krigepred {spm2}

Generate spatial predictions using kriging methods ('krige')

Description

This function is to make spatial predictions using kriging methods ('krige').

Usage

krigepred(
  trainx,
  trainy,
  trainx2,
  nmax = 12,
  transformation = "none",
  delta = 1,
  formula = var1 ~ 1,
  vgm.args = ("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  beta,
  ...
)

Arguments

trainx

a dataframe contains longitude (long), latitude (lat) and predictive variables of point samples. The location information must be named as 'long' and 'lat'.

trainy

a vector of response, must have length equal to the number of rows in trainx.

trainx2

a dataframe contains longitude (long), latitude (lat) and predictive variables of point locations (i.e., the centres of grids) to be predicted. The location information must be named as 'long' and 'lat' and in the first two columns respectively..

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.

formula

formula defining the response vector and (possible) regressor. an object (i.e., 'variogram.formula') for 'variogram' or a formula for 'krige'. see 'variogram' and 'krige' in gstat for details.

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.

beta

for simple kriging. see krige in gstat for details.

...

other arguments passed on to gstat.

Value

A dataframe of longitude, latitude, predictions and variances.

Note

The variances in the output are not transformed back when a transformation is used. This is because kriging variances are not measuring the uncertainty of predictions but they are indicator of the spatial distribution of sample density. The variances are exported only for interested users; and if needed, they can be transformed back from the output.

Author(s)

Jin Li

References

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

Examples

library(sp)
library(spm)
data(swmud)
data(sw)
okpred1 <- krigepred(swmud[, c(1,2)], swmud[, 3], sw, nmax = 7, transformation =
"arcsine", vgm.args = ("Sph"))
names(okpred1)