krigeLgm {geostatsp} | R Documentation

## Spatial prediction, or Kriging

### Description

Perform spatial prediction, producing a raster of predictions and conditional standard deviations.

### Usage

    krigeLgm(formula, data, grid,  covariates = NULL,
    	param,
        expPred = FALSE, nuggetInPrediction = TRUE,
        mc.cores=getOption("mc.cores", 1L))

### Arguments

`formula` | Either a model formula, or a data frame of linear covariates.
`data` | A SpatialPointsDataFrame containing the data to be interpolated
`grid` | 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 bounding box of `data`.
`covariates` | The spatial covariates used in prediction, either a `raster` stack or list of rasters.
`param` | A vector of named model parameters, as produced by `likfitLgm`
`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.
`mc.cores` | passed to `mcmapply` if greater than 1.

### Details

Given the model parameters and observed data, conditional means and variances of the spatial random field are computed.

### Value

A raster stack is returned with the following layers:

`fixed` | Estimated means from the fixed effects portion of the model
`random` | Predicted random effect
`krige.var` | Conditional variance of predicted random effect (on the transformed scale if applicable)
`predict` | Prediction of the response, sum of fixed and random effects. If exp.pred is TRUE, gives predictions on the exponentiated scale, and half of krige.var is added prior to exponentiating
`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.

If the prediction locations are different for fixed and random effects (typically coarser for the random effects), a list with two raster stacks is returned.

`prediction` | A raster stack as above, though the random effect prediction is resampled to the same locations as the fixed effects.
`random` | the predictions and conditional variance of the random effects, on the same raster as `newdata`

### See Also

`lgm`

### Examples

    data('swissRain')
    swissRain$lograin = log(swissRain$rain)
    swissRain[[names(swissAltitude)]] = extract(swissAltitude, swissRain)

    if(interactive()  | Sys.info()['user'] =='patrick') {
    swissFit = likfitLgm(data=swissRain,
    			formula=lograin~ CHE_alt,
    			param=c(range=46500, nugget=0.05,shape=1,
    					anisoAngleDegrees=35, anisoRatio=12),
    			paramToEstimate = c("range","nugget",
    				"anisoAngleDegrees", "anisoRatio")
    )
    myTrend = swissFit$model$formula
    myParams = swissFit$param
    dput(myParams)
    # will give the following
    } else {
    myParams=structure(c(0.0951770829449953, 0.77308786208928, 49379.3845752436,
    1, 11.673076577513, 0.649925237653982, 1, 2.26103881494066, 0.000146945995279231,
    37.2379731166102), .Names = c("nugget", "variance", "range",
    "shape", "anisoRatio", "anisoAngleRadians", "boxcox", "(Intercept)",
    "CHE_alt", "anisoAngleDegrees"))
    myTrend =lograin~ CHE_alt
    }

    # make sure krige can cope with missing values!
    swissAltitude[1:50,1:50] = NA
    swissKrige = krigeLgm(data=swissRain,
    	formula = myTrend,
    	covariates = swissAltitude,
    	param=myParams,
    	grid = 40, expPred=TRUE)

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

    # now with box cox and provide a raster for prediction, no covariates

    if(interactive()  | Sys.info()['user'] =='patrick') {
    swissFit2 = likfitLgm(
    	data=swissRain,
    			formula=rain~1,
    			param=c(range=52000, nugget=0.1,
    			shape=1, boxcox=0.5,
    					anisoAngleDegrees=35, anisoRatio=8),
    			paramToEstimate = c("range","nugget",
    				"anisoAngleDegrees", "anisoRatio"),
    			parscale = c(range=5000,nugget=0.01,
    				anisoRatio=1,anisoAngleDegrees=5)
    )
    myTrend2 = swissFit2$trend
    myParams2 = swissFit2$param
    dput(myParams2)
    } else {
    myParams2=structure(c(0.865530531647866, 8.76993204385615, 54143.5826959284,
    1, 7.36559089705556, 0.647158492167979, 0.5, 5.16254700135706,
    37.0794502772753), .Names = c("nugget", "variance", "range",
    "shape", "anisoRatio", "anisoAngleRadians", "boxcox", "(Intercept)",
    "anisoAngleDegrees"))
    myTrend2=rain~1
    }

    swissRaster = raster(extent(swissBorder), nrows=25, ncols=40)

    swissKrige2 = krigeLgm(data=swissRain, formula = myTrend2,
    	  param=myParams2,
    	grid = swissRaster)

    plot(swissKrige2[["predict"]], main="predicted rain with box-cox")
    plot(swissBorder, add=TRUE)

[Package geostatsp version 1.7.8 Index]