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 cross 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 = TRUE, 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()
A tibble::tibble() with one row for each term in the
regression. The tibble has columns:
The name of the regression term.
The estimated value of the regression term.
The standard error of the regression term.
The value of a statistic, almost always a T-statistic, to use in a hypothesis that the regression term is non-zero.
The two-sided p-value associated with the observed statistic.
The low end of a confidence interval for the regression
term. Included only if conf.int = TRUE.
The high end of a confidence interval for the regression
term. Included only if conf.int = TRUE.
An additional quantile column indicating with quantile the coefficient corresponds to.
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()