kernel_dispersion function

Adaptive Kernel Dispersion

Stress field and wavelength analysis using circular dispersion (or other statistical estimators for dispersion)

kernel_dispersion(
  x,
  stat = c("dispersion", "nchisq", "rayleigh"),
  grid = NULL,
  lon_range = NULL,
  lat_range = NULL,
  gridsize = 2.5,
  min_data = 3,
  threshold = 1,
  arte_thres = 200,
  dist_threshold = 0.1,
  R_range = seq(100, 2000, 100),
  ...
)

dispersion_grid(...)

Arguments

• x: sf object containing

  • azi: Azimuth in degree
  • unc: Uncertainties of azimuth in degree
  • prd: Predicted value for azimuth

• stat: The measurement of dispersion to be calculated. Either "dispersion" (default), "nchisq", or "rayleigh" for circular dispersion, normalized Chi-squared test statistic, or Rayleigh test statistic.

• grid: (optional) Point object of class sf.

• lon_range, lat_range: (optional) numeric vector specifying the minimum and maximum longitudes and latitudes (are ignored if "grid" is specified).

• gridsize: Numeric. Target spacing of the regular grid in decimal degree. Default is 2.5. (is ignored if "grid" is specified)

• min_data: Integer. Minimum number of data per bin. Default is 3

• threshold: Numeric. Threshold for stat value (default is 1)

• arte_thres: Numeric. Maximum distance (in km) of the grid point to the next data point. Default is 200

• dist_threshold: Numeric. Distance weight to prevent overweight of data nearby (0 to 1). Default is 0.1

• R_range: Numeric value or vector specifying the (adaptive) kernel half-width(s) as search radius (in km). Default is seq(50, 1000, 50)

• ...: optional arguments to dist_greatcircle()

Returns

sf object containing

• lon,lat: longitude and latitude in degree
• stat: output of function defined in stat
• R: The rearch radius in km.
• mdr: Mean distance of datapoints per search radius
• N: Number of data points in search radius

Note

dispersion_grid() was renamed to kernel_dispersion() to create a more consistent API.

Examples

data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
san_andreas_por <- san_andreas
san_andreas_por$azi <- PoR_shmax(san_andreas, PoR, "right")$azi.PoR
san_andreas_por$prd <- 135
kernel_dispersion(san_andreas_por)

See Also

circular_dispersion(), norm_chisq(), rayleigh_test()