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 modelling
function. This includes the name of the modelling function or any
arguments passed to the modelling 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`

.

# S3 method for survexp
glance(x, ...)

## Arguments

x |
An `survexp` object returned from `survival::survexp()` . |

... |
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` . Additionally, if you pass
`newdata = my_tibble` to an `augment()` method that does not
accept a `newdata` argument, it will use the default value for
the `data` argument. |

## See also

`glance()`

, `survival::survexp()`

Other survexp tidiers: `tidy.survexp`

Other survival tidiers: `augment.coxph`

,
`augment.survreg`

,
`glance.aareg`

, `glance.cch`

,
`glance.coxph`

, `glance.pyears`

,
`glance.survdiff`

,
`glance.survfit`

,
`glance.survreg`

, `tidy.aareg`

,
`tidy.cch`

, `tidy.coxph`

,
`tidy.pyears`

, `tidy.survdiff`

,
`tidy.survexp`

, `tidy.survfit`

,
`tidy.survreg`

## Value

A `tibble::tibble()`

with exactly one row and columns:

n.maxMaximum number of subjects at risk.

n.startInitial number of subjects at risk.

timepointsnumber of timepoints

## Examples

#> # A tibble: 88 x 3
#> time estimate n.risk
#> <dbl> <dbl> <int>
#> 1 0 1 102
#> 2 1 1.000 102
#> 3 2 1.000 99
#> 4 4 1.000 96
#> 5 5 1.000 94
#> 6 7 1.000 92
#> 7 8 1.000 91
#> 8 10 1.000 90
#> 9 11 1.000 89
#> 10 15 1.000 88
#> # ... with 78 more rows

#> # A tibble: 1 x 3
#> n.max n.start timepoints
#> <int> <int> <int>
#> 1 102 102 88