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Generic Function for Seasonal Adjustment Decomposition

Source: R/decomposition.R, R/generics.R
sa_decomposition.Rd

Generic function to format the seasonal adjustment decomposition components. sa_decomposition() is a generic function defined in other packages.

Usage

sadecomposition(y, sa, t, s, i, mul)

# S3 method for class 'JD3_SADECOMPOSITION'
print(x, n_last_obs = frequency(x$series), ...)

# S3 method for class 'JD3_SADECOMPOSITION'
plot(
  x,
  first_date = NULL,
  last_date = NULL,
  type_chart = c("sa-trend", "seas-irr"),
  caption = c(`sa-trend` = "Y, Sa, trend", `seas-irr` = "Sea., irr.")[type_chart],
  colors = c(y = "#F0B400", t = "#1E6C0B", sa = "#155692", s = "#1E6C0B", i = "#155692"),
  ...
)

sa_decomposition(x, ...)

Arguments

y, sa, t, s, i, mul

seasonal adjustment decomposition parameters.

x

the object to print.

n_last_obs

number of observations to print (by default equal to the frequency of the series).

...

further arguments.

first_date, last_date

first and last date to plot (by default all the data is used).

type_chart

the chart to plot: "sa-trend" (by default) plots the input time series, the seasonally adjusted and the trend; "seas-irr" plots the seasonal and the irregular components.

caption

the caption of the plot.

colors

the colours used in the plot.

Value

"JD3_SADECOMPOSITION" object.

Examples

decompo <- sadecomposition(
    y = ts(c(112, 118, 132, 129, 121, 135), start = 2000, frequency = 12L),
    sa = ts(c(121.72, 124.52, 125.4, 128.91, 128.84, 126.73), start = 2000, frequency = 12L),
    t = ts(c(122.24, 124.33, 126.21, 127.61, 127.8, 126.94), start = 2000, frequency = 12L),
    s = ts(c(0.92, 0.95, 1.05, 1, 0.94, 1.07), start = 2000, frequency = 12L),
    i = ts(c(1, 1, 0.99, 1.01, 1.01, 1), start = 2000, frequency = 12L),
    mul = TRUE
)
print(decompo)
#> Last values
#>          series     sa  trend seas  irr
#> Jan 2000    112 121.72 122.24 0.92 1.00
#> Feb 2000    118 124.52 124.33 0.95 1.00
#> Mar 2000    132 125.40 126.21 1.05 0.99
#> Apr 2000    129 128.91 127.61 1.00 1.01
#> May 2000    121 128.84 127.80 0.94 1.01
#> Jun 2000    135 126.73 126.94 1.07 1.00
plot(decompo)