I am trying to check the results of IDW interpolation by leave-one-out cross validation and then get the RMSE to see the quality of the prediction.
From github Interpolation in R, I found some hints and apply it in my case as following:
I have 63 locations which is saved as a SpatialPointDataFrame, named x_full_utm_2001. For each location, there is attached precipitation data, named sumdata_2001.
idw.out<- vector(length = length(sumdata_2001$Jan))
for (i in 1:length(sumdata_2001$Jan)) {
idw.out[i]<-idw(sumdata_2001$Jan~1, x_full_2001_utm[-i, ], x_full_2001_utm[i, ])$var1.pred
}
But I don't know why there is always error warning me as follows:
dimensions do not match: locations 124 and data 63
I'm wondering why it works out like this. How should I revise this?
