Cross-validation the output of "scatteredInterpolant" in order to choose best method (linear, nearest, and natural)
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I had the value of precipitation in 93 scattered coordinate stations; I used "scatteredInterpolant" to interpolate this 93 scattered data in gridded coordinates.
[new_lons,new_lats] = ndgrid(44.25:.5:63.75,24.25:.5:39.75); %make the grid for my new lats/lons
After doing that I achieved 1280 gridded data. The exact coordinates of above mentioned 93 scattered data not included between these 1280. I mean I achieved new values on new coordinates.
Now I want to check R2 and RMSE of different methods that included in scatteredInterpolant (linear, natural, and nearest) to investigate which interpolation method was good for my data set.
I think I should using scattered interpolation again to interpolate these 1280 values on initial 93 scattered coordinates and check R2 and RMSE of values in first 93 original scattered vales and new interpolated 93 values that interpolated using scatteredinterpolant before.
So am I right?
Is there any better approach available?
I appreciate any suggestions.
Bjorn Gustavsson on 5 May 2020
To me that sound somewhat sensible, but would primarily check the regular-grid interpolation-method, and not the scatteredInterpolant-methods. My first idea would be to try a leave-one-out attack instead. If you leave one point out from your 93 you could still create the scatteredInterpolanting, then you have one test-point to compare that with an actual observation, then you can repeat and leave another point out (preferably not from the perimeter, I'd guess) to build some statistics.