Display the effect of sequential model-standardisation steps

Plot supplied fits, calculated diagnostics, or a stored influ_steps object. Set refit = TRUE with one original model to fit a sequence internally. The displayed quantities are year-effect contrasts, including when spatial processes are changed between fits; they are not area-integrated indices.

Usage

plot_step(
  fits,
  labels = NULL,
  year = NULL,
  fill = "purple4",
  probs = c(0.025, 0.975),
  show_probs = TRUE,
  steps = NULL,
  refit = FALSE,
  component = NULL,
  keep_fits = FALSE,
  refit_args = list(),
  ...
)

Arguments

fits: One supported fitted model, an influ_diag, or an ordered list of fitted models or diagnostics. An existing influ_steps is also accepted.
labels: Unique step labels. Defaults to list names or generated labels.
year: Focus-variable name, inferred from the first input when omitted.
fill: Colour used for the current model's interval.
probs: Interval probabilities for newly calculated diagnostics. Existing diagnostics retain their original intervals.
show_probs: Show each step's uncertainty interval. These are not intervals for differences between models.
steps: For refitting, an ordered, uniquely named list of cumulative formulas or lists of model-update arguments. Each specification updates the original model. Use formula in an argument list for its formula change. If omitted, ordinary single-component models start with the focus term and progressively add the remaining formula terms. Offsets are retained. Spatial models require explicit steps so field changes are deliberate.
refit: Explicitly allow new model fitting. Defaults to FALSE.
component: Index component. Must be supplied if more than one is available, for example "positive" or "unconditional_mean" in a hurdle model. Even a combined-component index remains a focus-effect contrast, not the full expected response integrated over space.
keep_fits: Retain fitted models in the result? Defaults to FALSE to keep the returned object small. Save expensive fits separately if needed.
refit_args: Named arguments passed to the model update at every step, with original fitting rows and offsets locked. For lme4 negative-binomial fits, explicitly supply list(reestimate_nb = TRUE) to use glmer.nb at each stage, or FALSE to keep the original size parameter. The saved native call cannot distinguish originally estimated from user-fixed size. Automatic lme4 sequences retain all random terms while adding fixed terms; changes to random structure require explicit step formulas, each with a random term. such as brms sampling controls. Stage-specific arguments take precedence. Execution-only controls (seed, cores, refresh, silent, and verbose) do not by themselves force an otherwise unchanged original fit to rerun.
...: Arguments passed to influ() for fitted-model inputs, such as uncertainty = "none", weights, or reference_data. All steps use the same calculation arguments. Not used for model-fitting controls.

Value

A ggplot2::ggplot() object.