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.level = 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.

See also


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.


library(vars) data("Canada", package = "vars") mod <- VAR(Canada, p = 1, type = "both") tidy(mod)
#> # A tibble: 24 x 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 1.24 0.0863 14.4 1.82e-23 #> 8 prod prod.l1 0.195 0.0361 5.39 7.49e- 7 #> 9 prod rw.l1 -0.0678 0.0283 -2.40 1.90e- 2 #> 10 prod U.l1 0.623 0.169 3.68 4.30e- 4 #> # … with 14 more rows
#> # A tibble: 1 x 4 #> lag.order logLik nobs n #> <dbl> <dbl> <dbl> <dbl> #> 1 1 -208. 83 84