| tidy.coeftest | R Documentation | 
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. | 
tidy(), lmtest::coeftest()
# 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))
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