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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 spec
tidy(x, ...)

Arguments

x

A spec object created by stats::spectrum().

...

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:

  • tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

  • augment() methods will warn when supplied a newdata argument if it will be ignored.

See also

tidy(), stats::spectrum()

Other time series tidiers: tidy.acf(), tidy.ts(), tidy.zoo()

Value

A tibble::tibble() with columns:

freq

Vector of frequencies at which the spectral density is estimated.

spec

Vector (for univariate series) or matrix (for multivariate series) of estimates of the spectral density at frequencies corresponding to freq.

Examples


spc <- spectrum(lh)

tidy(spc)
#> # A tibble: 24 × 2
#>      freq   spec
#>     <dbl>  <dbl>
#>  1 0.0208 0.0912
#>  2 0.0417 0.331 
#>  3 0.0625 0.836 
#>  4 0.0833 1.17  
#>  5 0.104  0.350 
#>  6 0.125  1.51  
#>  7 0.146  0.328 
#>  8 0.167  0.618 
#>  9 0.188  0.320 
#> 10 0.208  0.0675
#> # ℹ 14 more rows

library(ggplot2)
ggplot(tidy(spc), aes(freq, spec)) +
  geom_line()