How can I test my model fits well using K-Leave Out Cross Validation say K=2. That means take out first 2 data and fit the model for the rest 10 data to find parameters and use 2 data for finding error and do this 6 times for this case.
I also asked the question at mathematica.stackexchange.com here
Here is my data and model. I tried something but it does not work properly. Any suggestion? Thanks in advance.
data={{1.*10^-10,2.58022},{7.,2.66034},{7.33333,3.18999},{7.66667,3.88375},{8.,4.13342},{8.33333,4.21529},{8.66667,4.22846},{9.,4.2579},{9.33333,4.28118},{9.66667,4.28596},{10.,4.29661},{11.,4.31207}};
data = TakeDrop[data, {#, # + 1}] & /@ Range[1, 12, 2]
parameters = {\[Gamma], \[Epsilon], k, c, p, is};
f[x_] := (E^-k \[Gamma])/(1 + c/(1 + (x/is)^p) + E^-k) + \[Epsilon]
Do[model[j] = Sum[( f@(Last@data[[j]])[[i, 1]] - (Last@data[[j]])[[i, 2]])^2, {i, 1, 10}];
fit[j] = NMinimize[{model[j], \[Gamma] > 0 && c > 0 && p > 0 &&
0 < is <= 11}, parameters] // Chop;
Thread[{\[Gamma], \[Epsilon], k, c, p, is} = parameters /. Last@fit[j]];
err[j] = Total@Abs[
f@(First@data[[j]])[[All, 1]] - (First@data[[j]])[[All, 2]]];
Print[err[j]], {j, 1, 6}]
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