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.
Usage
# S3 method for class 'survexp'
tidy(x, ...)Arguments
- x
An
survexpobject returned fromsurvival::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
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
# load libraries for models and data
library(survival)
# fit model
sexpfit <- survexp(
futime ~ 1,
rmap = list(
sex = "male",
year = accept.dt,
age = (accept.dt - birth.dt)
),
method = "conditional",
data = jasa
)
# summarize model fit with tidiers
tidy(sexpfit)
#> # A tibble: 88 × 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
#> # ℹ 78 more rows
glance(sexpfit)
#> # A tibble: 1 × 3
#> n.max n.start timepoints
#> <int> <int> <int>
#> 1 102 102 88