Produce residual plots of linear models — resplot • biometryassist

Produces plots of residuals for assumption checking of linear (mixed) models.

Usage

resplot(
  model.obj,
  shapiro = TRUE,
  call = FALSE,
  label.size = 10,
  axes.size = 10,
  call.size = 9,
  onepage = FALSE,
  onepage_cols = 3,
  mod.obj
)

Arguments

model.obj: A fitted model object of a supported class (aov, lm, lme (nlme::lme()), lmerMod (lme4::lmer()), asreml, mmer/mmes (sommer) or art (ARTool)). See the Supported model types section.
shapiro: (Logical) Display the Shapiro-Wilk test of normality on the plot? This test is unreliable for larger numbers of observations and will not work with n >= 5000 so will be omitted from any plots.
call: (Logical) Display the model call on the plot?
label.size: A numeric value for the size of the label (A,B,C) font point size.
axes.size: A numeric value for the size of the axes label font size in points.
call.size: A numeric value for the size of the model displayed on the plot.
onepage: (Logical) If TRUE and there are multiple plots, combines up to 6 plots per page.
onepage_cols: Integer. Number of columns to use in grid layout when onepage=TRUE. Default is 3.
mod.obj: Deprecated to be consistent with other functions. Please use model.obj instead.

Value

A ggplot2 object containing the diagnostic plots.

Supported model types

resplot() produces residual diagnostics for any model with an extract_model_info() method. These are currently:

Model class	Fitted by	Notes
`aov`, `lm`	`stats::aov()`, `stats::lm()`	Fixed-effects linear models.
`aovlist`	`stats::aov()` with an `Error()` term	Multi-stratum aov; each error stratum (except the intercept) is shown as a separate plot.
`lme`	`nlme::lme()`	Linear mixed model.
`lmerMod`	`lme4::lmer()`, `lme4breeding::lmebreed()`	Linear mixed model. `lmebreed()` (relationship-based) models also carry class `lmerMod`; their residuals/fitted values are on the response scale, so the diagnostics are valid.
`lmerModLmerTest`	`lmerTest::lmer()`	As `lmerMod`.
`asreml`	ASReml-R `asreml()`	Linear mixed model (commercial; not on CRAN). Residual strata are shown as separate plots.
`mmer`, `mmes`	sommer `mmer()` / `mmes()`	Linear mixed model.
`art`	`ARTool::art()`	Aligned rank transform model.
`afex_aov`	afex `aov_car()` / `aov_ez()` / `aov_4()`	Factorial / repeated-measures ANOVA; a single diagnostic panel from the model residuals.
`glmmTMB`	glmmTMB `glmmTMB()`	Gaussian family only. Non-Gaussian families error with a pointer to `DHARMa::simulateResiduals()`, since a normal Q-Q plot is not a valid diagnostic for them.

This set differs slightly from the comparison functions (see get_predictions()): resplot() additionally supports ARTool (art) models and sommer's legacy mmer interface. Neither is available for the comparison functions — ART uses aligned ranks (use ARTool::art.con()), and current sommer provides no predict() for mmer (refit with sommer::mmes()).

To add a new engine, write an extract_model_info.<class>() method returning a list with elements facet, facet_name, resids, fits, k and model_call, and add a row to the table above.

Examples

dat.aov <- aov(Petal.Length ~ Petal.Width, data = iris)
resplot(dat.aov)

resplot(dat.aov, call = TRUE)