tidy.survfit {broom} | R Documentation |
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 class 'survfit' tidy(x, ...)
x |
An |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble with one row for each time point and columns:
time |
timepoint |
n.risk |
number of subjects at risk at time t0 |
n.event |
number of events at time t |
n.censor |
number of censored events |
estimate |
estimate of survival or cumulative incidence rate when multistate |
std.error |
standard error of estimate |
conf.high |
upper end of confidence interval |
conf.low |
lower end of confidence interval |
state |
state if multistate survfit object inputted |
strata |
strata if stratified survfit object inputted |
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()
library(survival) cfit <- coxph(Surv(time, status) ~ age + sex, lung) sfit <- survfit(cfit) 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)