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 varest
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
A varest
object produced by a call to vars::VAR()
.
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.
The tibble has one row for each term in the regression. The
component
column indicates whether a particular
term was used to model either the "mean"
or "precision"
. Here the
precision is the inverse of the variance, often referred to as phi
.
At least one term will have been used to model the precision phi
.
The vars
package does not include a confint
method and does not report
confidence intervals for varest
objects. Setting the tidy
argument
conf.int = TRUE
will return a warning.
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.
Whether a particular term was used to model the mean or the precision in the regression. See details.
# feel free to ignore the following line—it allows {broom} to supply
# examples without requiring the model-supplying package to be installed.
if (requireNamespace("vars", quietly = TRUE)) {
# load libraries for models and data
library(vars)
# load data
data("Canada", package = "vars")
# fit models
mod <- VAR(Canada, p = 1, type = "both")
# summarize model fit with tidiers
tidy(mod)
glance(mod)
}
#> # A tibble: 1 × 4
#> lag.order logLik nobs n
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 -208. 83 84