deconvPointVgm {atakrig} | R Documentation |
Point-scale variogram, cross-variogram deconvolution.
Description
Point-scale variogram, cross-variogram deconvolution.
Usage
deconvPointVgm(x, model = "Exp", maxIter = 100,
fixed.range = NA, longlat = FALSE, maxSampleNum = 100, fig = TRUE, ...)
deconvPointCrossVgm(x, y, xPointVgm, yPointVgm, model = "Exp",
maxIter = 100, fixed.range = NA, longlat = FALSE,
maxSampleNum = 100, fig = TRUE, ...)
deconvPointVgmForCoKriging(x, model = "Exp", maxIter = 100,
fixed.range = NA, maxSampleNum = 100, fig = TRUE, ...)
Arguments
x , y |
for for |
xPointVgm , yPointVgm |
point-scale variograms of x and y respectively, gstat variogramModel. |
model |
commonly used variogram models supported, "Exp" for exponential model, "Sph" for spherical model, "Gau" for gaussian model. |
maxIter |
max iteration number of deconvolution. |
fixed.range |
a fixed variogram range for deconvoluted point-scale variogram. |
longlat |
indicator whether coordinates are longitude/latitude. |
maxSampleNum |
to save memory and to reduce calculation time, for large number of discretized areas, a number (maxSampleNum) of random sample will be used. The samples are collected by system sampling method. |
fig |
whether to plot deconvoluted variogram. |
... |
additional paramters passed to autofitVgm. |
Details
The deconvolution algorithm is implemented according to Pierre Goovaerts, Math. Geosci., 2008, 40: 101-128.
Value
pointVariogram |
deconvoluted point variogram. |
areaVariogram |
fitted area variogram from area centroids. |
experientialAreaVariogram |
experiential area variogram from area centroids. |
regularizedAreaVariogram |
regularized area variogram from discretized area points and point variogram. |
References
Goovaerts, P., 2008. Kriging and semivariogram deconvolution in the presence of irregular geographical units. Mathematical Geosciences 40 (1): 101-128.
See Also
Examples
library(atakrig)
library(terra)
rpath <- system.file("extdata", package="atakrig")
aod3k <- rast(file.path(rpath, "MOD04_3K_A2017042.tif"))
aod3k.d <- discretizeRaster(aod3k, 1500)
grid.pred <- discretizeRaster(aod3k, 1500, type = "all")
sv.ok <- deconvPointVgm(aod3k.d, model="Exp", ngroup=12, rd=0.8, fig = FALSE)
#pred.ataok <- ataKriging(aod3k.d, grid.pred, sv.ok, showProgress = FALSE)
library(atakrig)
library(sf)
## load demo data from rtop package
#if (!require("rtop", quietly = TRUE)) message("rtop library is required for demo data.")
rpath <- system.file("extdata", package="rtop")
observations <- read_sf(rpath, "observations")
## point-scale variogram
obs.discrete <- discretizePolygon(observations, cellsize=1500, id="ID", value="obs")
pointsv <- deconvPointVgm(obs.discrete, model="Exp", ngroup=12, rd=0.75, fig=TRUE)