rolling_test_dist function

Apply Rolling Functions using Circular Statistics

A generic function for applying a function to rolling margins of an array along an additional value.

distroll_circstats(
  x,
  distance,
  FUN,
  width = NULL,
  min_n = 2,
  align = c("right", "center", "left"),
  w = NULL,
  sort = TRUE,
  ...
)

distroll_confidence(
  x,
  distance,
  w = NULL,
  width = NULL,
  min_n = 2,
  align = c("right", "center", "left"),
  sort = TRUE,
  ...
)

distroll_dispersion(
  x,
  y,
  w = NULL,
  w.y = NULL,
  distance,
  width = NULL,
  min_n = 2,
  align = c("right", "center", "left"),
  sort = TRUE,
  ...
)

distroll_dispersion_sde(
  x,
  y,
  w = NULL,
  w.y = NULL,
  distance,
  width = NULL,
  min_n = 2,
  align = c("right", "center", "left"),
  sort = TRUE,
  ...
)

Arguments

• x, y: vectors of numeric values in degrees. length(y) is either 1 or length(x)

• distance: numeric. the independent variable along the values in x

  are sorted, e.g. the plate boundary distances

• FUN: the function to be applied

• width: numeric. the range across distance on which FUN should be applied on x. If NULL, then width is a number that separates the distances in 10 equal groups.

• min_n: integer. The minimum values that should be considered in FUN

  (2 by default), otherwise NA.

• align: specifies whether the index of the result should be left- or right-aligned or centered (default) compared to the rolling window of observations. This argument is only used if width represents widths.

• w: numeric. the weighting for x

• sort: logical. Should the values be sorted after distance prior to applying the function (TRUE by default).

• ...: optional arguments to FUN

• w.y: numeric. the weighting for y

Returns

two-column vectors of (sorted) x and the rolled statistics along distance.

Note

distroll_circstats() and friends are complete, and for new code it is recommended switching to distance_binned_stats(), which is fasrter, easier to use, more featureful, and still under active development.

Examples

data("plates")
plate_boundary <- subset(plates, plates$pair == "na-pa")
data("san_andreas")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
san_andreas$distance <- distance_from_pb(
  x = san_andreas,
  PoR = PoR,
  pb = plate_boundary,
  tangential = TRUE
)
dat <- san_andreas |> cbind(PoR_shmax(san_andreas, PoR, "right"))

distroll_circstats(dat$azi.PoR,
  distance = dat$distance,
  w = 1 / dat$unc, FUN = circular_mean
) |> head()
distroll_confidence(dat$azi.PoR, distance = dat$distance, w = 1 / dat$unc) |> head()
distroll_dispersion(dat$azi.PoR,
  y = 135,
  distance = dat$distance, w = 1 / dat$unc
) |> head()
distroll_dispersion_sde(dat$azi.PoR,
  y = 135,
  distance = dat$distance, w = 1 / dat$unc, R = 100
) |> head()

# New functions
distance_binned_stats(
  dat$azi.PoR,
  distance = dat$distance, width.breaks = 1, unc = dat$unc, prd = 135
) |> head()