如何对块克里金法进行交叉验证？ [英] how to do cross-validation for block kriging?

问题描述

我已经在自动映射程序包中编写了代码，以交叉验证不同的克里金法技术。我已经对它们全部进行了交叉验证，但是我无法编写用于块克里金法的代码。它显示了此错误：

I have written a code in automap package to cross-validate different kriging techniques. I have cross-validated all of them, but I cannot write the code for Block kriging. It shows this error:

未使用的参数（block = c（400,400））

unused argument (block=c(400,400))

library(automap)
mydata<-read.table(".../mydata.txt",header=T,sep=",")
colnames(mydata)=c("x","y","data1")
library(gstat)
coordinates(mydata)=~x+y
mygrids<-read.table(".../grids.txt",header=T,sep=",")
gridded(mygrids)=~x+y
block_kriging_cv<-autoKrige.cv(log(data1)~x+y, mydata, mygrids, block=c(400,400), model=c("Ste"), nfold=10)

现在，请告诉我如何解决。解决此问题后的最后一步将是所有cv的compare.cv，对吗？

Now, please let me know how I can fix it. The final step after fixing this will be compare.cv of all cv s, am I right?

推荐答案

这意味着autoKrige .cv命令不考虑您的数据块。它逐点而不是按块执行交叉验证。

This means that the autoKrige.cv command does not take in account the blocks of your data. It performs the cross-validation point-by-point and not by blocks.

交叉验证考虑了POINTS插值（或预测）估计的准确性。块克里金法是一种平滑方法，可以将整个区域划分为几个块，并针对每个区域计算估计值的局部平均值。换句话说，对于障碍区域，您没有值来比较通过克里金法所做的估算

Cross validation takes in account the accuracy of the estimates of the interpolation (or prediction) for POINTS while block kriging is a smoothing method that divides the whole area into several BLOCKS and calculate the local average of your estimations for each of those area. In other words, for the area 'block' you don't have a 'value' to compare your estimation made by kriging

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