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.

# S3 method for aov
glance(x, ...)

## Arguments

x |
An `aov` object, such as those created by `stats::aov()` . |

... |
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 pass `conf.lvel = 0.9` , all computation will
proceed using `conf.level = 0.95` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## Note

Note that `tidy.aov()`

now contains the numerator and denominator degrees of
freedom, which were included in the output of `glance.aov()`

in some
previous versions of the package.

## See also

## Value

A `tibble::tibble()`

with exactly one row and columns:

AICAkaike's Information Criterion for the model.

BICBayesian Information Criterion for the model.

devianceDeviance of the model.

logLikThe log-likelihood of the model. [stats::logLik()] may be a useful reference.

nobsNumber of observations used.

## Examples

#> # A tibble: 4 x 6
#> term df sumsq meansq statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 wt 1 848. 848. 121. 1.08e-11
#> 2 qsec 1 82.9 82.9 11.9 1.82e- 3
#> 3 disp 1 0.00102 0.00102 0.000147 9.90e- 1
#> 4 Residuals 28 195. 6.98 NA NA