lgm-methods {geostatsp} | R Documentation |
Calculate MLE's of model parameters and perform spatial prediction.
## 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,Spatial,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,Spatial,Raster,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,Spatial,Raster,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,Spatial,Raster,Raster' 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,Spatial,Raster,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,Raster,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,Raster,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, ...)
formula |
A model formula for the fixed effects, or a character string specifying the response variable. |
data |
A |
grid |
Either a |
covariates |
The spatial covariates used in prediction, either a |
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 |
nuggetInPrediction |
If |
reml |
If |
mc.cores |
If |
aniso |
Set to |
fixShape |
Set to |
fixBoxcox |
Set to |
fixNugget |
Set to |
buffer |
Extra distance to add around |
... |
Additional arguments passed to |
When data
is a SpatialPointsDataFrame
, 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.
A list is returned which includes a RasterStack
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 |
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.
data("swissRain") 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) ## Not run: 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) ## End(Not run)