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 pyears
tidy(x, ...)A pyears object returned from survival::pyears().
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.
expected is only present in the output when if a ratetable
term is present.
If the data.frame = TRUE argument is supplied to pyears,
this is simply the contents of x$data.
Other pyears tidiers:
glance.pyears()
Other survival tidiers:
augment.coxph(),
augment.survreg(),
glance.aareg(),
glance.cch(),
glance.coxph(),
glance.pyears(),
glance.survdiff(),
glance.survexp(),
glance.survfit(),
glance.survreg(),
tidy.aareg(),
tidy.cch(),
tidy.coxph(),
tidy.survdiff(),
tidy.survexp(),
tidy.survfit(),
tidy.survreg()
A tibble::tibble() with columns:
Expected number of events.
Person-years of exposure.
number of subjects contributing time
observed number of events
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("survival", quietly = TRUE)) {
# load libraries for models and data
library(survival)
# generate and format data
temp.yr <- tcut(mgus$dxyr, 55:92, labels = as.character(55:91))
temp.age <- tcut(mgus$age, 34:101, labels = as.character(34:100))
ptime <- ifelse(is.na(mgus$pctime), mgus$futime, mgus$pctime)
pstat <- ifelse(is.na(mgus$pctime), 0, 1)
pfit <- pyears(Surv(ptime / 365.25, pstat) ~ temp.yr + temp.age + sex, mgus,
data.frame = TRUE
)
# summarize model fit with tidiers
tidy(pfit)
glance(pfit)
# if data.frame argument is not given, different information is present in
# output
pfit2 <- pyears(Surv(ptime / 365.25, pstat) ~ temp.yr + temp.age + sex, mgus)
tidy(pfit2)
glance(pfit2)
}
#> # A tibble: 1 × 3
#> total offtable nobs
#> <dbl> <dbl> <int>
#> 1 8.32 0.727 241