lgm: Linear Geostatistical Models

Description

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

Usage

lgm(data, locations, covariates = NULL, formula = NULL, shape = 1, 
    fixShape = TRUE, aniso = FALSE, boxcox = 1, fixBoxcox = TRUE, 
    nugget = 0, fixNugget = FALSE, expPred = FALSE, nuggetInPrediction = TRUE,
    ...)

Arguments

data

A SpatialPointsDataFrame containing the data to be interpolated, or a vector of observed data

covariates

The spatial covariates used in prediction, either a raster stack or list of rasters. Covariates in formula but not in data will be extracted from covariates.

locations

Either a raster, 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 boundin

formula

A model formula for the fixed effects.

shape

Order of the Matern correlation

fixShape

Set to FALSE to estimate the Matern order

aniso

Set to TRUE to use geometric anisotropy.

boxcox

Box-Cox transformation parameter, set to 1 for no transformation.

fixBoxcox

Set to FALSE to estimate the Box-Cox parameter.

nugget

Value for the nugget effect (observation error

fixNugget

Set to FALSE to estimate the nugget effect parameter.

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 fitt

...

Additional arguments passed to likfitLgm. Starting values can be specified with a vector param of named elements

Value

A raster stack is returned with the following layers:
fixedEstimated means from the fixed effects portion of the model
randomPredicted random effect
krigeSdConditional standard deviation of predicted random effect (on the transformed scale if applicable)
predictPrediction of the response, sum of predicted fixed and random effects. For Box-Cox or log-transformed data on the natural (untransformed) scale.
predict.logIf exp.pred=TRUE, the prediction of the logged process.
predict.boxcoxIf a box cox transformation was used, the prediction of the process on the transformed scale.

Details

Calls likfitLgm and krige

Examples

Run this code

data("swissRain")
 
swissRes =  lgm(swissRain, locations=20, formula="rain",
	covariates=swissAltitude, boxcox=0.5, fixBoxcox=TRUE, 
	shape=1, fixShape=TRUE,
	aniso=FALSE, nugget=0, fixNugget=TRUE,
	nuggetInPrediction=FALSE
	)
	

swissRes$summary

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



load(url("http://www.filefactory.com/file/frd1mhownd9/n/CHE_adm0_RData"))

library(RColorBrewer)
par(mar=c(0,0,0,3))
plot(gadm) 
plot(mask(projectRaster(
	swissRes$predict[["predict"]],  crs=gadm@proj4string),gadm), 
add=T,alpha=0.6, col=brewer.pal(9, "Blues"))
plot(gadm, add=TRUE)