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 survdiff
tidy(x, ...)An survdiff object returned from survival::survdiff().
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 survdiff tidiers:
glance.survdiff()
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.survexp(),
tidy.survfit(),
tidy.survreg()
A tibble::tibble() with columns:
Weighted expected number of events in each group.
Number of subjects in each group.
weighted observed number of events in each group.
# 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
s <- survdiff(
Surv(time, status) ~ pat.karno + strata(inst),
data = lung
)
# summarize model fit with tidiers
tidy(s)
glance(s)
}
#> # A tibble: 1 × 3
#> statistic df p.value
#> <dbl> <dbl> <dbl>
#> 1 21.4 7 0.00326