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 ivreg tidy(x, conf.int = FALSE, conf.level = 0.95, instruments = FALSE, ...)
x | An |
---|---|
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 |
instruments | Logical indicating whether to return
coefficients from the second-stage or diagnostics tests for
each endogenous regressor (F-statistics). Defaults to |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
This tidier currently only supports ivreg
-classed objects
outputted by the AER
package. The ivreg
package also outputs
objects of class ivreg
, and will be supported in a later release.
Other ivreg tidiers:
augment.ivreg()
,
glance.ivreg()
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.
p-value for Sargan test of overidentifying restrictions.
p-value for weak instruments test.
p-value for Wu-Hausman weak instruments test for endogeneity.
The value of a T-statistic to use in a hypothesis that the regression term is non-zero.
Statistic for Sargan test of overidentifying restrictions.
Statistic for Wu-Hausman test.
Statistic for Wu-Hausman weak instruments test for endogeneity.
The standard error of the regression term.
The name of the regression term.
if (requireNamespace("AER", quietly = TRUE)) { library(AER) data("CigarettesSW", package = "AER") ivr <- ivreg( log(packs) ~ income | population, data = CigarettesSW, subset = year == "1995" ) summary(ivr) tidy(ivr) tidy(ivr, conf.int = TRUE) tidy(ivr, conf.int = TRUE, instruments = TRUE) augment(ivr) augment(ivr, data = CigarettesSW) augment(ivr, newdata = CigarettesSW) glance(ivr) } #> # A tibble: 1 × 8 #> r.squared adj.r.squared sigma statistic p.value df df.residual nobs #> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <int> #> 1 0.131 0.112 0.229 5.98 0.0184 2 46 48