Josh O'Brien 发布的答案是好的,它帮助了我(作为起点),但这种方法太慢了,因为我有大量的数据列表。下面的方法是不错的选择。它使用fields
和工作得更快。
功能
rescale <- function(x, newrange=range(x)){
xrange <- range(x)
mfac <- (newrange[2]-newrange[1])/(xrange[2]-xrange[1])
newrange[1]+(x-xrange[1])*mfac
}
ResizeMat <- function(mat, ndim=dim(mat)){
if(!require(fields)) stop("`fields` required.")
# input object
odim <- dim(mat)
obj <- list(x= 1:odim[1], y=1:odim[2], z= mat)
# output object
ans <- matrix(NA, nrow=ndim[1], ncol=ndim[2])
ndim <- dim(ans)
# rescaling
ncord <- as.matrix(expand.grid(seq_len(ndim[1]), seq_len(ndim[2])))
loc <- ncord
loc[,1] = rescale(ncord[,1], c(1,odim[1]))
loc[,2] = rescale(ncord[,2], c(1,odim[2]))
# interpolation
ans[ncord] <- interp.surface(obj, loc)
ans
}
让我们看看它是如何工作的
## Original data (4x4)
rr <- matrix(1:16, ncol=4, nrow=4)
ss <- ResizeMat(rr, c(5,5))
tt <- ResizeMat(rr, c(3,3))
## Plot for comparison
par(mfcol=c(2,2), mar=c(1,1,2,1))
image(rr, main="original data", axes=FALSE)
image(ss, main="resampled to 5-by-5", axes=FALSE)
image(tt, main="resampled to 3-by-3", axes=FALSE)