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 '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.
For glance(), additional arguments passed to summary().
Otherwise ignored.
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.
# 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)
#> # A tibble: 24 × 6
#> group term estimate std.error statistic p.value
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 e e.l1 1.24 0.0863 14.4 1.82e-23
#> 2 e prod.l1 0.195 0.0361 5.39 7.49e- 7
#> 3 e rw.l1 -0.0678 0.0283 -2.40 1.90e- 2
#> 4 e U.l1 0.623 0.169 3.68 4.30e- 4
#> 5 e const -279. 75.2 -3.71 3.92e- 4
#> 6 e trend -0.0407 0.0197 -2.06 4.24e- 2
#> 7 prod e.l1 0.0129 0.126 0.103 9.19e- 1
#> 8 prod prod.l1 0.963 0.0527 18.3 9.43e-30
#> 9 prod rw.l1 -0.0391 0.0412 -0.948 3.46e- 1
#> 10 prod U.l1 0.211 0.247 0.855 3.95e- 1
#> # ℹ 14 more rows
glance(mod)
#> # A tibble: 1 × 4
#> lag.order logLik nobs n
#> <dbl> <dbl> <dbl> <dbl>
#> 1 1 -208. 83 84