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 cross 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 Arima
tidy(x, conf.int = FALSE, conf.level = 0.95, ...)

## Arguments

x |
An object of class `Arima` created by `stats::arima()` . |

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` . 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. |

## See also

## Value

A `tibble::tibble()`

with columns:

conf.highThe upper end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

conf.lowThe lower end of a confidence interval for the term under consideration. Included only if `conf.int = TRUE`.

estimateThe estimated value of the regression term.

std.errorThe standard error of the regression term.

termThe name of the regression term.

## Examples

#> # A tibble: 2 x 3
#> term estimate std.error
#> <fct> <dbl> <dbl>
#> 1 ar1 0.574 0.116
#> 2 intercept 2.41 0.147

#> # A tibble: 1 x 4
#> sigma logLik AIC BIC
#> <dbl> <dbl> <dbl> <dbl>
#> 1 0.444 -29.4 64.8 70.4