tidy.varest {broom} | R Documentation |
Tidy a(n) varest object
Description
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.
Usage
## S3 method for class 'varest'
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
Arguments
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
|
Details
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.
Value
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. |
component |
Whether a particular term was used to model the mean or the precision in the regression. See details. |
See Also
Examples
# 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)