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 coeftest
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
A coeftest
object returned from lmtest::coeftest()
.
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. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
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.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("lmtest", quietly = TRUE)) {
# load libraries for models and data
library(lmtest)
m <- lm(dist ~ speed, data = cars)
coeftest(m)
tidy(coeftest(m))
tidy(coeftest(m, conf.int = TRUE))
# a very common workflow is to combine lmtest::coeftest with alternate
# variance-covariance matrices via the sandwich package. The lmtest
# tidiers support this workflow too, enabling you to adjust the standard
# errors of your tidied models on the fly.
library(sandwich)
# "HC3" (default) robust SEs
tidy(coeftest(m, vcov = vcovHC))
# "HC2" robust SEs
tidy(coeftest(m, vcov = vcovHC, type = "HC2"))
# N-W HAC robust SEs
tidy(coeftest(m, vcov = NeweyWest))
# the columns of the returned tibble for glance.coeftest() will vary
# depending on whether the coeftest object retains the underlying model.
# Users can control this with the "save = TRUE" argument of coeftest().
glance(coeftest(m))
glance(coeftest(m, save = TRUE))
}
#> # A tibble: 1 × 12
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0.651 0.644 15.4 89.6 1.49e-12 1 -207. 419. 425.
#> # … with 3 more variables: deviance <dbl>, df.residual <int>, nobs <int>