Glance accepts an object of type `equiv_mean_extremum`

and returns a
`tibble::tibble()`

with
one row of summaries.

Glance does not do any calculations: it just gathers the results in a tibble.

```
# S3 method for equiv_mean_extremum
glance(x, ...)
```

- x
an equiv_mean_extremum object returned from

`equiv_mean_extremum()`

- ...
Additional arguments. Not used. Included only to match generic signature.

A one-row `tibble::tibble()`

with the following
columns:

`alpha`

the value of alpha passed to this function`n_sample`

the number of observations in the sample for which equivalency is being checked. This is either the value`n_sample`

passed to this function or the length of the vector`data_sample`

.`modcv`

logical value indicating whether the acceptance thresholds are calculated using the modified CV approach`threshold_min_indiv`

The calculated threshold value for minimum individual`threshold_mean`

The calculated threshold value for mean`result_min_indiv`

a character vector of either "PASS" or "FAIL" indicating whether the data from`data_sample`

passes the test for minimum individual. If`data_sample`

was not supplied, this value will be`NULL`

`result_mean`

a character vector of either "PASS" or "FAIL" indicating whether the data from`data_sample`

passes the test for mean. If`data_sample`

was not supplied, this value will be`NULL`

`min_sample`

The minimum value from the vector`data_sample`

. if`data_sample`

was not supplied, this will have a value of`NULL`

`mean_sample`

The mean value from the vector`data_sample`

. If`data_sample`

was not supplied, this will have a value of`NULL`

```
x0 <- rnorm(30, 100, 4)
x1 <- rnorm(5, 91, 7)
eq <- equiv_mean_extremum(data_qual = x0, data_sample = x1, alpha = 0.01)
glance(eq)
#> # A tibble: 1 × 9
#> alpha n_sample modcv threshold_min_indiv threshold_mean result_min_indiv
#> <dbl> <int> <lgl> <dbl> <dbl> <chr>
#> 1 0.01 5 FALSE 88.5 95.5 FAIL
#> # ℹ 3 more variables: result_mean <chr>, min_sample <dbl>, mean_sample <dbl>
## # A tibble: 1 x 9
## alpha n_sample modcv threshold_min_indiv threshold_mean
## <dbl> <int> <lgl> <dbl> <dbl>
## 1 0.01 5 FALSE 86.2 94.9
## # ... with 4 more variables: result_min_indiv <chr>, result_mean <chr>,
## # min_sample <dbl>, mean_sample <dbl>
```