Visual check of model various assumptions (normality of residuals, normality of random effects, linear relationship, homogeneity of variance, multicollinearity).

check_model(x, ...)

# S3 method for default
  dot_size = 2,
  line_size = 0.8,
  panel = TRUE,
  check = "all",
  alpha = 0.2,
  dot_alpha = 0.8,
  colors = c("#3aaf85", "#1b6ca8", "#cd201f"),
  theme = "see::theme_lucid",
  detrend = FALSE,
  verbose = TRUE,



A model object.


Currently not used.

dot_size, line_size

Size of line and dot-geoms.


Logical, if TRUE, plots are arranged as panels; else, single plots for each diagnostic are returned.


Character vector, indicating which checks for should be performed and plotted. May be one or more of "all", "vif", "qq", "normality", "linearity", "ncv", "homogeneity", "outliers", "reqq". "reqq" is a QQ-plot for random effects and only available for mixed models. "ncv" is an alias for "linearity", and checks for non-constant variance, i.e. for heteroscedasticity, as well as the linear relationship. By default, all possible checks are performed and plotted.

alpha, dot_alpha

The alpha level of the confidence bands and dot-geoms. Scalar from 0 to 1.


Character vector with color codes (hex-format). Must be of length 3. First color is usually used for reference lines, second color for dots, and third color for outliers or extreme values.


String, indicating the name of the plot-theme. Must be in the format "package::theme_name" (e.g. "ggplot2::theme_minimal").


Should QQ/PP plots be detrended?


Toggle off warnings.


The data frame that is used for plotting.


For Bayesian models from packages rstanarm or brms, models will be "converted" to their frequentist counterpart, using bayestestR::bayesian_as_frequentist. A more advanced model-check for Bayesian models will be implemented at a later stage.


This function just prepares the data for plotting. To create the plots, see needs to be installed. Furthermore, this function suppresses all possible warnings. In case you observe suspicious plots, please refer to the dedicated functions (like check_collinearity(), check_normality() etc.) to get informative messages and warnings.

Linearity Assumption

The plot Linearity checks the assumption of linear relationship. However, the spread of dots also indicate possible heteroscedasticity (i.e. non-constant variance); hence, the alias "ncv" for this plot. Some caution is needed when interpreting these plots. Although these plots are helpful to check model assumptions, they do not necessarily indicate so-called "lack of fit", e.g. missed non-linear relationships or interactions. Thus, it is always recommended to also look at effect plots, including partial residuals.

Residuals for (Generalized) Linear Models

Plots that check the normality of residuals (QQ-plot) or the homogeneity of variance use standardized Pearson's residuals for generalized linear models, and standardized residuals for linear models. The plots for the normality of residuals (with overlayed normal curve) and for the linearity assumption use the default residuals for lm and glm (which are deviance residuals for glm).


if (FALSE) {
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)

if (require("lme4")) {
  m <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
  check_model(m, panel = FALSE)

if (require("rstanarm")) {
  m <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)