glskrigepred: Generate spatial predictions using the hybrid method of...

In spm2: Spatial Predictive Modeling

Generate spatial predictions using the hybrid method of generalized least squares ('gls') and kriging ('krige') ('glskrige')

Description

This function is for generating spatial predictions using the hybrid method of 'gls' and 'krige' (glskrige).

Usage

glskrigepred(
  model = var1 ~ 1,
  longlat,
  trainxy,
  predx,
  y,
  longlatpredx,
  corr.args = NULL,
  weights = NULL,
  transformation = "none",
  delta = 1,
  formula.krige = res1 ~ 1,
  vgm.args = c("Sph"),
  anis = c(0, 1),
  alpha = 0,
  block = 0,
  beta,
  nmaxkrige = 12,
  ...
)

Arguments

model	a formula defining the response variable and predictive variables.
longlat	a dataframe contains longitude and latitude of point samples.
trainxy	a dataframe contains longitude (long), latitude (lat), predictive variables and the response variable of point samples. That is, the location information must be names as 'long' and 'lat'.
predx	a dataframe or matrix contains columns of predictive variables for the grids to be predicted.
y	a vector of the response variable in the formula, that is, the left part of the formula.
longlatpredx	a dataframe contains longitude and latitude of point locations (i.e., the centers of grids) to be predicted. The location information must be named as 'long' and 'lat'.
corr.args	arguments for 'correlation' in 'gls'. See '?corClasses' in 'nlme' for details. By default, "NULL" is used. When "NULL" is used, then 'gls' is actually performing 'lm'.
weights	describing the within-group heteroscedasticity structure. Defaults to "NULL", corresponding to homoscedastic errors. See '?gls' in 'nlme' for details.
transformation	transform the residuals of 'gls' to normalize 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. The default is 1.
formula.krige	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 '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.
nmaxkrige	for a local predicting: the number of nearest observations that should be used for a prediction or simulation, where nearest is defined in terms of the space of the spatial locations. By default, 12 observations are used.
...	other arguments passed on to 'gls' and 'krige'.

Value

A dataframe of longitude, latitude, and predictions.

Author(s)

Jin Li

References

Pinheiro, J. C. and D. M. Bates (2000). Mixed-Effects Models in S and S-PLUS. New York, Springer.

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

Examples

library(spm)
library(nlme)

data(petrel)
data(petrel.grid)

gravel <- petrel[, c(1, 2, 6:9, 5)]
longlat <- petrel[, c(1, 2)]
range1 <- 0.8
nugget1 <- 0.5
model <- log(gravel + 1) ~  long + lat +  bathy + dist + I(long^2) + I(lat^2) +
I(lat^3) + I(bathy^2) + I(bathy^3) + I(dist^2) + I(dist^3) + I(relief^2) + I(relief^3)
y <- log(gravel[, 7] +1)

glskrigepred1 <- glskrigepred(model = model, longlat = longlat, trainxy = gravel,
predx = petrel.grid, y = y, longlatpredx = petrel.grid[, c(1:2)],
transformation = "none", formula.krige = res1 ~ 1,
vgm.args = "Sph", nmaxkrige = 12, corr.args = corSpher(c(range1, nugget1),
form = ~ lat + long, nugget = TRUE))

names(glskrigepred1)

# Back transform 'glskrigepred$predictions' to generate the final predictions
glskrige.predictions <- exp(glskrigepred1$predictions) - 1
range(glskrige.predictions)

spm2 documentation built on April 6, 2023, 5:19 p.m.