TransGaussian kriging using Box-Cox transforms
TransGaussian (ordinary) kriging function using Box-Cox transforms
krigeTg(formula, locations, newdata, model = NULL, ..., nmax = Inf, nmin = 0, maxdist = Inf, block = numeric(0), nsim = 0, na.action = na.pass, debug.level = 1, lambda = 1.0)
formula |
formula that defines the dependent variable as a linear
model of independent variables; suppose the dependent variable has name
|
locations |
object of class |
newdata |
Spatial object with prediction/simulation locations;
the coordinates should have names as defined in |
model |
variogram model of the TRANSFORMED dependent variable, see vgm, or fit.variogram |
nmax |
for local kriging: the number of nearest observations that should be used for a kriging prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, all observations are used |
nmin |
for local kriging: if the number of nearest observations
within distance |
maxdist |
for local kriging: only observations within a distance
of |
block |
does not function correctly, afaik |
nsim |
does not function correctly, afaik |
na.action |
function determining what should be done with missing values in 'newdata'. The default is to predict 'NA'. Missing values in coordinates and predictors are both dealt with. |
lambda |
value for the Box-Cox transform |
debug.level |
debug level, passed to predict; use -1 to see progress in percentage, and 0 to suppress all printed information |
... |
other arguments that will be passed to gstat |
Function krigeTg uses transGaussian kriging as explained in
http://www.math.umd.edu/~bnk/bak/Splus/kriging.html.
As it uses the R/gstat krige function to derive everything, it needs in addition to ordinary kriging on the transformed scale a simple kriging step to find m from the difference between the OK and SK prediction variance, and a kriging/BLUE estimation step to obtain the estimate of mu.
an SpatialPointsDataFrame object containing the fields:
m for the m (Lagrange) parameter for each location;
var1SK.pred the c0 Cinv correction obtained by
muhat for the mean estimate at each location;
var1SK.var the simple kriging variance;
var1.pred the OK prediction on the transformed scale;
var1.var the OK kriging variance on the transformed scale;
var1TG.pred the transGaussian kriging predictor;
var1TG.var the transGaussian kriging variance, obtained by
phi'(muhat, lambda)^2 * var1.var
Edzer Pebesma
N.A.C. Cressie, 1993, Statistics for Spatial Data, Wiley.
library(sp) data(meuse) coordinates(meuse) = ~x+y data(meuse.grid) gridded(meuse.grid) = ~x+y v = vgm(1, "Exp", 300) x1 = krigeTg(zinc~1,meuse,meuse.grid,v, lambda=1) # no transform x2 = krige(zinc~1,meuse,meuse.grid,v) summary(x2$var1.var-x1$var1TG.var) summary(x2$var1.pred-x1$var1TG.pred) lambda = -0.25 m = fit.variogram(variogram((zinc^lambda-1)/lambda ~ 1,meuse), vgm(1, "Exp", 300)) x = krigeTg(zinc~1,meuse,meuse.grid,m,lambda=-.25) spplot(x["var1TG.pred"], col.regions=bpy.colors()) summary(meuse$zinc) summary(x$var1TG.pred)
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