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.
Usage
# S3 method for survdiff
tidy(x, ...)Arguments
- x
An
survdiffobject returned fromsurvival::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 passconf.lvel = 0.9, all computation will proceed usingconf.level = 0.95. Two exceptions here are:
See also
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()
Value
A tibble::tibble() with columns:
- exp
Weighted expected number of events in each group.
- N
Number of subjects in each group.
- obs
weighted observed number of events in each group.
Examples
# 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)
#> # A tibble: 8 × 4
#> pat.karno N obs exp
#> <chr> <dbl> <dbl> <dbl>
#> 1 30 2 1 0.692
#> 2 40 2 1 1.10
#> 3 50 4 4 1.17
#> 4 60 30 27 16.3
#> 5 70 41 31 26.4
#> 6 80 50 38 41.9
#> 7 90 60 38 47.2
#> 8 100 35 21 26.2
glance(s)
#> # A tibble: 1 × 3
#> statistic df p.value
#> <dbl> <dbl> <dbl>
#> 1 21.4 7 0.00326