Decomposes a FASSTER model into its individual components, allowing you to examine the contribution of each term to the fitted values. This is useful for understanding which components drive the model's predictions and how well different aspects of the model fit the data.
Usage
# S3 method for class 'FASSTER'
components(object, ...)Value
A dable (decomposition table) containing the response variable and each model component as separate columns. The components sum to the response variable.
Examples
if (requireNamespace("tsibbledata", quietly = TRUE)) {
# Fit a FASSTER model and extract components
library(tsibble)
library(dplyr)
fit <- tsibbledata::aus_retail |>
filter(
State == "Victoria",
Industry == "Cafes, restaurants and catering services"
) |>
model(fasster = FASSTER(Turnover ~ trend(1) + season("year")))
# Extract and view components
components(fit)
}
#>
#> Attaching package: ‘tsibble’
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, union
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
#> # A dable: 441 x 7 [1M]
#> # Key: State, Industry, .model [1]
#> # : Turnover = `season("year")` + `trend(1)`
#> State Industry .model Month Turnover `season("year")` `trend(1)`
#> <chr> <chr> <chr> <mth> <dbl> <dbl> <dbl>
#> 1 Victoria Cafes, restaur… fasst… 1982 Apr 36.4 -2.08 37.9
#> 2 Victoria Cafes, restaur… fasst… 1982 May 36.2 -2.01 38.1
#> 3 Victoria Cafes, restaur… fasst… 1982 Jun 35.7 -1.95 38.2
#> 4 Victoria Cafes, restaur… fasst… 1982 Jul 34.6 -1.39 37.9
#> 5 Victoria Cafes, restaur… fasst… 1982 Aug 32.5 -1.46 37.1
#> 6 Victoria Cafes, restaur… fasst… 1982 Sep 33.9 -0.799 35.7
#> 7 Victoria Cafes, restaur… fasst… 1982 Oct 37.7 1.95 35.2
#> 8 Victoria Cafes, restaur… fasst… 1982 Nov 40.3 3.81 35.5
#> 9 Victoria Cafes, restaur… fasst… 1982 Dec 45.2 8.71 35.9
#> 10 Victoria Cafes, restaur… fasst… 1983 Jan 36.9 -1.38 36.2
#> # ℹ 431 more rows