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Model interface for exposure-response plots

Source: R/er-generics.R
er_model_interface.Rd

erplots draws exposure-response plots from any fitted model that implements this small interface, rather than assuming a particular model class (e.g. a logistic regression glm). To make a model usable with er_plot_show_model() and friends, implement at least a method for er_predict(). Implementing er_simulate() additionally enables simulation-based visualisations (e.g. spaghetti plots, VPCs); implementing er_summary() enables annotations such as p-value labels.

Usage

er_predict(model, newdata, conf_level = 0.95, ...)

er_simulate(model, newdata, nsim = 100, seed = NULL, ...)

er_summary(model, ...)

Arguments

model

A fitted exposure-response model object

newdata

A data frame of covariate values at which to predict

conf_level

Confidence level for the prediction interval

...

Passed to methods

nsim

Number of simulation replicates

seed

Optional RNG seed

Value

  • er_predict() returns newdata with three additional columns: fit_resp (point prediction), ci_lower, and ci_upper.

  • er_simulate() returns a data frame containing nsim replicates of newdata, with a sim_id column identifying each replicate, and a fit_resp column giving the simulated prediction for that replicate (reflecting parameter uncertainty). Models that cannot support simulation-based visualisation should not implement a method; the default method returns NULL; callers should treat a NULL result as "not available" rather than an error.

  • er_summary() returns a named list of scalar summary statistics (for example list(p_value = 0.013)), or NULL if nothing is available. The default method returns NULL.