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

Usage

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

Value

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

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$unc, FUN = circular_mean)
#>         distance         x   n
#>  [1,] -9.2265048  68.11323   9
#>  [2,] -7.6044815 127.98953  19
#>  [3,] -5.9824582 155.84338  14
#>  [4,] -4.3604350 154.02577  39
#>  [5,] -2.7384117 138.55072 125
#>  [6,] -1.1163884 138.29357 759
#>  [7,]  0.5056349 142.46616 153
#>  [8,]  2.1276582 147.30864   5
#>  [9,]  3.7496815 178.57158   2
#> [10,]  5.3717047        NA   1
distroll_confidence(dat$azi.PoR, distance = dat$distance, w = 1 / dat$unc)
#> Warning: NaNs produced
#>         distance          x   n
#>  [1,] -9.2265048 147.581596   9
#>  [2,] -7.6044815  44.732416  19
#>  [3,] -5.9824582 101.990075  14
#>  [4,] -4.3604350  43.031293  39
#>  [5,] -2.7384117   9.675720 125
#>  [6,] -1.1163884   4.369003 759
#>  [7,]  0.5056349  11.713927 153
#>  [8,]  2.1276582  20.165812   5
#>  [9,]  3.7496815        NaN   2
#> [10,]  5.3717047         NA   1
distroll_dispersion(dat$azi.PoR, y = 135, distance = dat$distance, w = 1 / dat$unc)
#>         distance          x   n
#>  [1,] -9.2265048 0.56974161   9
#>  [2,] -7.6044815 0.31555788  19
#>  [3,] -5.9824582 0.39396588  14
#>  [4,] -4.3604350 0.32627346  39
#>  [5,] -2.7384117 0.06582746 125
#>  [6,] -1.1163884 0.09488957 759
#>  [7,]  0.5056349 0.11027106 153
#>  [8,]  2.1276582 0.06141144   5
#>  [9,]  3.7496815 0.47800478   2
#> [10,]  5.3717047         NA   1
distroll_dispersion_sde(dat$azi.PoR, y = 135, distance = dat$distance, w = 1 / dat$unc, R = 100)
#>         distance          x   n
#>  [1,] -9.2265048 0.19014500   9
#>  [2,] -7.6044815 0.15332942  19
#>  [3,] -5.9824582 0.21273555  14
#>  [4,] -4.3604350 0.11869068  39
#>  [5,] -2.7384117 0.02095192 125
#>  [6,] -1.1163884 0.01171352 759
#>  [7,]  0.5056349 0.02254621 153
#>  [8,]  2.1276582 0.03335982   5
#>  [9,]  3.7496815 0.05898341   2
#> [10,]  5.3717047         NA   1