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 = 3L,
max_data = Inf,
min_dist_threshold = 200,
dist_threshold = 0.1,
stat_threshold = Inf,
R_range = seq(100, 2000, 100),
...
)dispersion_grid(...)
sf object containing
longitude and latitude in degree
output of function defined in stat
The rearch radius in km.
Mean distance of datapoints per search radius
Number of data points in search radius
sf object containing
Azimuth in degree
Uncertainties of azimuth in degree
Predicted value for azimuth
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.
(optional) Point object of class sf.
(optional) numeric vector specifying the minimum
and maximum longitudes and latitudes (are ignored if "grid" is specified).
Numeric. Target spacing of the regular grid in decimal
degree. Default is 2.5. (is ignored if "grid" is specified)
Integer. Minimum number of data per bin. Default is 3
integer. The number of nearest observations that should be
used for prediction, where "nearest" is defined in terms of the space of the
spatial locations. Default is Inf.
Numeric. Maximum distance (in km) of the grid point to the next data point. Default is 200
Numeric. Distance weight to prevent overweight of data
nearby (0 to 1). Default is 0.1
numeric. Generates missing values when the kernel
stat value exceeds this threshold. Default is Inf.
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()
circular_dispersion(), norm_chisq(), rayleigh_test()
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
san_andreas_por <- data2PoR(san_andreas, PoR)
san_andreas_por$prd <- 135
kernel_dispersion(san_andreas_por) |> head()
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