我的目标是使用克里格方法缩小回归模型的残差。
我使用R中的randomForest包来执行回归。我的数据集由一个因变量和一个自变量组成（以栅格格式）。当我导出随机森林（RF）回归的残差时，输出是一个堆叠栅格（500个栅格，作为树的数量）。
我的问题是，我可以像执行简单线性回归那样导出残差的单个栅格吗？
我正在使用的代码和数据集：

library(randomForest)
library(terra)
library(raster)

wd = "path/"

ntl = rast(paste0(wd, "ntl.tif"))
covar = rast(paste0(wd, "agbh.tif"))

covar = resample(covar, ntl, method = "bilinear")

s = c(ntl, covar)
names(s) = c("ntl", "covar")

m <- randomForest(ntl ~ ., 
                  data = as.data.frame(s), 
                  mtry = 1, 
                  importance = TRUE, 
                  na.action = na.omit)

# extract residuals
rsds = s$ntl - predict(m)

数据集：

ntl = rast(ncols=113, nrows=56, nlyrs=1, xmin=4498800, xmax=4544000, ymin=4292800, ymax=4315200, names=c('ntl'), crs='PROJCRS[\"World_Mollweide\",BASEGEOGCRS[\"WGS 84\",DATUM[\"World Geodetic System 1984\",ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ID[\"EPSG\",6326]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"Degree\",0.0174532925199433]]],CONVERSION[\"unnamed\",METHOD[\"Mollweide\"],PARAMETER[\"Longitude of natural origin\",0,ANGLEUNIT[\"Degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"False easting\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1,ID[\"EPSG\",9001]]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1,ID[\"EPSG\",9001]]]]')

covar = rast(ncols=452, nrows=225, nlyrs=1, xmin=4498900, xmax=4544100, ymin=4292600, ymax=4315100, names=c('agbh'), crs='PROJCRS[\"World_Mollweide\",BASEGEOGCRS[\"WGS 84\",DATUM[\"World Geodetic System 1984\",ELLIPSOID[\"WGS 84\",6378137,298.257223563,LENGTHUNIT[\"metre\",1]],ID[\"EPSG\",6326]],PRIMEM[\"Greenwich\",0,ANGLEUNIT[\"Degree\",0.0174532925199433]]],CONVERSION[\"unnamed\",METHOD[\"Mollweide\"],PARAMETER[\"Longitude of natural origin\",0,ANGLEUNIT[\"Degree\",0.0174532925199433],ID[\"EPSG\",8802]],PARAMETER[\"False easting\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8806]],PARAMETER[\"False northing\",0,LENGTHUNIT[\"metre\",1],ID[\"EPSG\",8807]]],CS[Cartesian,2],AXIS[\"(E)\",east,ORDER[1],LENGTHUNIT[\"metre\",1,ID[\"EPSG\",9001]]],AXIS[\"(N)\",north,ORDER[2],LENGTHUNIT[\"metre\",1,ID[\"EPSG\",9001]]]]')