tidy.polr: Tidy a(n) polr 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 polr
tidy(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  exponentiate = FALSE,
  p.values = FALSE,
  ...
)

Value

A tibble::tibble() with columns:

conf.high: Upper bound on the confidence interval for the estimate.
conf.low: Lower bound on the confidence interval for the estimate.
estimate: The estimated value of the regression term.
p.value: The two-sided p-value associated with the observed statistic.
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.

Arguments

x

A polr object returned from MASS::polr().

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.

p.values

Logical. Should p-values be returned, based on chi-squared tests from MASS::dropterm(). 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.

Details

In broom 0.7.0 the coefficient_type column was renamed to coef.type, and the contents were changed as well. Now the contents are coefficient and scale, rather than coefficient and zeta.

Calculating p.values with the dropterm() function is the approach suggested by the MASS package author. This approach is computationally intensive so that p.values are only returned if requested explicitly. Additionally, it only works for models containing no variables with more than two categories. If this condition is not met, a message is shown and NA is returned instead of p-values.

Examples

Run this code


# load libraries for models and data
library(MASS)

# fit model
fit <- polr(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)

# summarize model fit with tidiers
tidy(fit, exponentiate = TRUE, conf.int = TRUE)

glance(fit)
augment(fit, type.predict = "class")

fit2 <- polr(factor(gear) ~ am + mpg + qsec, data = mtcars)

tidy(fit, p.values = TRUE)