glskrigeidwpred {spm2}

Generate spatial predictions using the hybrid methods of generalised least squares ('gls'), 'kriging' and inverse distance weighted ('IDW')

Description

This function is for generating spatial predictions using the hybrid methods of 'gls', 'kriging' and 'IDW', including all methods implemented in 'glskrigeidwcv'.

Usage

glskrigeidwpred(
  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,
  idp = 2,
  nmaxidw = 12,
  hybrid.parameter = 2,
  lambda = 1,
  ...
)

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 normalise the data for 'krige'; 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.

idp

a numeric number specifying the inverse distance weighting power.

nmaxidw

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.

hybrid.parameter

the default is 2 that is for 'glskrigeglsidw'; for 'glsglskrigeglsidw', it needs to be 3.

lambda

ranging from 0 to 2; the default is 1 for 'glskrigeglsidw' and 'glsglskrigeglsidw'; and if it is < 1, more weight is placed on 'krige', otherwise more weight is placed on 'idw'; and if it is 0, 'idw' is not considered and the resultant methods is 'glskrige' when the default 'hybrid.parameter' is used; and if it is 2, then the resultant method is 'glsidw' when the default 'hybrid.parameter' is used.

...

other arguments passed on to 'gls', 'krige' and 'gstat'.

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)

glskrigeidwpred1 <- glskrigeidwpred(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,
idp = 2, nmaxidw = 12,  corr.args = corSpher(c(range1, nugget1),
form = ~ lat + long, nugget = TRUE))

names(glskrigeidwpred1)

# Back transform 'glskrigeidwpred$predictions' to generate the final predictions
glskrigeidw.predictions <- exp(glskrigeidwpred1$predictions) - 1
range(glskrigeidw.predictions)