krige {gstat} | R Documentation |
Function for simple, ordinary or universal kriging (sometimes called external drift kriging), kriging in a local neighbourhood, point kriging or kriging of block mean values (rectangular or irregular blocks), and conditional (Gaussian or indicator) simulation equivalents for all kriging varieties. For multivariable prediction, see gstat and predict.gstat
krige(formula, locations, data, newdata, model, beta, nmax = Inf, nmin = 0, maxdist = Inf, block, nsim = 0, indicators = FALSE, na.action = na.pass, ...)
formula |
formula that defines the dependent variable as a linear
model of independent variables; suppose the dependent variable has name
z , for ordinary and simple kriging use the formula z~1 ;
for simple kriging also define beta (see below); for universal
kriging, suppose z is linearly dependent on x and y ,
use the formula z~x+y |
locations |
formula with only independent variables that define the
spatial data locations (coordinates), e.g. ~x+y ; if data
is of class spatial.data.frame , this argument may be ignored, as
it can be derived from the data |
data |
data frame; should contain the dependent variable, independent variables, and coordinates. |
newdata |
data frame with prediction/simulation locations; should
contain columns with the independent variables (if present) and the
coordinates with names as defined in locations |
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 is less than nmin , a missing
value will be generated; see maxdist |
maxdist |
for local kriging: only observations within a distance
of maxdist from the prediction location are used for prediction
or simulation; if combined with nmax , both criteria apply |
block |
block size; a vector with 1, 2 or 3 values containing the size of a rectangular in x-, y- and z-dimension respectively (0 if not set), or a data frame with 1, 2 or 3 columns, containing the points that discretize the block in the x-, y- and z-dimension to define irregular blocks relative to (0,0) or (0,0,0)—see also the details section of predict.gstat. By default, predictions or simulations refer to the support of the data values. |
nsim |
integer; if set to a non-zero value, conditional simulation
is used instead of kriging interpolation. For this, sequential Gaussian
or indicator simulation is used (depending on the value of
indicators ), following a single random path through the data. |
indicators |
logical, only relevant if nsim is non-zero; if
TRUE, use indicator simulation; else use Gaussian simulation |
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. |
... |
other arguments that will be passed to gstat |
This function is a simple wrapper function around gstat and predict.gstat for univariate kriging prediction and conditional simulation methods available in gstat. For multivariate prediction or simulation, or for other interpolation methods provided by gstat (such as inverse distance weighted interpolation or trend surface interpolation) use the functions gstat and predict.gstat directly.
For further details, see predict.gstat.
a data frame containing the coordinates of newdata
, and columns
of prediction and prediction variance (in case of kriging) or the
abs(nsim)
columns of the conditional Gaussian or indicator
simulations
Daniel G. Krige is a South African scientist who was a mining engineer
when he first used generalised least squares prediction with spatial
covariances in the 50's. George Matheron coined the term kriging
in the 60's for the action of doing this, although very similar approaches
had been taken in the field of meteorology. Beside being Krige's name,
I consider "krige" to be to "kriging" what "predict" is to "prediction".
Edzer J. Pebesma
N.A.C. Cressie, 1993, Statistics for Spatial Data, Wiley.
Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.
data(meuse) data(meuse.grid) m <- vgm(.59, "Sph", 874, .04) # ordinary kriging: x <- krige(log(zinc)~1, ~x+y, model = m, data = meuse, newd = meuse.grid) library(lattice) levelplot(var1.pred~x+y, x, aspect = mapasp(x), main = "ordinary kriging predictions") levelplot(var1.var~x+y, x, aspect = mapasp(x), main = "ordinary kriging variance") # simple kriging: x <- krige(log(zinc)~1, ~x+y, model = m, data = meuse, newdata = meuse.grid, beta=5.9) # residual variogram: m <- vgm(.4, "Sph", 954, .06) # universal block kriging: x <- krige(log(zinc)~x+y, ~x+y, model = m, data = meuse, newdata = meuse.grid, block = c(40,40)) levelplot(var1.pred~x+y, x, aspect = mapasp(x), main = "universal kriging predictions") levelplot(var1.var~x+y, x, aspect = mapasp(x), main = "universal kriging variance")