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 varest
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)
```

## Arguments

- x
A

`varest`

object produced by a call to`vars::VAR()`

.- conf.int
Logical indicating whether or not to include a confidence interval in the tidied output. Defaults to

`FALSE`

.- conf.level
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`

. Two exceptions here are:

## 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.

## 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)
#> # 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
```