np.kriging: Nonparametric (residual) kriging

In rubenfcasal/npsp: Nonparametric Spatial Statistics

Nonparametric (residual) kriging

Description

Compute simple kriging or residual kriging predictions (and also the corresponding simple kriging standard errors ). Currently, only global (residual) simple kriging is implemented.

Usage

np.kriging(object, ...)

## Default S3 method:
np.kriging(
  object,
  svm,
  lp.resid = NULL,
  ngrid = object$grid$n,
  intermediate = FALSE,
  ...
)

## S3 method for class 'np.geo'
np.kriging(object, ngrid = object$grid$n, intermediate = FALSE, ...)

kriging.simple(x, y, newx, svm, intermediate = FALSE)

Arguments

object	object used to select a method: local polynomial estimate of the trend (class locpol.bin) or nonparametric geostatistical model (class extending np.geo).
...	further arguments passed to or from other methods.
svm	semivariogram model (of class extending svarmod).
lp.resid	residuals (defaults to residuals(object)).
ngrid	number of grid nodes in each dimension.
intermediate	logical, determines whether the intermediate computations are included in the output (component kriging; see Value). These calculations can be reused, e.g. for bootstrap.
x	vector/matrix with data locations (each component/row is an observation location).
y	vector of data (response variable).
newx	vector/matrix with the (irregular) locations to predict (each component/row is a prediction location). or an object extending grid.par-class (data.grid).

Value

np.kriging(), and kriging.simple() when newx defines gridded data (extends grid.par or data.grid classes), returns an S3 object of class krig.grid (kriging results + grid par.). A data.grid object with the additional (some optional) components:

kpred	vector or array (dimension $grid$n) with the kriging predictions.
ksd	vector or array with the kriging standard deviations.
kriging	(if requested) a list with 4 components: lambda matrix of kriging weights (columns correspond with predictions and rows with data)). cov.est (estimated) covariance matrix of the data. chol Cholesky factorization of cov.est. cov.pred matrix of (estimated) covariances between data (rows) and predictions (columns).

When newx is a matrix of coordinates (where each row is a prediction location), kriging.simple() returns a list with the previous components (kpred, ksd and, if requested, kriging).

See Also

np.fitgeo, locpol, np.svar.

Examples

geomod <- np.fitgeo(aquifer[,1:2], aquifer$head)
krig.grid <- np.kriging(geomod, ngrid = c(96, 96)) # 9216 locations
old.par <- par(mfrow = c(1,2))
simage(krig.grid, 'kpred', main = 'Kriging predictions', 
       xlab = "Longitude", ylab = "Latitude", reset = FALSE )
simage(krig.grid, 'ksd', main = 'Kriging sd', xlab = "Longitude", 
       ylab = "Latitude" , col = hot.colors(256), reset = FALSE)
par(old.par)

rubenfcasal/npsp documentation built on Nov. 13, 2024, 4:48 p.m.