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.
# S3 method for spec
tidy(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
. 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.
A tibble::tibble()
with columns:
Vector of frequencies at which the spectral density is estimated.
Vector (for univariate series) or matrix (for multivariate series) of estimates of the spectral density at frequencies corresponding to freq.
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
#> # … with 14 more rows
library(ggplot2)
ggplot(tidy(spc), aes(freq, spec)) +
geom_line()