augment.decomposed.ts {broom} | R Documentation

## Augment data with information from a(n) decomposed.ts object

### Description

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 all columns used to fit the model are present.

Augment will often behavior different depending on whether `data` or `newdata` is specified. 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 some 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.

### Usage

    ## S3 method for class 'decomposed.ts'
    augment(x, ...)

### Arguments

`x` | A `decomposed.ts` object returned from `stats::decompose()`.

`...`

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 observation in the original times series:

``.seasonal`` | The seasonal component of the decomposition.
``.trend`` | The trend component of the decomposition.
``.remainder`` | The remainder, or "random" component of the decomposition.
``.weight`` | The final robust weights (`stl` only).
``.seasadj`` | The seasonally adjusted (or "deseasonalised") series.

### See Also

`augment()`, `stats::decompose()`

Other decompose tidiers: `augment.stl`

### Examples

    # 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(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))

[Package broom version 0.5.2 Index]