spm2 (version 1.1.3)

glsidwpred: Generate spatial predictions using the hybrid method of generalized least squares ('gls') and inverse distance weighted ('IDW') ('glsidw')

Description

This function is for generating spatial predictions using the hybrid method of 'gls' and 'idw' ('glsidw') (see reference #1).

Usage

glsidwpred(
  model = var1 ~ 1,
  longlat,
  trainxy,
  y,
  longlatpredx,
  predx,
  corr.args = NULL,
  weights = NULL,
  idp = 2,
  nmaxidw = 12,
  ...
)

Value

A dataframe of longitude, latitude, and predictions.

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'.

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'.

predx

a dataframe or matrix contains columns of predictive variables for the grids to be predicted.

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.

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.

...

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

Author

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

# \donttest{
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)

glsidwpred1 <- glsidwpred(model = model, longlat = longlat, trainxy = gravel,
y = y, longlatpredx = petrel.grid[, c(1:2)], predx = petrel.grid,
 idp = 2, nmaxidw = 12, corr.args = corSpher(c(range1, nugget1),
 form = ~ lat + long, nugget = TRUE))

names(glsidwpred1)

# Back transform 'glsidwpred$predictions' to generate the final predictions
glsidw.predictions <- exp(glsidwpred1$predictions) - 1
range(glsidw.predictions)
# }