View source: R/interpolation.R
stress2grid | R Documentation |
Stress field interpolation and wavelength analysis using a kernel (weighted) mean/median and standard deviation/IQR of stress data. Parameters can be adjusted to have inverse-distance-weighting (IDW) or nearest-neighbor interpolations (NN).
stress2grid(
x,
stat = c("mean", "median"),
grid = NULL,
lon_range = NULL,
lat_range = NULL,
gridsize = 2,
min_data = 3L,
max_data = Inf,
max_sd = Inf,
threshold = deprecated(),
min_dist_threshold = 200,
arte_thres = deprecated(),
method_weighting = FALSE,
quality_weighting = TRUE,
dist_weighting = c("inverse", "linear", "none"),
idp = 1,
qp = 1,
mp = 1,
dist_threshold = 0.1,
R_range = seq(50, 1000, 50),
...
)
stress2grid_stats(
x,
grid = NULL,
lon_range = NULL,
lat_range = NULL,
gridsize = 2,
min_data = 4L,
max_data = Inf,
threshold = deprecated(),
min_dist_threshold = 200,
arte_thres = deprecated(),
method_weighting = FALSE,
quality_weighting = TRUE,
dist_weighting = c("inverse", "linear", "none"),
idp = 1,
qp = 1,
mp = 1,
dist_threshold = 0.1,
R_range = seq(50, 1000, 50),
mode = FALSE,
kappa = 10,
...
)
x |
|
stat |
whether the direction of interpolated SHmax is based on the
circular mean and standard deviation ( |
grid |
(optional) Point object of class |
lon_range , lat_range |
(optional) numeric vector specifying the minimum
and maximum longitudes and latitudes (ignored if |
gridsize |
numeric. Target spacing of the regular grid in decimal
degree. Default is |
min_data |
integer. If the number of observations within distance
|
max_data |
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 |
max_sd |
numeric. Threshold for deviation of direction in degrees; if exceeds, missing values will be generated. |
threshold |
|
min_dist_threshold |
numeric. Distance threshold for smallest distance
of the prediction location to the next observation location.
Default is |
arte_thres |
|
method_weighting |
logical. If a method weighting should be applied:
Default is |
quality_weighting |
logical. If a quality weighting should be applied:
Default is |
dist_weighting |
Distance weighting method which should be used. One of
|
idp , qp , mp |
numeric. The weighting power of inverse distance, quality
and method (the higher the value, the more weight).
Default is |
dist_threshold |
numeric. Distance weight to prevent overweight of data
nearby (0 to 1). Default is |
R_range |
numeric value or vector specifying the kernel half-width(s)
search radii,
i.e. the maximum distance from the prediction location to be used for
prediction (in km). Default is |
... |
(optional) arguments to |
mode |
logical. Should the circular mode be included in the statistical summary (slow)? |
kappa |
numeric. von Mises distribution concentration parameter used
for the circular mode. Will be estimated using |
stress2grid()
is originally based on the MATLAB script
"stress2grid" by Ziegler and Heidbach (2019):
https://github.com/MorZieg/Stress2Grid.
The tectonicr version has been significantly modified to provide better
performance and more flexibility.
stress2grid_stats()
is based on stress2grid()
but calculates circular
summary statistics (see circular_summary()
).
sf
object containing
longitude and latitude in degrees
Circular mean od median SHmax in degree
Circular standard deviation or Quasi-IQR on the Circle of SHmax in degrees
Search radius in km
Mean distance between grid point and datapoints per search radius
Number of data points in search radius
When stress2grid_stats()
, azi
and sd
are replaced by the output of
circular_summary()
.
Ziegler, M. and Heidbach, O. (2019). Matlab Script Stress2Grid v1.1. GFZ Data Services. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.5880/wsm.2019.002")}
dist_greatcircle()
, PoR_stress2grid()
, compact_grid()
,
circular_mean()
, circular_median()
, circular_sd()
, circular_summary()
data("san_andreas")
# Inverse Distance Weighting interpolation:
stress2grid(san_andreas, stat = "median") |> head()
# Nearest Neighbor interpolation:
stress2grid(san_andreas, stat = "median", max_data = 5) |> head()
## Not run:
stress2grid_stats(san_andreas) |> head()
## End(Not run)
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