predict.gstatModel: Predict from an object of class '"gstatModel"'

Description Usage Arguments Details Note Author(s) References See Also

View source: R/predict.gstatModel.R

Description

Predicts from an object of class gstatModel-class using new prediction locations. The function combines predictions by regression (e.g. GLM) and interpolation of residuals (kriging) via the Regression-Kriging (RK) or Kriging with External Drift (KED, also known as Universal Kriging) framework.

Usage

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## S4 method for signature 'gstatModel'
predict(object, 
     predictionLocations, nmin = 10, nmax = 30, debug.level = -1, 
     predict.method = c("RK", "KED"), nfold = 5, verbose = FALSE, 
     nsim = 0, mask.extra = TRUE, block, 
     zmin = -Inf, zmax = Inf, subsample = length(object@sp), 
     coarsening.factor = 1, vgmmodel = object@vgmModel,
     subset.observations = !is.na(object@sp@coords[,1]), betas = c(0,1), extend = .5, ...)
## S4 method for signature 'list'
predict(object, 
     predictionLocations, nmin = 10, nmax = 30, debug.level = -1, 
     predict.method = c("RK", "KED"), nfold = 5, verbose = FALSE, 
     nsim = 0, mask.extra = TRUE, block, 
     zmin = -Inf, zmax = Inf, subsample = length(object@sp), ...)

Arguments

object

object of type "gstatModel"

predictionLocations

object of type "SpatialPixelsDataFrame" prediction locations (must contain all covariates from the model)

nmin

integer; minimum number of nearest observations sent to gstat::krige

nmax

integer; maximum number of nearest observations sent to gstat::krige

debug.level

integer; default debug level mode sent to gstat::krige

predict.method

character; mathematical implementation of the gstat::krige interpolation method with covariates: Regression-Kriging (RK) or Kriging with External Drift (KED)

nfold

integer; n-fold cross validation sent to gstat::krige.cv

verbose

logical; specifies whether to supress the progress bar of the gstat::krige.cv

nsim

integer; triggers the geostatistical simulations

mask.extra

logical; specifies whether to mask out the extrapolation pixels (prediction variance exceeding the global variance)

block

numeric; support size (block support for objects of type "SpatialPixelsDataFrame" is chosen by default)

zmin

numeric; lower physical limit for the target variable

zmax

numeric; upper physical limit for the target variable

subsample

integer; sub-sample point observations to speed up the processing

coarsening.factor

integer; coarsening factor (1:5) to speed up the processing

vgmmodel

object of class data.frame corresponding to the gstat::vgm variogram

subset.observations

logical; vector specifying the subset of observations used for interpolation

extend

numeric; fraction of the range for which the spatial domain should be extended when searching for observations for kriging

betas

numeric; vector of the beta coefficients to be passed to the gstat::krige

...

other optional arguments that can be passed to gstat::krige and/or predict.glm

Details

Selecting predict.method = "KED" invokes simple kriging with external drift with betas set at 0 (intercept) and 1 (regression predictions used as the only covariate). This assumes that the regression model already results in an unbiased estimator of the trend model.
If not speficied otherwise, subset.observations by default selects only obserations within the spatial domain (bounding box) of the predictionLocations plus 50% of the one third of the extent of the area (extend). In the case of spatial duplicates in 2D or 3D, subset.observations will automatically remove all duplicates before running kriging. All points in 3D that stand exactly above each other will be removed by default.
Predictions can be speed up by using a larger coarsening.factor e.g. 2 to 5, in which case the ordinary kriging on residuals will run at a coarser resolution, and the output would be then downscaled to the original resolution using splines (via the warp method). In the case of predict.method = RK, the kriging variance is derived as a sum of the GLM variance and the OK variance, which is statistically sub-optimal.

Note

Predictions using predict.method = "KED" (the default gstat setting) can be time consuming for large data set and can result in instabilities (singular matrix problems) if the search radius is small and/or if all covariates contain exactly the same values. Predictions using predict.method = "RK" on the other hand can be speed up, but will typically underestimate the prediction variance (taken as a simple sum of the regression and ordinary kriging variances). Compare to the "KED" variance that includes also a cross-term (see Hengl et al. 2007 for more details).

Author(s)

Tomislav Hengl, Gerard B.M. Heuvelink and Bas Kempen

References

See Also

gstatModel-class, fit.gstatModel


GSIF documentation built on May 31, 2017, 4:57 a.m.