I want to perform IDW interpolation using R, and more specifically, using the "idw" command from the gstat package. Also, I want to use the optimal number of neighbors (point locations) and distance power, which will be determined by what combination of idp and nmax produces the lowest RMSE in leave-one-out-cross validation. For this reason, after each run of IDW with different "nmax" and "idp" parameters, I perform the leave-one-out cross validation. In order to do this, I have written a double for loop in R that tests each idp parameter for each number of nmax neighbors, but my script can't produce the result I want.

`power = seq(from = 1, to = 1.6, by = 0.1) neigh = seq(from = 4, to = 5, by = 1)

January

for(i in power) { for(j in 1:length(neigh) jan_idw = idw(Jan ~ 1, st, grd, nmax = power, idp = neigh) list_jan_idw = c(list_jan_idw,jan_idw ) krige.cv(Jan ~ 1, st, nfold = 52) } }`

For each run, I want the results to be stored in a data frame or matrix.

I want to perform IDW interpolation using R, and more specifically, using the idw command from the gstat package. Also, I want to use the optimal number of neighbors (point locations) and distance power, which will be determined by what combination of "idp" and "nmax" produces the lowest RMSE in leave-one-out-cross validation. For this reason, after each run of IDW with different "nmax" and "idp" parameters, I perform the leave-one-out cross validation. In order to do this, I have written a double for loop in R that tests each "idp" parameter for each number of "nmax" neighbors, but my script can't produce the result I want.

power = seq(from = 1, to = 1.6, by = 0.1)
neigh = seq(from = 4, to = 5, by = 1)
# January
for(i in power) {
  for(j in 1:length(neigh)
    jan_idw = idw(Jan ~ 1, st, grd, nmax = power, idp = neigh)
    list_jan_idw = c(list_jan_idw,jan_idw )
    krige.cv(Jan ~ 1, st, nfold = 52)
  }
} 

For each run, I want the results to be stored in a data frame or matrix.