spm2 (version 1.1.3)

glspred: Generate spatial predictions using generalized least squares ('gls')

Description

This function is for generating spatial predictions using 'gls' method in 'nlme' package.

Usage

glspred(
  model = var1 ~ 1,
  trainxy,
  longlatpredx,
  predx,
  corr.args = NULL,
  weights = NULL,
  ...
)

Value

A dataframe of longitude, latitude and predictions.

Arguments

model

a formula defining the response variable and predictive variables.

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'.

longlatpredx

a dataframe contains longitude and latitude of point locations (i.e., the centers of grids) to be predicted, need to 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.

...

other arguments passed on to 'gls'.

Author

Jin Li

References

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

Examples

# \donttest{
library(spm)
library(nlme)

data(petrel)
data(petrel.grid)

gravel <- petrel[, c(1, 2, 6:9, 5)]
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)

glspred1 <- glspred(model = model, trainxy = gravel,
 longlatpredx = petrel.grid[, c(1:2)], predx = petrel.grid,
 corr.args = corSpher(c(range1, nugget1), form = ~ lat + long, nugget = TRUE))

names(glspred1)

# Back transform 'glspred1$predictions' to generate the final predictions
gls.predictions <- exp(glspred1$predictions) - 1
range(gls.predictions)
# }