Greg (version 2.0.2)

tidy.rms: Tidy a(n) rms model object

Description

Tidy summarizes information about the components of a model. A model component might be a single term in a regressions. 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 rms
tidy(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  ...,
  .add_print_p_and_stat_values = getOption("Greg.tidy_add_p_and_stat_values", default =
    FALSE)
)

Value

A tibble::tibble() with columns: - `term` The name of the regression term. - `factor` The factor if the term is a character/factor term. - `column_term` The full name as in the original input data - `estimate` The estimated value of the regression term. - `conf.high` Upper bound on the confidence interval for the estimate.c - `conf.low` Lower bound on the confidence interval for the estimate. - `p.value` The two-sided p-value associated with the observed statistic. - `statistic` The value of a statistic to use in a hypothesis that the regression term is non-zero. - `std.error` The standard error of the regression term.

Arguments

x

An rms model, e.g. [`rms::cph()`], [`rms::lrm()`]

conf.int

Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to FALSE.

conf.level

The confidence level to use for the confidence interval if conf.int = TRUE. Must be strictly greater than 0 and less than 1. Defaults to 0.95, which corresponds to a 95 percent confidence interval.

exponentiate

Logical indicating whether or not to exponentiate the the coefficient estimates. This is typical for logistic and multinomial regressions, but a bad idea if there is no log or logit link. Defaults to FALSE.

...

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.

.add_print_p_and_stat_values

For estimating print values there is a workaround that relies on capturing output from the `print(x)` and is not considered safe.

Details

This is a quick fix for addressing the lack of `rms`-compatibility with the `broom` package, see [broom issue 30](https://github.com/tidymodels/broom/issues/30).

Examples

library(rms)
library(broom)
library(tidyverse)

set.seed(10)
cov <- tibble(x1 = runif(200)) |> 
  mutate(x_bool_fact = if_else(x1 > 0.5,
                               "Yes",
                               sample(c("Yes", "No"), size = n(), replace = TRUE)),
         x_multi_fact = sample(c("Strange", "Factor", "Names"), size = n(), replace = TRUE),
         ftime = rexp(n()),
         fstatus = sample(0:1, size = n(), replace = TRUE),
         x_good_predictor = fstatus * runif(n()))

ddist <- datadist(cov)
options(datadist = "ddist")

cph_fit <- cph(Surv(ftime, fstatus) ~ x1 + x_bool_fact + 
                 x_multi_fact + x_good_predictor, data = cov)
tidy(cph_fit)