Tidy a(n) Arima object

Source: R/stats-arima-tidiers.R

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.

Value

A tibble::tibble with one row for each coefficient and columns:

term

The term in the nonlinear model being estimated and tested

estimate

The estimated coefficient

std.error

The standard error from the linear model

If conf.int = TRUE, also returns
conf.low

low end of confidence interval

conf.high

high end of confidence interval

See also

stats::arima()

Other Arima tidiers: glance.Arima

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