bestMatch.RdDetermine for each grid cell which reference it is most similar to. A reference consists of a SpatVector with reference locations, or a data.frame or matrix in which each column matches a layer name in the SpatRaster.
Similarity is computed with the mean absolute or the mean squared differences between the cell and the reference, or with an alternative function you provide. It may be important to first scale the input.
# S4 method for class 'SpatRaster,SpatVector'
bestMatch(x, y, labels=NULL, fun="squared", ...,
filename="", overwrite=FALSE, wopt=list())
# S4 method for class 'SpatRaster,data.frame'
bestMatch(x, y, labels=NULL, fun="squared", ...,
filename="", overwrite=FALSE, wopt=list())
# S4 method for class 'SpatRaster,matrix'
bestMatch(x, y, labels=NULL, fun="squared", ...,
filename="", overwrite=FALSE, wopt=list())SpatRaster
SpatVector, data.frame or matrix
character. labels that correspond to each class (row in y
character. One of "abs" for the mean absolute difference, or "squared" for the mean squared difference. Or a true function like terra:::match_sqr
additional arguments passed to fun. For the built-in functions this can be na.rm=TRUE
character. Output filename
logical. If TRUE, filename is overwritten
additional arguments for writing files as in writeRaster
SpatRaster
f <- system.file("ex/logo.tif", package = "terra")
r <- rast(f)
# locations of interest
pts <- vect(cbind(c(25.25, 34.324, 43.003), c(54.577, 46.489, 30.905)))
pts$code <- LETTERS[1:3]
plot(r)
points(pts, pch=20, cex=2, col="red")
text(pts, "code", pos=4, halo=TRUE)
x <- scale(r)
s1 <- bestMatch(x, pts, labels=pts$code)
plot(s1)
# same result
e <- extract(x, pts, ID=FALSE)
s2 <- bestMatch(x, e, labels=c("Ap", "Nt", "Ms"))