R: TransGaussian kriging using Box-Cox transforms

krigeTg {gstat} | R Documentation

## TransGaussian kriging using Box-Cox transforms

### Description

TransGaussian (ordinary) kriging function using Box-Cox transforms

### Usage

    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)

### 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 use a formula like `z~1`; the dependent variable should be NOT transformed.
`locations` | object of class `Spatial`, with observations
`newdata` | Spatial object with prediction/simulation locations; the coordinates should have names as defined in `locations`
`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` 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` | 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

### Details

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.

For further details, see krige and predict.

### Value

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

### Author(s)

Edzer Pebesma

### References

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

http://www.gstat.org/

### See Also

gstat, predict

### Examples

    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)

[Package gstat version 2.0-3 Index]