Glance accepts an object of type basis and returns a
tibble::tibble() with
one row of summaries for each basis value.
Glance does not do any calculations: it just gathers the results in a tibble.
# S3 method for class 'basis'
glance(x, include_diagnostics = FALSE, ...)A tibble::tibble() with the following
columns:
p the the content of the tolerance bound. Normally 0.90 or 0.99
conf the confidence level. Normally 0.95
distribution a string representing the distribution assumed
when calculating the basis value
modcv a logical value indicating whether the modified
CV approach was used. Only applicable to pooling methods.
n the sample size
r the number of groups used in the calculation. This will
be NA for single-point basis values
basis the basis value
For the pooled basis methods (basis_pooled_cv and
basis_pooled_sd), the tibble::tibble()
returned by glance will have one row for each group included in
the pooling. For all other basis methods, the resulting tibble
will have a single row.
If include_diagnostics=TRUE, there will be additional columns
corresponding with the diagnostic tests performed. These column(s) will
be of type character and will contain a "P" if the diagnostic test
passed, a "F" if the diagnostic test failed, an "O" if the diagnostic
test was overridden or NA if the test was not run (typically
because an optional argument was not passed to the function that
computed the basis value).
set.seed(10)
x <- rnorm(20, 100, 5)
b <- basis_normal(x = x)
#> `outliers_within_batch` not run because parameter `batch` not specified
#> `between_batch_variability` not run because parameter `batch` not specified
glance(b)
#> # A tibble: 1 × 7
#> p conf distribution modcv n r basis
#> <dbl> <dbl> <chr> <lgl> <int> <lgl> <dbl>
#> 1 0.9 0.95 Normal FALSE 20 NA 92.0
## # A tibble: 1 x 7
## p conf distribution modcv n r basis
## <dbl> <dbl> <chr> <lgl> <int> <lgl> <dbl>
## 1 0.9 0.95 Normal FALSE 20 NA 92.0
glance(b, include_diagnostics = TRUE)
#> # A tibble: 1 × 11
#> p conf distribution modcv n r basis outliers_within_batch
#> <dbl> <dbl> <chr> <lgl> <int> <lgl> <dbl> <chr>
#> 1 0.9 0.95 Normal FALSE 20 NA 92.0 NA
#> # ℹ 3 more variables: between_batch_variability <chr>, outliers <chr>,
#> # anderson_darling_normal <chr>
## # A tibble: 1 x 11
## p conf distribution modcv n r basis outliers_within…
## <dbl> <dbl> <chr> <lgl> <int> <lgl> <dbl> <chr>
## 1 0.9 0.95 Normal FALSE 20 NA 92.0 NA
## # … with 3 more variables: between_batch_variability <chr>,
## # outliers <chr>, anderson_darling_normal <chr>