Tidy a(n) coeftest object
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 class 'coeftest' tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
x |
A |
conf.int |
Logical indicating whether or not to include a confidence
interval in the tidied output. Defaults to |
conf.level |
The confidence level to use for the confidence interval
if |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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. |
if (requireNamespace("lmtest", quietly = TRUE)) {
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)
tidy(coeftest(m, vcov = vcovHC)) # "HC3" (default) robust SEs
tidy(coeftest(m, vcov = vcovHC, type = "HC2")) # "HC2" robust SEs
tidy(coeftest(m, vcov = NeweyWest)) # N-W HAC robust SEs
# 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)) # More columns
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