Description Usage Arguments Value Note Author(s) Examples
Combines the stats::prcomp
method and predicts a list principal components for an object of type "SpatialPixelsDataFrame"
.
1 2 |
obj |
SpatialPixelsDataFrame. |
formulaString |
optional model definition. |
scale. |
scale all numbers. |
silent |
silent output. |
Object of class SpatialComponents
. List of grids with generic names PC1
,...,PCp
, where p
is the total number of input grids.
This method assumes that the input covariates are cross-correlated and hence their overlap can be reduced. The input variables are scaled by default and the missing values will be replaced with 0 values to reduce loss of data due to missing pixels.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | if(requireNamespace("plotKML", quietly = TRUE)){
library(sp)
library(plotKML)
pal = rev(rainbow(65)[1:48])
data(eberg_grid)
gridded(eberg_grid) <- ~x+y
proj4string(eberg_grid) <- CRS("+init=epsg:31467")
formulaString <- ~ PRMGEO6+DEMSRT6+TWISRT6+TIRAST6
eberg_spc <- spc(eberg_grid, formulaString)
names(eberg_spc@predicted) # 11 components on the end;
## plot maps:
rd = range(eberg_spc@predicted@data[,1], na.rm=TRUE)
sq = seq(rd[1], rd[2], length.out=48)
spplot(eberg_spc@predicted[1:4], at=sq, col.regions=pal)
}
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