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 class 'equiv_mean_extremum'
glance(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>