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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, ...)
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.level = 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.
A tibble::tibble()
with columns:
Number of individuals at risk at time zero.
Point in time.
Estimate survival
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()
# NOT RUN {
library(survival)
sexpfit <- survexp(
futime ~ 1,
rmap = list(
sex = "male",
year = accept.dt,
age = (accept.dt - birth.dt)
),
method = "conditional",
data = jasa
)
tidy(sexpfit)
glance(sexpfit)
# }
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