raster (version 1.8-9)

interpolate: Interpolate

Description

Make a RasterLayer with interpolated values based on a a fitted model object of classes such as 'gstat' or 'Krige'. I.e. these are models that have 'x' and 'y' as independent variables. If x and y are the only independent variables provide an empty (no associated data in memory or on file) RasterLayer for which you want predictions. If there are more spatial predictor variables provide these as a Raster* object in the first argument of the function. If you do not have x and y locations as implicit predictors in your model you should use predict instead.

Usage

interpolate(object, ...)

Arguments

object

a Raster* object

...

Additional arguments. See below, under Methods

Value

• a RasterLayer object

Methods

A full call to the method is predict(object, model, filename='', fun=predict, xyOnly=TRUE, ext=NULL, const=NULL, index=1, na.rm=TRUE, ...) rll{ object Raster* object model Fitted model object filename Output filename for a new raster; if NA the result is not written to a file but returned with the RasterLayer object, in the data slot fun Function. Default value is 'predict', but can be replaced with e.g. 'predict.se' (depending on the class of the model) xyOnly Logical. If TRUE, values of the Raster* object are not considered as co-variables; and only x and y (longitude and latitude) are used. This should match the model ext An Extent object to limit the prediction to a sub-region of x const data.frame. Can be used to add a constant for which there is no Raster object for model predictions. Particulalry useful if the constant is a character-like factor value index Integer. To select the column if 'predict.model' returns a matrix with multiple columns na.rm Logical. Remove cells with NA values in the predictors before solving the model (and return a NA value for those cells). In most cases this will not affect the output. This option prevents errors with models that cannot handle NA values. ... Additional arguments, see below } The following additional arguments can be passed, to replace default values rll{ format Character. Output file type. See writeRaster datatype Character. Output data type. See dataType overwrite Logical. If TRUE, "filename" will be overwritten if it exists progress Character. "text", "window", or "" (the default, no progress bar) }

See Also

predict, predict.gstat, Tps

Examples

## Thin plate spline interpolation with x and y only
library(fields) 
r <- raster(system.file("external/test.grd", package="raster"))
ra <- aggregate(r, 10)
xy <- data.frame(xyFromCell(ra, 1:ncell(ra)))
v <- getValues(ra)
tps <- Tps(xy, v)
p <- raster(r)
p <- interpolate(p, tps)
p <- mask(p, r)
plot(p)
se <- interpolate(p, tps, fun=predict.se)
se <- mask(se, r)
plot(se)


##gstat examples
library(gstat)
## inverse distance weighted interpolation with gstat
r <- raster(system.file("external/test.grd", package="raster"))
data(meuse)
mg <- gstat(id = "zinc", formula = zinc~1, locations = ~x+y, data=meuse, nmax=7, set=list(idp = .5))
z <- interpolate(r, mg)
z <- mask(z, r)

## kriging
coordinates(meuse) = ~x+y
v <- variogram(log(zinc)~1, meuse)
m <- fit.variogram(v, vgm(1, "Sph", 300, 1))
g <- gstat(NULL, "log.zinc", log(zinc)~1, meuse, model = m)
projection(r) <- projection(meuse)
x <- interpolate(r, g)