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Observed-layer builders for VPC plots

Source: R/er-vpc-style-observed.R
er_style_vpc_observed.Rd

Builder functions for the observed layer (er_vpc_add_observed()), drawing the observed side of a visual predictive check as a mean/rate + confidence interval per bin (the default, adaptive to plot_by's type), a continuous-x line of empirical percentiles, or a point/interval per bin and per requested percentile.

Usage

er_style_vpc_observed_quantile_line(
  data,
  config,
  exposure,
  response,
  theme,
  point_size = 1.5,
  ...
)

er_style_vpc_observed_quantile_errorbar(
  data,
  config,
  exposure,
  response,
  theme,
  point_size = 1.5,
  errorbar_width = NULL,
  dodge = 0,
  prob_dodge_width = 0,
  ...
)

er_style_vpc_observed_mean_errorbar(
  data,
  config,
  exposure,
  response,
  theme,
  point_size = 2,
  errorbar_width = NULL,
  dodge = 0,
  show_label = FALSE,
  label_size = 3,
  ...
)

Arguments

data

The original data frame.

config

Configuration for the observed layer.

exposure

Exposure variable.

response

Response variable.

theme

Theme components.

point_size

Point size for all three point/interval builders. Defaults to 1.5 for er_style_vpc_observed_quantile_line()/ er_style_vpc_observed_quantile_errorbar(), or 2 for er_style_vpc_observed_mean_errorbar().

...

Additional named arguments forwarded from er_vpc_add_observed()'s own ....

errorbar_width

Width of er_style_vpc_observed_mean_errorbar()'s and er_style_vpc_observed_quantile_errorbar()'s error bars. Interpreted differently depending on plot_by's type: for a categorical plot_by, it's a bar width in the same implied unit-scaled category-gap units ggplot2::geom_errorbar() normally expects; for a numeric plot_by, it's a fraction of plot_by's own range (config$group_limits), so 1 would span the full range. Defaults to NULL, which resolves to 0.15 (er_style_vpc_observed_quantile_errorbar()) or 0.2 (er_style_vpc_observed_mean_errorbar()) for a categorical plot_by, or 0.025 for a numeric one.

dodge

Horizontal offset (as a fraction of plot_by's own range, like errorbar_width for a numeric plot_by) applied to all of this builder's error bars/points, for both er_style_vpc_observed_mean_errorbar() and er_style_vpc_observed_quantile_errorbar(). Default 0 (no offset, the previous behaviour). Useful for manually separating the observed layer from an overlapping simulated one at the same bin – e.g. dodge = -0.01 on the observed builder paired with dodge = 0.01 on the corresponding simulated builder. Only supported when plot_by is numeric; a nonzero value is ignored with a warning for a categorical plot_by, where dodging isn't implemented yet.

prob_dodge_width

Horizontal spread (as a fraction of plot_by's own range) applied to er_style_vpc_observed_quantile_errorbar()'s requested probs within a single bin, symmetrically centred on that bin's own position (added on top of dodge, if also supplied). Default 0 (all probs plotted at the same position, the previous behaviour). Useful when several probs' error bars overlap enough to be unreadable. Same numeric-plot_by-only restriction as dodge.

show_label

For er_style_vpc_observed_mean_errorbar() only: whether to draw config$summary's y_mid_lbl (the rate/mean, formatted via er_vpc_theme()'s format_percent/format_number) as a text label just above each point's upper CI bound. Default FALSE (no label, the previous behaviour).

label_size

Text size for show_label's label. Defaults to 3.

Value

A list of geoms; see er_style().

Details

See er_style_vpc() for the shared interface every VPC-grammar builder implements.

Choosing a builder

All three builders plot the observed side of a bin against the simulated side drawn by their er_style_vpc_simulated() counterpart; which one to reach for depends on how much of the response's distribution you need to see, and what kind of response/plot_by you have:

  • er_style_vpc_observed_mean_errorbar() (the default) – one point + confidence interval per bin, summarising the rate/mean only. Works for every response type and either kind of plot_by. Start here unless you specifically need percentile bands.

  • er_style_vpc_observed_quantile_line() – a continuous line per requested percentile, for a fuller picture of the response's distribution across bins. Requires a continuous/count response and a numeric plot_by; pairs with er_style_vpc_simulated_quantile_ribbon().

