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, ...)
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:
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.
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 } #> # 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>