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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]] 0.01952634 0.6248429   0.5421682 0.002009833
#> [Release[3]]-[Release[2]] 0.03125519 0.6876142   0.5142713 0.054619515
#> [Release[4]]-[Release[3]] 0.05215226 0.5215226   0.6124930 0.113613559
#> [Release[5]]-[Release[4]] 1.00000000 2.0000000         NaN 1.000000000
#>                              Noise.F Noise.pvalue
#> [Release[2]]-[Release[1]] 0.06431466    0.9378388
#> [Release[3]]-[Release[2]] 1.20162933    0.3215052
#> [Release[4]]-[Release[3]] 1.13613559    0.3678697
#> [Release[5]]-[Release[4]] 2.00000000          NaN