gstat (version 0.9-22)

krige: Simple, Ordinary or Universal, global or local, Point or Block Kriging, or simulation.

Description

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, and function for inverse distance weighted interpolation. For multivariable prediction, see gstat and predict.gstat

Usage

krige(formula, locations, data, newdata, model, ..., beta, nmax
= Inf, nmin = 0, maxdist = Inf, block, nsim = 0, indicators = FALSE,
na.action = na.pass)
idw(formula, locations, data, newdata, nmax = Inf, nmin = 0, maxdist = Inf, block = numeric(0),
na.action = na.pass, idp = 2.0)

Arguments

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 be

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-dimens

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

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

idp

numeric; specify the inverse distance weighting power

Value

• 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

Details

Function krige 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.

Function idw performs just as krige without a model being passed, but allows direct specification of the inverse distance weighting power. Don't use with predictors in the formula.

For further details, see predict.gstat.

References

N.A.C. Cressie, 1993, Statistics for Spatial Data, Wiley.

http://www.gstat.org/

Pebesma, E.J., 2004. Multivariable geostatistics in S: the gstat package. Computers & Geosciences, 30: 683-691.

See Also

gstat, predict.gstat

Examples

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")
# add grid:
levelplot(var1.var~x+y, x, aspect = mapasp(x),
		panel = function(...) {
			panel.levelplot(...)
			panel.abline(h = 0:3*1000 + 330000, v= 0:2*1000 + 179000, col = "grey")
		},
	main = "universal kriging variance")