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Objective function changes across models

Source: R/xset_plots.R
iofv_vs_mod.Rd

Another visualization of how individual objective functions change over the course of model development.

Usage

iofv_vs_mod(
  xpdb_s,
  ...,
  .lineage = FALSE,
  auto_backfill = FALSE,
  mapping = NULL,
  orientation = "x",
  type = "bjc",
  title = "Individual OFVs across models",
  subtitle = "Based on @nind individuals, Initial OFV: @ofv",
  caption = "Initial @dir",
  tag = NULL,
  axis.text = "@run",
  facets,
  .problem,
  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

auto_backfill

<logical> If TRUE, apply <backfill_iofv()> automatically. FALSE by default to encourage data control as a separate process to plotting control.

mapping

ggplot2 style mapping

orientation

Defaults to x

type

Passed to <xplot_boxplot>

title

Plot title

subtitle

Plot subtitle

caption

Plot caption

tag

Plot tag

axis.text

What to label the model. This is parsed on a per-model basis.

facets

Additional facets

.problem

Problem number

quiet

Silence output

Value

The desired plot

Examples

# \donttest{

pheno_set %>%
  focus_qapply(backfill_iofv) %>%
  iofv_vs_mod()
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, TIME, AMT, WT, APGR ... and 17 more variables


pheno_set %>%
  focus_qapply(backfill_iofv) %>%
  iofv_vs_mod(run3,run11,run14,run15)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, TIME, AMT, WT, APGR ... and 17 more variables


pheno_set %>%
  focus_qapply(backfill_iofv) %>%
  iofv_vs_mod(.lineage = TRUE)
#> Using data from $prob no.1
#> Removing duplicated rows based on: ID
#> Tidying data by ID, TIME, AMT, WT, APGR ... and 17 more variables


# }