Augment data with information from a(n) decomposed.ts object
Augment accepts a model object and a dataset and adds
information about each observation in the dataset. Most commonly, this
includes predicted values in the .fitted column, residuals in the
.resid column, and standard errors for the fitted values in a .se.fit
column. New columns always begin with a . prefix to avoid overwriting
columns in the original dataset.
Users may pass data to augment via either the data argument or the
newdata argument. If the user passes data to the data argument,
it must be exactly the data that was used to fit the model
object. Pass datasets to newdata to augment data that was not used
during model fitting. This still requires that at least all predictor
variable columns used to fit the model are present. If the original outcome
variable used to fit the model is not included in newdata, then no
.resid column will be included in the output.
Augment will often behave differently depending on whether data or
newdata is given. This is because there is often information
associated with training observations (such as influences or related)
measures that is not meaningfully defined for new observations.
For convenience, many augment methods provide default data arguments,
so that augment(fit) will return the augmented training data. In these
cases, augment tries to reconstruct the original data based on the model
object with varying degrees of success.
The augmented dataset is always returned as a tibble::tibble with the
same number of rows as the passed dataset. This means that the
passed data must be coercible to a tibble. At this time, tibbles do not
support matrix-columns. This means you should not specify a matrix
of covariates in a model formula during the original model fitting
process, and that splines::ns(), stats::poly() and
survival::Surv() objects are not supported in input data. If you
encounter errors, try explicitly passing a tibble, or fitting the original
model on data in a tibble.
We are in the process of defining behaviors for models fit with various
na.action arguments, but make no guarantees about behavior when data is
missing at this time.
## S3 method for class 'decomposed.ts' augment(x, ...)
x |
A |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
A tibble::tibble with one row for each observation in the original times series:
|
The seasonal component of the decomposition. |
|
The trend component of the decomposition. |
|
The remainder, or "random" component of the decomposition. |
|
The final robust weights ( |
|
The seasonally adjusted (or "deseasonalised") series. |
Other decompose tidiers:
augment.stl()
# Time series of temperatures in Nottingham, 1920-1939:
nottem
# Perform seasonal decomposition on the data with both decompose
# and stl:
d1 <- stats::decompose(nottem)
d2 <- stats::stl(nottem, s.window = "periodic", robust = TRUE)
# Compare the original series to its decompositions.
cbind(
broom::tidy(nottem), broom::augment(d1),
broom::augment(d2)
)
# Visually compare seasonal decompositions in tidy data frames.
library(tibble)
library(dplyr)
library(tidyr)
library(ggplot2)
decomps <- tibble(
# Turn the ts objects into data frames.
series = list(as.data.frame(nottem), as.data.frame(nottem)),
# Add the models in, one for each row.
decomp = c("decompose", "stl"),
model = list(d1, d2)
) %>%
rowwise() %>%
# Pull out the fitted data using broom::augment.
mutate(augment = list(broom::augment(model))) %>%
ungroup() %>%
# Unnest the data frames into a tidy arrangement of
# the series next to its seasonal decomposition, grouped
# by the method (stl or decompose).
group_by(decomp) %>%
unnest(c(series, augment)) %>%
mutate(index = 1:n()) %>%
ungroup() %>%
select(decomp, index, x, adjusted = .seasadj)
ggplot(decomps) +
geom_line(aes(x = index, y = x), colour = "black") +
geom_line(aes(
x = index, y = adjusted, colour = decomp,
group = decomp
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