Glance accepts an object of type `equiv_change_mean`

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_change_mean glance(x, ...)

x | a |
---|---|

... | 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`

.`mean_sample`

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

passed to this function or the mean of the vector`data-sample`

.`sd_sample`

the standard deviation of the observations in the sample for which equivalency is being checked. This is either the value`mean_sample`

passed to this function or the standard deviation of the vector`data-sample`

.`n_qual`

the number of observations in the qualification data to which the sample is being compared for equivalency. This is either the value`n_qual`

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

.`mean_qual`

the mean of the qualification data to which the sample is being compared for equivalency. This is either the value`mean_qual`

passed to this function or the mean of the vector`data_qual`

.`sd_qual`

the standard deviation of the qualification data to which the sample is being compared for equivalency. This is either the value`mean_qual`

passed to this function or the standard deviation of the vector`data_qual`

.`modcv`

logical value indicating whether the equivalency calculations were performed using the modified CV approach`sp`

the value of the pooled standard deviation. If`modecv = TRUE`

, this pooled standard deviation includes the modification to the qualification CV.`t0`

the test statistic`t_req`

the t-value for \(\alpha / 2\) and \(df = n1 + n2 -2\)`threshold_min`

the minimum value of the sample mean that would result in a pass`threshold_max`

the maximum value of the sample mean that would result in a pass`result`

a character vector of either "PASS" or "FAIL" indicating the result of the test for change in mean

x0 <- rnorm(30, 100, 4) x1 <- rnorm(5, 91, 7) eq <- equiv_change_mean(data_qual = x0, data_sample = x1, alpha = 0.01) glance(eq)#> # A tibble: 1 x 14 #> alpha n_sample mean_sample sd_sample n_qual mean_qual sd_qual modcv sp #> <dbl> <int> <dbl> <dbl> <int> <dbl> <dbl> <lgl> <dbl> #> 1 0.01 5 95.6 7.69 30 97.9 3.48 FALSE 4.22 #> # … with 5 more variables: t0 <dbl>, t_req <dbl>, threshold_min <dbl>, #> # threshold_max <dbl>, result <chr>## # A tibble: 1 x 14 ## alpha n_sample mean_sample sd_sample n_qual mean_qual sd_qual modcv ## <dbl> <int> <dbl> <dbl> <int> <dbl> <dbl> <lgl> ## 1 0.01 5 85.8 9.93 30 100. 3.90 FALSE ## # ... with 6 more variables: sp <dbl>, t0 <dbl>, t_req <dbl>, ## # threshold_min <dbl>, threshold_max <dbl>, result <chr>