broom (version 0.8.0)

tidy.aareg: Tidy a(n) aareg 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 aareg
tidy(x, ...)

Arguments

x

An aareg object returned from survival::aareg().

...

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.

Value

A tibble::tibble() with columns:

estimate

The estimated value of the regression term.

p.value

The two-sided p-value associated with the observed statistic.

robust.se

robust version of standard error estimate.

statistic

The value of a T-statistic to use in a hypothesis that the regression term is non-zero.

std.error

The standard error of the regression term.

term

The name of the regression term.

z

z score.

Details

robust.se is only present when x was created with dfbeta = TRUE.

See Also

tidy(), survival::aareg()

Other aareg tidiers: glance.aareg()

Other survival tidiers: augment.coxph(), augment.survreg(), glance.aareg(), glance.cch(), glance.coxph(), glance.pyears(), glance.survdiff(), glance.survexp(), glance.survfit(), glance.survreg(), tidy.cch(), tidy.coxph(), tidy.pyears(), tidy.survdiff(), tidy.survexp(), tidy.survfit(), tidy.survreg()

Examples

# NOT RUN {
# feel free to ignore the following line<U+2014>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
afit <- aareg(
  Surv(time, status) ~ age + sex + ph.ecog,
  data = lung,
  dfbeta = TRUE
)

# summarize model fit with tidiers
tidy(afit)

}

# }