Tidy a(n) survexp object

Tidy summarizes information about the components of a model. A model component might be a single term in a regression, a single hypothesis, a cluster, or a class. Exactly what tidy considers to be a model component varies across models but is usually self-evident. If a model has several distinct types of components, you will need to specify which components to return.

# S3 method for survexp
tidy(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

tidy(), survival::survexp()

Other survexp tidiers: glance.survexp()

Other survival tidiers: augment.coxph(), augment.survreg(), glance.aareg(), glance.cch(), glance.coxph(), glance.pyears(), glance.survdiff(), glance.survexp(), glance.survfit(), glance.survreg(), tidy.aareg(), tidy.cch(), tidy.coxph(), tidy.pyears(), tidy.survdiff(), tidy.survfit(), tidy.survreg()

Value

A tibble::tibble() with columns:

n.risk

Number of individuals at risk at time zero.

time

Point in time.

estimate

Estimate survival

Examples

library(survival)
sexpfit <- survexp(
  futime ~ 1,
  rmap = list(
    sex = "male",
    year = accept.dt,
    age = (accept.dt - birth.dt)
  ),
  method = "conditional",
  data = jasa
)

tidy(sexpfit)
#> # A tibble: 88 x 3
#>     time estimate n.risk
#>    <dbl>    <dbl>  <int>
#>  1     0     1       102
#>  2     1     1.00    102
#>  3     2     1.00     99
#>  4     4     1.00     96
#>  5     5     1.00     94
#>  6     7     1.00     92
#>  7     8     1.00     91
#>  8    10     1.00     90
#>  9    11     1.00     89
#> 10    15     1.00     88
#> # … with 78 more rows
glance(sexpfit)
#> # A tibble: 1 x 3
#>   n.max n.start timepoints
#>   <int>   <int>      <int>
#> 1   102     102         88