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 survfit
tidy(x, ...)
An survfit
object returned from survival::survfit()
.
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.
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.survexp()
,
tidy.survreg()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
Number of censored events.
Number of events at time t.
Number of individuals at risk at time zero.
The standard error of the regression term.
Point in time.
estimate of survival or cumulative incidence rate when multistate
state if multistate survfit object input
strata if stratified survfit object input
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("survival", quietly = TRUE)) {
# load libraries for models and data
library(survival)
# fit model
cfit <- coxph(Surv(time, status) ~ age + sex, lung)
sfit <- survfit(cfit)
# summarize model fit with tidiers + visualization
tidy(sfit)
glance(sfit)
library(ggplot2)
ggplot(tidy(sfit), aes(time, estimate)) +
geom_line() +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)
# multi-state
fitCI <- survfit(Surv(stop, status * as.numeric(event), type = "mstate") ~ 1,
data = mgus1, subset = (start == 0)
)
td_multi <- tidy(fitCI)
td_multi
ggplot(td_multi, aes(time, estimate, group = state)) +
geom_line(aes(color = state)) +
geom_ribbon(aes(ymin = conf.low, ymax = conf.high), alpha = .25)
}