broom (version 1.0.4)

tidy.pyears: Tidy a(n) pyears object

Description

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 pyears
tidy(x, ...)

Value

A tibble::tibble() with columns:

expected

Expected number of events.

pyears

Person-years of exposure.

n

number of subjects contributing time

event

observed number of events

Arguments

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. Two exceptions here are:

• tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

• augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

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.

See Also

tidy(), survival::pyears()

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()

Examples

# 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)