tidy.svyolr: Tidy a(n) svyolr 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 svyolr
tidy(x, conf.int = FALSE, conf.level = 0.95, exponentiate = 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 svyolr object returned from survey::svyolr().

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.

Details

The tidy.svyolr() tidier is a light wrapper around tidy.polr(). However, the implementation for p-value calculation in tidy.polr() is both computationally intensive and specific to that model, so the p.values argument to tidy.svyolr() is currently ignored, and will raise a warning when passed.

Examples

Run this code

if (FALSE) { # rlang::is_installed("survey") && rlang::is_installed("MASS")
library(broom)
library(survey)

data(api)
dclus1 <- svydesign(id = ~dnum, weights = ~pw, data = apiclus1, fpc = ~fpc)
dclus1 <- update(dclus1, mealcat = cut(meals, c(0, 25, 50, 75, 100)))

m <- svyolr(mealcat ~ avg.ed + mobility + stype, design = dclus1)

m

tidy(m, conf.int = TRUE)
}