Apply Rolling Median Filter — filter_rollmedian • aniprocess

Applies a rolling median filter to a numeric vector using data.table::frollmedian().

Usage

filter_rollmedian(
  x,
  window_width = 5,
  min_obs = 1,
  align = c("center", "right", "left"),
  keep_na = TRUE
)

Arguments

x: Numeric vector to filter.
window_width: Integer specifying window size for the rolling calculation.
min_obs: Minimum number of non-NA values required in the window. Positions with fewer non-NA values return NA. Defaults to 1.
align: Window alignment. One of "center" (default), "right", or "left". A centred window is symmetric about the point, so the filtered signal keeps its timing; "right" looks only backwards, which is what you want when the next sample does not exist yet, at the cost of lagging the signal by (window_width - 1) / 2 samples.
keep_na: Logical. If TRUE (default), positions that were NA in the input are NA in the output — gaps stay gaps. If FALSE, the values used to fill those gaps are kept, so the output has fewer NAs than the input and genuinely-missing stretches come back as interpolated estimates.

Value

Filtered numeric vector, same length as x.

Details

Edge handling matches filter_rollmean(): partial windows at the edges for align = "right"/"left"; NA at the edges for align = "center".

Examples

x <- c(1, 2, 100, 4, 5, 6, 7)
filter_rollmedian(x, window_width = 3)
#> [1] NA  2  4  5  5  6 NA

# The median discards the spike; a rolling mean smears it across three
# neighbouring samples instead
filter_rollmean(x, window_width = 3)
#> [1]       NA 34.33333 35.33333 36.33333  5.00000  6.00000       NA