tidy.rq: 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 rq
tidy(x, se.type = NULL, conf.int = FALSE, conf.level = 0.95, ...)

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: 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.

Examples

Run this code


# 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

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