tidy.rqs {broom}

Tidy a(n) rqs 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 'rqs'
tidy(x, se.type = "rank", conf.int = FALSE, conf.level = 0.95, ...)

Arguments

x

An rqs 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".

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.rqs()

Details

If se.type = "rank" confidence intervals are calculated by summary.rq. 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.

quantile

Linear conditional quantile.

See Also

tidy(), quantreg::rq()

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

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