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, ...)a equiv_change_mean object returned from
equiv_change_mean()
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 × 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
#> # ℹ 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>