 foravg_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
pheno_set %>%
modavg_xpdb(
avg_cols = IPRED,
auto_backfill = TRUE,
algorithm = "maa",
weight_basis = "aic"
)
#>
#> ── ~ xp_xtras object
#> Model description: na
#> run3.lst overview:
#> - Software: nonmem 7.5.0
#> - Attached files (memory usage 843 Kb):
#> + obs tabs: $prob no.1 (modified): na, run3tab
#> + sim tabs: <none>
#> + output files: run3.cor, run3.cov, run3.ext, run3.grd, run3.phi, run3.shk
#> + special: <none>
#> - gg_theme: theme_readable
#> - xp_theme: xp_xtra_theme new_x$xp_theme
#> - Options: dir = ~/Step 3, quiet = FALSE, manual_import = NULL, cvtype = exact