Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA
.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
Usage
# S3 method for class 'anova'
glance(x, ...)
Arguments
- x
An
anova
object, such as those created bystats::anova()
,car::Anova()
,car::leveneTest()
, orcar::linearHypothesis()
.- ...
Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in
...
, where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you passconf.lvel = 0.9
, all computation will proceed usingconf.level = 0.95
. Two exceptions here are:
Note
Note that the output of glance.anova()
will vary depending on the initializing
anova call. In some cases, it will just return an empty data frame. In other
cases, glance.anova()
may return columns that are also common to
tidy.anova()
. This is partly to preserve backwards compatibility with early
versions of broom
, but also because the underlying anova model yields
components that could reasonably be interpreted as goodness-of-fit summaries
too.
See also
Other anova tidiers:
glance.aov()
,
tidy.TukeyHSD()
,
tidy.anova()
,
tidy.aov()
,
tidy.aovlist()
,
tidy.manova()
Value
A tibble::tibble()
with exactly one row and columns:
- deviance
Deviance of the model.
- df.residual
Residual degrees of freedom.