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.
# S3 method for rqs
tidy(x, se.type = "rank", conf.int = FALSE, conf.level = 0.95, ...)
An rqs
object returned from quantreg::rq()
.
Character specifying the method to use to calculate
standard errors. Passed to quantreg::summary.rq()
se
argument.
Defaults to "rank"
.
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to FALSE
.
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()
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.
Other quantreg tidiers:
augment.nlrq()
,
augment.rqs()
,
augment.rq()
,
glance.nlrq()
,
glance.rq()
,
tidy.nlrq()
,
tidy.rq()
A tibble::tibble()
with columns:
Upper bound on the confidence interval for the estimate.
Lower bound on the confidence interval for the estimate.
The estimated value of the regression term.
The two-sided p-value associated with the observed statistic.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
The standard error of the regression term.
The name of the regression term.
Linear conditional quantile.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("quantreg", quietly = TRUE)) {
# 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
}
#> # A tibble: 42 × 5
#> stack.loss stack.x[,"Air.Flow"] [,"Water.Temp"] .tau .resid .fitted
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 42 80 27 0.25 1.10e+ 1 31.0
#> 2 42 80 27 0.5 5.06e+ 0 36.9
#> 3 37 80 27 0.25 6.00e+ 0 31.0
#> 4 37 80 27 0.5 -1.42e-14 37
#> 5 37 75 25 0.25 1.05e+ 1 26.5
#> 6 37 75 25 0.5 5.43e+ 0 31.6
#> 7 28 62 24 0.25 9.00e+ 0 19
#> 8 28 62 24 0.5 7.63e+ 0 20.4
#> 9 18 62 22 0.25 1.00e+ 0 17.0
#> 10 18 62 22 0.5 -1.22e+ 0 19.2
#> # … with 32 more rows, and 1 more variable: stack.x[3] <dbl>