check_model  R Documentation 
Visual check of various model assumptions (normality of residuals, normality of random effects, linear relationship, homogeneity of variance, multicollinearity).
check_model(x, ...)
## Default S3 method:
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 = TRUE,
show_dots = NULL,
bandwidth = "nrd",
type = "density",
verbose = FALSE,
...
)
x 
A model object. 
... 
Arguments passed down to the individual check functions, especially
to 
dot_size, line_size 
Size of line and dotgeoms. 
panel 
Logical, if 
check 
Character vector, indicating which checks for should be performed
and plotted. May be one or more of 
alpha, dot_alpha 
The alpha level of the confidence bands and dotgeoms. Scalar from 0 to 1. 
colors 
Character vector with color codes (hexformat). 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. 
theme 
String, indicating the name of the plottheme. Must be in the
format 
detrend 
Logical. Should QQ/PP plots be detrended? Defaults to

show_dots 
Logical, if 
bandwidth 
A character string indicating the smoothing bandwidth to
be used. Unlike 
type 
Plot type for the posterior predictive checks plot. Can be 
verbose 
If 
For Bayesian models from packages rstanarm or brms,
models will be "converted" to their frequentist counterpart, using
bayestestR::bayesian_as_frequentist
.
A more advanced modelcheck for Bayesian models will be implemented at a
later stage.
See also the related vignette.
The data frame that is used for plotting.
Posterior predictive checks can be used to look for systematic discrepancies
between real and simulated data. It helps to see whether the type of model
(distributional family) fits well to the data. See check_predictions()
for further details.
The plot Linearity checks the assumption of linear relationship.
However, the spread of dots also indicate possible heteroscedasticity (i.e.
nonconstant variance, hence, the alias "ncv"
for this plot), thus it shows
if residuals have nonlinear patterns. This plot helps to see whether
predictors may have a nonlinear relationship with the outcome, in which case
the reference line may roughly indicate that relationship. A straight and
horizontal line indicates that the model specification seems to be ok. But
for instance, if the line would be Ushaped, some of the predictors probably
should better be modeled as quadratic term. See check_heteroscedasticity()
for further details.
Some caution is needed when interpreting these plots. Although these plots are helpful to check model assumptions, they do not necessarily indicate socalled "lack of fit", e.g. missed nonlinear relationships or interactions. Thus, it is always recommended to also look at effect plots, including partial residuals.
This plot checks the assumption of equal variance (homoscedasticity). The desired pattern would be that dots spread equally above and below a straight, horizontal line and show no apparent deviation.
This plot is used to identify influential observations. If any points in this
plot fall outside of Cook’s distance (the dashed lines) then it is considered
an influential observation. See check_outliers()
for further details.
This plot checks for potential collinearity among predictors. In a nutshell,
multicollinearity means that once you know the effect of one predictor, the
value of knowing the other predictor is rather low. Multicollinearity might
arise when a third, unobserved variable has a causal effect on each of the
two predictors that are associated with the outcome. In such cases, the actual
relationship that matters would be the association between the unobserved
variable and the outcome. See check_collinearity()
for further details.
This plot is used to determine if the residuals of the regression model are
normally distributed. Usually, dots should fall along the line. If there is
some deviation (mostly at the tails), this indicates that the model doesn't
predict the outcome well for that range that shows larger deviations from
the line. For generalized linear models, a halfnormal QQ plot of the
absolute value of the standardized deviance residuals is shown, however, the
interpretation of the plot remains the same. See check_normality()
for
further details.
For count models, an overdispersion plot is shown. Overdispersion occurs
when the observed variance is higher than the variance of a theoretical model.
For Poisson models, variance increases with the mean and, therefore, variance
usually (roughly) equals the mean value. If the variance is much higher,
the data are "overdispersed". See check_overdispersion()
for further
details.
For models from binomial families, a binned residuals plot is shown.
Binned residual plots are achieved by cutting the the data into bins and then
plotting the average residual versus the average fitted value for each bin.
If the model were true, one would expect about 95% of the residuals to fall
inside the error bounds. See binned_residuals()
for further details.
Plots that check the normality of residuals (QQplot) 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.
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.
Other functions to check model assumptions and and assess model quality:
check_autocorrelation()
,
check_collinearity()
,
check_convergence()
,
check_heteroscedasticity()
,
check_homogeneity()
,
check_outliers()
,
check_overdispersion()
,
check_predictions()
,
check_singularity()
,
check_zeroinflation()
m < lm(mpg ~ wt + cyl + gear + disp, data = mtcars)
check_model(m)
data(sleepstudy, package = "lme4")
m < lme4::lmer(Reaction ~ Days + (Days  Subject), sleepstudy)
check_model(m, panel = FALSE)
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