lgm: Linear Geostatistical Models

In geostatsp: Geostatistical Modelling with Likelihood and Bayes

Linear Geostatistical Models

Description

Calculate MLE's of model parameters and perform spatial prediction.

Usage

## S4 method for signature 'missing,ANY,ANY,ANY'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'numeric,ANY,ANY,ANY'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'character,ANY,ANY,ANY'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,SpatVector,numeric,ANY'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,SpatVector,SpatRaster,missing'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,SpatVector,SpatRaster,list'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,SpatVector,SpatRaster,SpatRaster'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,SpatVector,SpatRaster,data.frame'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,SpatRaster,ANY,ANY'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 
## S4 method for signature 'formula,data.frame,SpatRaster,data.frame'
lgm(
formula, data, grid, covariates, 
buffer=0, shape=1, boxcox=1, nugget = 0, 
expPred=FALSE, nuggetInPrediction=TRUE,
reml=TRUE,mc.cores=1,
aniso=FALSE,
fixShape=TRUE,
fixBoxcox=TRUE,
fixNugget = FALSE,
...) 

Arguments

formula	A model formula for the fixed effects, or a character string specifying the response variable.
data	A SpatVector or SpatRaster layer, brick or stack containing the locations and observations, and possibly covariates.
grid	Either a SpatRaster, or a single integer giving the number of cells in the X direction which predictions will be made on. If the later the predictions will be a raster of square cells covering the bounding box of data.
covariates	The spatial covariates used in prediction, either a SpatRaster stack or list of rasters. Covariates in formula but not in data will be extracted from covariates.
shape	Order of the Matern correlation
boxcox	Box-Cox transformation parameter (or vector of parameters), set to 1 for no transformation.
nugget	Value for the nugget effect (observation error) variance, or vector of such values.
expPred	Should the predictions be exponentiated, defaults to FALSE.
nuggetInPrediction	If TRUE, predict new observations by adding the nugget effect. The prediction variances will be adjusted accordingly, and the predictions on the natural scale for logged or Box Cox transformed data will be affected. Otherwise predict fitted values.
reml	If TRUE (the default), use restricted maximum likelihood.
mc.cores	If mc.cores>1, this argument is passed to mclapply and computations are done in parallel where possible.
aniso	Set to TRUE to use geometric anisotropy.
fixShape	Set to FALSE to estimate the Matern order
fixBoxcox	Set to FALSE to estimate the Box-Cox parameter.
fixNugget	Set to FALSE to estimate the nugget effect parameter.
buffer	Extra distance to add around grid.
...	Additional arguments passed to likfitLgm. Starting values can be specified with a vector param of named elements

Details

When data is a SpatVector, parameters are estimated using optim to maximize the log-likelihood function computed by likfitLgm and spatial prediction accomplished with krigeLgm.

With data being a Raster object, a Markov Random Field approximation to the Matern is used (experimental). Parameters to be estimated should be provided as vectors of possible values, with optimization only considering the parameter values supplied.

Value

A list is returned which includes a SpatRaster named predict having layers:

fixed	Estimated means from the fixed effects portion of the model
random	Predicted random effect
krigeSd	Conditional standard deviation of predicted random effect (on the transformed scale if applicable)
predict	Prediction of the response, sum of predicted fixed and random effects. For Box-Cox or log-transformed data on the natural (untransformed) scale.
predict.log	If exp.pred=TRUE, the prediction of the logged process.
predict.boxcox	If a box cox transformation was used, the prediction of the process on the transformed scale.

In addition, the element summery contains a table of parameter estimates and confidence intervals. optim contains the output from the call to the optim function.

See Also

likfitLgm, krigeLgm

Examples

data("swissRain")
swissRain = unwrap(swissRain)
swissAltitude = unwrap(swissAltitude)
swissBorder = unwrap(swissBorder)
 
swissRes =  lgm( formula="rain", 
	data=swissRain[1:60,], grid=20,
	covariates=swissAltitude, boxcox=0.5, fixBoxcox=TRUE, 
	shape=1, fixShape=TRUE,
	aniso=FALSE, nugget=0, fixNugget=FALSE,
	nuggetInPrediction=FALSE
)

swissRes$summary

plot(swissRes$predict[["predict"]], main="predicted rain") 
plot(swissBorder, add=TRUE)

geostatsp documentation built on Sept. 23, 2023, 1:06 a.m.