okpred: Generate spatial predictions using ordinary kriging (OK)

In spm: Spatial Predictive Modeling

Generate spatial predictions using ordinary kriging (OK)

Description

This function is to make spatial predictions using ordinary kriging.

Usage

okpred(
  longlat,
  trainy,
  longlat2,
  nmax = 12,
  transformation = "none",
  delta = 1,
  vgm.args = ("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  ...
)

Arguments

longlat	a dataframe contains longitude and latitude of point samples.
trainy	a vector of response, must have length equal to the number of rows in longlat.
longlat2	a dataframe contains longitude and latitude of point locations (i.e., the centres of grids) to be predicted.
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, 12 observations are used.
transformation	transform the response variable to normalise the data; can be "sqrt" for square root, "arcsine" for arcsine, "log" or "none" for non transformation. By default, "none" is used.
delta	numeric; to avoid log(0) in the log transformation.
vgm.args	arguments for vgm, e.g. variogram model of response variable and anisotropy parameters. see notes vgm in gstat for details. By default, "Sph" is used.
anis	anisotropy parameters: see notes vgm in gstat for details.
alpha	direction in plane (x,y). see variogram in gstat for details.
block	block size. see krige in gstat for details.
...	other arguments passed on to gstat.

Value

A dataframe of longitude, latitude, predictions and variances.

Author(s)

Jin Li

References

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

Examples

## Not run: 
library(sp)
data(swmud)
data(sw)
okpred1 <- okpred(swmud[, c(1,2)], swmud[, 3], sw, nmax = 7, transformation =
"arcsine", vgm.args = ("Sph"))
names(okpred1)

## End(Not run)

spm documentation built on May 6, 2022, 9:06 a.m.