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 cross 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

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 1.000 102 #> 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
glance(sexpfit)
#> # A tibble: 1 x 3 #> n.max n.start timepoints #> <int> <int> <int> #> 1 102 102 88