GSIF (version 0.3-1)

gstatModel-class: A class for a geostatistical model

Description

A class containing fitted parameters of a geostatistical model to be used to run predictions by regression-kriging. It comprises regression model (e.g. GLM), variogram model, and observation locations of sampled values used to fit the model.

Details

Any model passed to the regModel slot must come with generic functions such as residuals, fitted.values, summary and predict.

See Also

predict.gstatModel, plotKML::SpatialPredictions-class, plotKML::RasterBrickSimulations-class, gstat::gstat, stats::glm

Examples

## load observations:
library(plotKML)
data(meuse)
coordinates(meuse) <- ~x+y
proj4string(meuse) <- CRS("+init=epsg:28992")
## load grids:
data(meuse.grid)
coordinates(meuse.grid) <- ~x+y
gridded(meuse.grid) <- TRUE
proj4string(meuse.grid) <- CRS("+init=epsg:28992")
## fit a model:
omm <- fit.gstatModel(meuse, om~dist+ffreq, family=gaussian(link="log"), meuse.grid)
show(omm@regModel)
## produce SpatialPredictions:
om.rk <- predict(omm, predictionLocations = meuse.grid)
#plotKML(om.rk)
## run a proper cross-validation:
rk.cv <- validate(omm)
## RMSE:
sqrt(mean((rk.cv$validation$var1.pred-rk.cv$validation$observed)^2))