er_plot: The exposure-response plotting mini-language

In erplots: Model-Agnostic Exposure-Response Plots

The exposure-response plotting mini-language

Description

Create an er_plot specification for exposure-response visualization. Build the plot by adding layers (model, summary, quantiles, data, groups) and render with plot()/print() or er_plot_build().

Usage

er_plot(data, exposure, response, stratify_by = NULL, response_type = "auto")

Arguments

data	Data frame or tibble containing the observed data.
exposure	Exposure variable (one variable, unquoted).
response	Response variable (one variable, unquoted).
stratify_by	Stratification variable used for colour and fill (one variable, unquoted).
response_type	One of "auto", "binary", "continuous", or "count".

Details

Layers are either singleton or additive: model, summary, quantile, and data layers are singleton (a second call replaces the previous); groups are additive (each call adds a panel).

stratify_by declares a discrete variable used for colour/fill across layers; each layer's keep_strata controls whether it uses stratification. Rows with NA in the stratification variable are kept as their own level. A numeric stratify_by errors – bin it yourself first with cut_quantile()/cut_exposure_quantile() and pass the resulting factor.

response_type governs response-scale defaults and which interval method the quantile and VPC layers use; see response_type below and er_plot_add_quantiles() for details.

Value

An (empty) plot object of class er_plot.

See Also

er_plot_add_model(), er_plot_add_summary(), er_plot_add_quantiles(), er_plot_add_data(), er_plot_add_groups(), er_plot_build(), er_plot_theme(), er_model_interface

Examples

if (requireNamespace("erglm", quietly = TRUE)) {
library(erglm)
mod <- erglm_model(ae1 ~ aucss, erglm_data, family = binomial())

erglm_data |>
  er_plot(aucss, ae1) |>
  er_plot_add_model(mod) |>
  er_plot_add_quantiles() |>
  er_plot_add_groups(aucss) |>
  plot()
}

erplots documentation built on Oct. 4, 2026, 5:06 p.m.