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


...  Additional arguments. Not used. Included only to match generic signature. 
A onerow 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 x 9 #> alpha n_sample modcv threshold_min_i… threshold_mean result_min_indiv #> <dbl> <int> <lgl> <dbl> <dbl> <chr> #> 1 0.01 5 FALSE 88.5 95.5 FAIL #> # … with 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>