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

In Greg: Regression Helper Functions

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 class '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)
)

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).

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.

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)

Greg documentation built on Nov. 16, 2022, 5:06 p.m.