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, ...)

Arguments

x

a equiv_change_mean object returned from equiv_change_mean()

...

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

Value

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

See also

Examples

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>