| glance.survfit {broom} | R Documentation |
Glance at a(n) survfit object
Description
Glance accepts a model object and returns a tibble::tibble()
with exactly one row of model summaries. The summaries are typically
goodness of fit measures, p-values for hypothesis tests on residuals,
or model convergence information.
Glance never returns information from the original call to the modeling function. This includes the name of the modeling function or any arguments passed to the modeling function.
Glance does not calculate summary measures. Rather, it farms out these
computations to appropriate methods and gathers the results together.
Sometimes a goodness of fit measure will be undefined. In these cases
the measure will be reported as NA.
Glance returns the same number of columns regardless of whether the
model matrix is rank-deficient or not. If so, entries in columns
that no longer have a well-defined value are filled in with an NA
of the appropriate type.
Usage
## S3 method for class 'survfit'
glance(x, ...)
Arguments
x |
An |
... |
Additional arguments passed to |
Value
A tibble::tibble() with exactly one row and columns:
events |
Number of events. |
n.max |
Maximum number of subjects at risk. |
n.start |
Initial number of subjects at risk. |
nobs |
Number of observations used. |
records |
Number of observations |
rmean |
Restricted mean (see [survival::print.survfit()]). |
rmean.std.error |
Restricted mean standard error. |
conf.low |
lower end of confidence interval on median |
conf.high |
upper end of confidence interval on median |
median |
median survival |
See Also
Other cch tidiers:
glance.cch(),
tidy.cch()
Other survival tidiers:
augment.coxph(),
augment.survreg(),
glance.aareg(),
glance.cch(),
glance.coxph(),
glance.pyears(),
glance.survdiff(),
glance.survexp(),
glance.survreg(),
tidy.aareg(),
tidy.cch(),
tidy.coxph(),
tidy.pyears(),
tidy.survdiff(),
tidy.survexp(),
tidy.survfit(),
tidy.survreg()
Examples
# 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)