krigeLgm | R Documentation |

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

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

`formula` |
Either a model formula, or a data frame of linear covariates. |

`data` |
A |

`grid` |
Either a |

`covariates` |
The spatial covariates used in prediction, either a |

`param` |
A vector of named model parameters, as produced by |

`expPred` |
Should the predictions be exponentiated, defaults to |

`nuggetInPrediction` |
If |

`mc.cores` |
passed to |

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

A raster 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 |

`lgm`

```
data('swissRain')
swissAltitude = unwrap(swissAltitude)
swissRain = unwrap(swissRain)
swissRain$lograin = log(swissRain$rain)
swissRain[[names(swissAltitude)]] = extract(swissAltitude, swissRain, ID=FALSE)
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
swissBorder = unwrap(swissBorder)
swissKrige = krigeLgm(
data=swissRain,
formula = myTrend,
covariates = swissAltitude,
param=myParams,
grid = squareRaster(swissBorder, 40), expPred=TRUE)
plot(swissKrige[["predict"]], main="predicted rain")
plot(swissBorder, add=TRUE)
```

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