  • er_style_vpc_observed_quantile_errorbar() – a point + interval per requested percentile per bin, the discrete-bin analogue of the line idiom above. Same response-type restriction, but also works with a categorical plot_by; pairs with er_style_vpc_simulated_quantile_errorbar().

Mean/errorbar (default)

er_style_vpc_observed_mean_errorbar() plots config$summary's rate/mean + confidence interval, adapting its x-position to plot_by's type (config$is_numeric_group): equally spaced at each bin's categorical (or quantile-bin) label when plot_by is categorical, or at each bin's numeric median (x_median, from config$summary) on plot_by's own numeric scale when plot_by is numeric. Because it adapts its x-position family at build time rather than declaring one statically, it carries no vpc_layout tag – pair it with er_style_vpc_simulated_mean_errorbar(), which mirrors the same adaptive logic.

Percentile line

er_style_vpc_observed_quantile_line() plots config$percentiles – one line per requested percentile – at each bin's numeric midpoint on plot_by's own numeric scale, for pairing with er_style_vpc_simulated_quantile_ribbon(). config$percentiles is only computed for a continuous/count response (see er_vpc()'s probs argument); calling er_style_vpc_observed_quantile_line() without it errors.

Percentile errorbar

er_style_vpc_observed_quantile_errorbar() plots config$percentiles – a point + confidence interval (via ci_quantile()) for each requested percentile – for pairing with er_style_vpc_simulated_quantile_errorbar(). Like er_style_vpc_observed_mean_errorbar(), it adapts its x-position to plot_by's type (config$is_numeric_group): equally spaced at each bin's categorical (or quantile-bin) label when plot_by is categorical, or at each bin's numeric median (x_median, from config$percentiles) on plot_by's own numeric scale when plot_by is numeric. Because it adapts its x-position family at build time rather than declaring one statically, it carries no vpc_layout tag. Unlike er_style_vpc_observed_quantile_line()/ er_style_vpc_simulated_quantile_ribbon(), it supports a categorical plot_by as well as a numeric one; like it, it requires a continuous/count response (a binary response's distribution is already fully described by its rate) and errors informatively without config$percentiles. When more than one percentile is requested, all of them are currently plotted at the same x-position within a bin rather than dodged apart, so overlapping error bars/points are only distinguishable by their y-position – dodging support may be added in a future release.

Legends and overlap

Each builder maps a constant color = "Observed", so ggplot2 merges its legend entry with whatever the paired simulated-layer builder maps for "Simulated" into a single combined legend.

In the worst case – er_style_vpc_observed_quantile_errorbar() paired with er_style_vpc_simulated_quantile_errorbar() for a numeric plot_by with several probs – up to 2 * length(probs) error bars land at the exact same x-position within a bin (every probs value, for both the observed and simulated layers), which can be unreadable. dodge (separating the observed and simulated layers) and prob_dodge_width (spreading a single layer's own probs apart) are both opt-in, manual escape hatches for this – see their own argument docs above. Neither is automatic, because which collision is actually occurring (source-vs-source, probs-vs-probs, or both) depends on the data at hand.

See also

er_style_vpc(), er_style_vpc_simulated()

Examples

if (requireNamespace("erglm", quietly = TRUE)) {
  library(erglm)
  mod <- erglm_model(ae2 ~ aucss + sex, erglm_data, family = binomial())

  # er_style_vpc_observed_mean_errorbar(): the default, adaptive to
  # plot_by's type
  erglm_data |>
    er_vpc(aucss, ae2, plot_by = aucss) |>
    er_vpc_add_observed(style = er_style_vpc_observed_mean_errorbar) |>
    er_vpc_add_simulated(model = mod, seed = 6203, style = er_style_vpc_simulated_mean_errorbar) |>
    plot()

  # er_style_vpc_observed_quantile_line(): continuous-x percentile
  # lines, paired with the matching simulated ribbon builder
  mod2 <- erglm_model(biomarker_change ~ aucss, erglm_data, family = gaussian())
  erglm_data |>
    er_vpc(aucss, biomarker_change, plot_by = aucss) |>
    er_vpc_add_observed(style = er_style_vpc_observed_quantile_line) |>
    er_vpc_add_simulated(
      model = mod2, seed = 8417, style = er_style_vpc_simulated_quantile_ribbon
    ) |>
    plot()
}