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Model average plots

Source: R/xset_plots.R
modavg_plots.Rd

[Experimental]

This is for use when the model averaging of a set is planned.

Usage

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

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

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

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

plotfun_modavg(
  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",
  .fun = NULL,
  .funargs = list(),
  quiet
)

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

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.

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.

.fun

<function> For slightly more convenient piping of model-averaged xpose_data into a plotting function.

.funargs

<list> Extra args to pass to function. If passing tidyselect arguments, be mindful of where quosures might be needed. See Examples.

Value

The desired plot

See also

modavg_xpdb()

Examples

# \donttest{

pheno_set %>%
  dv_vs_ipred_modavg(run8,run9,run10, auto_backfill = TRUE)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'


pheno_set %>%
  dv_vs_pred_modavg(run8,run9,run10, auto_backfill = TRUE)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'


pheno_set %>%
  ipred_vs_idv_modavg(run8,run9,run10, auto_backfill = TRUE)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'


pheno_set %>%
  pred_vs_idv_modavg(run8,run9,run10, auto_backfill = TRUE)
#> Using data from $prob no.1
#> Filtering data by EVID == 0
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'


# Model averaged ETA covariates
pheno_set %>%
  plotfun_modavg(run8,run9,run10, auto_backfill = TRUE,
     avg_by_type = "eta",.fun = eta_vs_catcov,
     # Note quoting
     .funargs = list(etavar=quote(ETA1)))
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, TIME, AMT, WT, MDV ... and 31 more variables


# }