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

stats::arima()

Other Arima tidiers: glance.Arima

Value

A tibble::tibble() with columns:

conf.high

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

conf.low

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

estimate

The estimated value of the regression term.

std.error

The standard error of the regression term.

term

The name of the regression term.

Examples

fit <- arima(lh, order = c(1, 0, 0)) tidy(fit)
#> # A tibble: 2 x 3 #> term estimate std.error #> <fct> <dbl> <dbl> #> 1 ar1 0.574 0.116 #> 2 intercept 2.41 0.147
glance(fit)
#> # A tibble: 1 x 4 #> sigma logLik AIC BIC #> <dbl> <dbl> <dbl> <dbl> #> 1 0.444 -29.4 64.8 70.4