View source: R/interpolation.R
kernel_dispersion | R Documentation |
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),
...
)
x |
|
stat |
The measurement of dispersion to be calculated. Either
|
grid |
(optional) Point object of class |
lon_range , lat_range |
(optional) numeric vector specifying the minimum
and maximum longitudes and latitudes (are ignored if |
gridsize |
Numeric. Target spacing of the regular grid in decimal
degree. Default is 2.5. (is ignored if |
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 |
... |
optional arguments to |
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
circular_dispersion()
, norm_chisq()
, rayleigh_test()
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)
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