tidy.rq: Tidy a(n) rq object

In dgrtwo/broom: Convert Statistical Objects into Tidy Tibbles

Tidy a(n) rq 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 class 'rq'
tidy(x, se.type = NULL, conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x	An rq object returned from quantreg::rq().
se.type	Character specifying the method to use to calculate standard errors. Passed to quantreg::summary.rq() se argument. Defaults to "rank" if the sample size is less than 1000, otherwise defaults to "nid".
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.
...	Additional arguments passed to quantreg::summary.rq().

Details

If se.type = "rank" confidence intervals are calculated by summary.rq and statistic and p.value values are not returned. When only a single predictor is included in the model, no confidence intervals are calculated and the confidence limits are set to NA.

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.

See Also

tidy(), quantreg::rq()

Other quantreg tidiers: augment.nlrq(), augment.rqs(), augment.rq(), glance.nlrq(), glance.rq(), tidy.nlrq(), tidy.rqs()

Examples

# load modeling library and data
library(quantreg)

data(stackloss)

# median (l1) regression fit for the stackloss data. 
mod1 <- rq(stack.loss ~ stack.x, .5)  

# weighted sample median
mod2 <- rq(rnorm(50) ~ 1, weights = runif(50))

# summarize model fit with tidiers
tidy(mod1)
glance(mod1)
augment(mod1)

tidy(mod2)
glance(mod2)
augment(mod2)

# varying tau to generate an rqs object
mod3 <- rq(stack.loss ~ stack.x, tau = c(.25, .5))

tidy(mod3)
augment(mod3)

# glance cannot handle rqs objects like `mod3`--use a purrr 
# `map`-based workflow instead

dgrtwo/broom documentation built on Feb. 14, 2023, 12:40 a.m.