Tidy a(n) acf object

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 acf
tidy(x, ...)

Arguments

x

An acf object created by stats::acf(), stats::pacf() or stats::ccf().

...

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 columns:

lag

lag values

acf

calculated correlation

See also

tidy(), stats::acf(), stats::pacf(), stats::ccf()

Other time series tidiers: tidy.spec, tidy.ts, tidy.zoo

Examples

tidy(acf(lh, plot = FALSE))
#> # A tibble: 17 x 2
#>      lag      acf
#>    <dbl>    <dbl>
#>  1     0  1.00   
#>  2     1  0.576  
#>  3     2  0.182  
#>  4     3 -0.145  
#>  5     4 -0.175  
#>  6     5 -0.150  
#>  7     6 -0.0210 
#>  8     7 -0.0203 
#>  9     8 -0.00420
#> 10     9 -0.136  
#> 11    10 -0.154  
#> 12    11 -0.0972 
#> 13    12  0.0490 
#> 14    13  0.120  
#> 15    14  0.0867 
#> 16    15  0.119  
#> 17    16  0.151  
tidy(ccf(mdeaths, fdeaths, plot = FALSE))
#> # A tibble: 31 x 2
#>       lag     acf
#>     <dbl>   <dbl>
#>  1 -1.25   0.0151
#>  2 -1.17   0.366 
#>  3 -1.08   0.615 
#>  4 -1      0.708 
#>  5 -0.917  0.622 
#>  6 -0.833  0.340 
#>  7 -0.75  -0.0245
#>  8 -0.667 -0.382 
#>  9 -0.583 -0.612 
#> 10 -0.5   -0.678 
#> # … with 21 more rows
tidy(pacf(lh, plot = FALSE))
#> # A tibble: 16 x 2
#>      lag      acf
#>    <dbl>    <dbl>
#>  1     1  0.576  
#>  2     2 -0.223  
#>  3     3 -0.227  
#>  4     4  0.103  
#>  5     5 -0.0759 
#>  6     6  0.0676 
#>  7     7 -0.104  
#>  8     8  0.0120 
#>  9     9 -0.188  
#> 10    10  0.00255
#> 11    11  0.0656 
#> 12    12  0.0320 
#> 13    13  0.0219 
#> 14    14 -0.0931 
#> 15    15  0.230  
#> 16    16  0.0444