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Signal VS Noise

Source: R/tests.R
signalnoise.Rd

Linear regression models to determine whether revisions are ‘news’ or ‘noise’. For 'noise': R (revisions) on P (preliminary estimate). For 'news': R on L (latter estimate).

Usage

signalnoise(vintages.view, gap = 1, na.zero = FALSE)

Arguments

vintages.view

mts object. Vertical or diagonal view of the create_vintages() output

gap

Integer. Gap to consider between each vintages. Default is 1 which means that revisions are calculated and tested for each vintages consecutively.

na.zero

Boolean whether missing values should be considered as 0 or rather as data not (yet) available (the default).

See also

revision_analysis(), render_report()

Examples

## Simulated data
df_long <- simulate_long(
    n_period = 10L * 4L,
    n_revision = 5L,
    periodicity = 4L,
    start_period = as.Date("2010-01-01")
)

## Create vintage and test
vintages <- create_vintages(df_long, periodicity = 4L)
signalnoise(vintages[["diagonal_view"]])
#>                                News.R2       News.F News.pvalue    Noise.R2
#> [Release[2]]-[Release[1]] 2.092282e-03 0.0669530179   0.9353785 0.002278357
#> [Release[3]]-[Release[2]] 2.406276e-04 0.0060156911   0.9940039 0.006630292
#> [Release[4]]-[Release[3]] 8.888437e-06 0.0001244381   0.9998756 0.010529377
#> [Release[5]]-[Release[4]] 9.803707e-02 0.3921482835   0.7183143 0.078961499
#>                              Noise.F Noise.pvalue
#> [Release[2]]-[Release[1]] 0.07290742    0.9298511
#> [Release[3]]-[Release[2]] 0.16575729    0.8482549
#> [Release[4]]-[Release[3]] 0.14741128    0.8644779
#> [Release[5]]-[Release[4]] 0.31584599    0.7599674