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Apply Rolling Functions using Circular Statistics

Source: R/roll_statistics.R
roll_circstats.Rd

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

Usage

roll_circstats(
  x,
  w = NULL,
  FUN,
  axial = TRUE,
  na.rm = TRUE,
  width = NULL,
  by.column = FALSE,
  partial = TRUE,
  fill = NA,
  ...
)

Arguments

x

numeric vector. Values in degrees.

w

(optional) Weights. A vector of positive numbers and of the same length as x.

FUN

the function to be applied

axial

logical. Whether the data are axial, i.e. pi-periodical (TRUE, the default) or directional, i.e. \(2 \pi\)-periodical (FALSE).

na.rm

logical value indicating whether NA values in x should be stripped before the computation proceeds.

width

integer specifying the window width (in numbers of observations) which is aligned to the original sample according to the align argument. If NULL, an optimal width is calculated.

by.column

logical. If TRUE, FUN is applied to each column separately.

partial

logical or numeric. If FALSE then FUN is only applied when all indexes of the rolling window are within the observed time range. If TRUE (default), then the subset of indexes that are in range are passed to FUN. A numeric argument to partial can be used to determine the minimal window size for partial computations. See below for more details.

fill

a three-component vector or list (recycled otherwise) providing filling values at the left/within/to the right of the data range. See the fill argument of zoo::na.fill() for details

...

optional arguments passed to zoo::rollapply()

Value

numeric vector with the results of the rolling function.

Note

If the rolling statistics are applied to values that are a function of distance it is recommended to sort the values first.

Examples

data("plates")
plate_boundary <- subset(plates, plates$pair == "na-pa")
data("san_andreas")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
distance <- distance_from_pb(
  x = san_andreas,
  PoR = PoR,
  pb = plate_boundary,
  tangential = TRUE
)
dat <- san_andreas[order(distance), ]
roll_circstats(dat$azi, w = 1 / dat$unc, circular_mean, width = 51) |> head()
#> [1] 172.460149 175.401782 178.547220 176.670176 179.709559   1.298709