krige.cv  R Documentation 
Cross validation functions for simple, ordinary or universal point (co)kriging, kriging in a local neighbourhood.
gstat.cv(object, nfold, remove.all = FALSE, verbose = interactive(), all.residuals = FALSE, ...) krige.cv(formula, locations, ...) krige.cv.locations(formula, locations, data, model = NULL, ..., beta = NULL, nmax = Inf, nmin = 0, maxdist = Inf, nfold = nrow(data), verbose = interactive(), debug.level = 0) krige.cv.spatial(formula, locations, model = NULL, ..., beta = NULL, nmax = Inf, nmin = 0, maxdist = Inf, nfold = nrow(locations), verbose = interactive(), debug.level = 0)
object 
object of class gstat; see function gstat 
nfold 
integer; if larger than 1, then apply nfold cross validation;
if 
remove.all 
logical; if TRUE, remove observations at cross validation locations not only for the first, but for all subsequent variables as well 
verbose 
logical; if FALSE, progress bar is suppressed 
all.residuals 
logical; if TRUE, residuals for all variables are returned instead of for the first variable only 
... 
other arguments that will be passed to predict
in case of 
formula 
formula that defines the dependent variable as a linear
model of independent variables; suppose the dependent variable has name

locations 
data object deriving from class 
data 
data frame (deprecated); should contain the dependent variable, independent
variables, and coordinates; only to be provided if 
model 
variogram model of dependent variable (or its residuals), defined by a call to vgm or fit.variogram 
beta 
only for simple kriging (and simulation based on simple kriging); vector with the trend coefficients (including intercept); if no independent variables are defined the model only contains an intercept and this should be the simple kriging mean 
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 
debug.level 
print debugging information; 0 suppresses debug information 
Leaveoneout cross validation (LOOCV) visits a data point, and predicts the value at that location by leaving out the observed value, and proceeds with the next data point. (The observed value is left out because kriging would otherwise predict the value itself.) Nfold cross validation makes a partitions the data set in N parts. For all observation in a part, predictions are made based on the remaining N1 parts; this is repeated for each of the N parts. Nfold cross validation may be faster than LOOCV.
data frame containing the coordinates of data
or those
of the first variable in object
, and columns of prediction and
prediction variance of cross validated data points, observed values,
residuals, zscore (residual divided by kriging standard error), and fold.
If all.residuals
is true, a data frame with residuals for all
variables is returned, without coordinates.
locations specifies which coordinates in data
refer to spatial coordinates
Object locations knows about its own spatial locations
Leaveoneout cross validation seems to be much faster in plain (standalone) gstat, apparently quite a bit of the effort is spent moving data around from R to gstat.
Edzer Pebesma
krige, gstat, predict
library(sp) data(meuse) coordinates(meuse) < ~x+y m < vgm(.59, "Sph", 874, .04) # fivefold cross validation: x < krige.cv(log(zinc)~1, meuse, m, nmax = 40, nfold=5) bubble(x, "residual", main = "log(zinc): 5fold CV residuals") # multivariable; thanks to M. Rufino: meuse.g < gstat(id = "zn", formula = log(zinc) ~ 1, data = meuse) meuse.g < gstat(meuse.g, "cu", log(copper) ~ 1, meuse) meuse.g < gstat(meuse.g, model = vgm(1, "Sph", 900, 1), fill.all = TRUE) x < variogram(meuse.g, cutoff = 1000) meuse.fit = fit.lmc(x, meuse.g) out = gstat.cv(meuse.fit, nmax = 40, nfold = 5) summary(out) out = gstat.cv(meuse.fit, nmax = 40, nfold = c(rep(1,100), rep(2,55))) summary(out) # mean error, ideally 0: mean(out$residual) # MSPE, ideally small mean(out$residual^2) # Mean square normalized error, ideally close to 1 mean(out$zscore^2) # correlation observed and predicted, ideally 1 cor(out$observed, out$observed  out$residual) # correlation predicted and residual, ideally 0 cor(out$observed  out$residual, out$residual)
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