Glance accepts a model object and returns a `tibble::tibble()`

with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.

Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.

Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as `NA`

.

Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an `NA`

of the appropriate type.

## Usage

```
# S3 method for binDesign
glance(x, ...)
```

## Arguments

- x
A binGroup::binDesign object.

- ...
Additional arguments. Not used. Needed to match generic signature only.

**Cautionary note:**Misspelled arguments will be absorbed in`...`

, where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass`conf.lvel = 0.9`

, all computation will proceed using`conf.level = 0.95`

. Two exceptions here are:

## See also

`glance()`

, `binGroup::binDesign()`

Other bingroup tidiers:
`tidy.binDesign()`

,
`tidy.binWidth()`

## Value

A `tibble::tibble()`

with exactly one row and columns:

- power
Power achieved by the analysis.

- n
Sample size used to achieve this power.

- power.reached
Whether the desired power was reached.

- maxit
Number of iterations performed.

## Examples

```
# load libraries for models and data
library(binGroup)
des <- binDesign(
nmax = 300, delta = 0.06,
p.hyp = 0.1, power = .8
)
glance(des)
#> # A tibble: 1 × 4
#> power n power.reached maxit
#> <dbl> <int> <lgl> <int>
#> 1 0.805 240 TRUE 238
tidy(des)
#> # A tibble: 238 × 2
#> n power
#> <int> <dbl>
#> 1 3 0.0000640
#> 2 4 0.000248
#> 3 5 0.000602
#> 4 6 0.00117
#> 5 7 0.0000813
#> 6 8 0.000157
#> 7 9 0.000274
#> 8 10 0.000443
#> 9 11 0.000673
#> 10 12 0.0000640
#> # ℹ 228 more rows
library(ggplot2)
ggplot(tidy(des), aes(n, power)) +
geom_line()
```