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, ...)
x | An |
---|---|
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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.
library(survival) s <- survdiff( Surv(time, status) ~ pat.karno + strata(inst), data = lung ) tidy(s)#> # A tibble: 8 x 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#> # A tibble: 1 x 3 #> statistic df p.value #> <dbl> <dbl> <dbl> #> 1 21.4 7 0.00326