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
check_model(
x,
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,
show_dots = NULL,
verbose = TRUE,
...
)
The data frame that is used for plotting.
A model object.
Currently not used.
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", "pp_check",
"binned_residuals" or "overdispersion", Not that not all check apply
to all type of models (see 'Details'). "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.
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?
Logical, if TRUE, will show data points in the plot. Set
to FALSE for models with many observations, if generating the plot is too
time-consuming. By default, show_dots = NULL. In this case check_model()
tries to guess whether performance will be poor due to a very large model
and thus automatically shows or hides dots.
Toggle off warnings.
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.
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).
For models with many observations, or for more complex models in general,
generating the plot might become very slow. One reason might be that the
underlying graphic engine becomes slow for plotting many data points. In
such cases, setting the argument show_dots = FALSE might help. Furthermore,
look at the check argument and see if some of the model checks could be
skipped, which also increases performance.
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.
if (FALSE) {
m <- lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
check_model(m)
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)
check_model(m)
}
}
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