Plot of residuals versus linear predictor values

Usage

residuals_linpred_plot(
  model,
  type = NULL,
  ylab = NULL,
  xlab = NULL,
  title = NULL,
  subtitle = NULL,
  caption = NULL,
  point_col = "grey20",
  point_alpha = 1,
  line_col = "red",
  seed = NULL,
  by_family = NULL,
  rank = NULL,
  conditioning = NULL,
  ...,
  n_dharma = 250L
)

Arguments

model: a fitted model supported by diagnostic_support.
type: character or NULL; residual type: "deviance", "pearson", "response", "pit", "quantile", "dharma", or "surrogate" (for ocat() or multinom() GAMs). Grouped models automatically use quantile residuals when all active components are supported, otherwise all use deviance residuals with a message. Ordinary models retain deviance residuals by default. See appraise() for distributional support.
ylab: character or expression; the label for the y axis. If not supplied, a suitable label will be generated.
xlab: character or expression; the label for the x axis. If not supplied, a suitable label will be generated.
title: character or expression; the title for the plot. See ggplot2::labs().
subtitle: character or expression; the subtitle for the plot. See ggplot2::labs().
caption: character or expression; the plot caption. See ggplot2::labs().
point_col: Deprecated; use gratia_theme_get() theme settings instead. Constant point colour when components are not mapped to colour. Pooled grouped diagnostics instead colour points by component.
point_alpha: Deprecated; use gratia_theme_get() theme settings instead. Numeric; alpha transparency for points in plots.
line_col: Deprecated; use gratia_theme_get() theme settings instead. Colour specification for 1:1 line.
seed: integer; random seed to use for PIT, quantile, surrogate or DHARMa-style residuals.
by_family: logical or NULL; separate indexed grouped-family components into facets. The default pools quantile residuals with component colours and facets traditional residuals. TRUE and FALSE explicitly select the layout. In appraise(), observed-versus-fitted values always use component facets. Standalone observed_fitted_plot() facets by default and permits explicit pooling. Ignored for ordinary models.
rank: logical or NULL; use within-component ranks on the horizontal axis of residual-versus-predictor plots. The default ranks pooled grouped plots and retains original predictors in facets. TRUE or FALSE overrides this independently of the residual type and layout. Only finite observations included in each component determine its ranks; average ties are used.
conditioning: character or NULL; "conditional" holds fitted random effects fixed, "marginal" averages over new effects, and "gam_component" uses the extracted GAM. Availability and defaults depend on the model; see diagnostic_support.
...: optional arguments passed to model preparation, such as the sdmTMB field_method.
n_dharma: positive integer; number of response simulations for type = "dharma". This is separate from n_simulate, which controls traditional QQ reference calculations. Defaults to 250.

Details

The default subtitle identifies the response family. Conditioning and field choices appear in the caption where relevant. Supply caption to replace this text, or caption = "" to omit it. The scalable version uses the same labels.