t seems like you are trying to do k- fold validation.To use K-Fold validation for bias correction with the given, follow these steps:
1. Prepare Data: Align and size-match your buoy "Tb" and satellite "Ts" data
2. Set Up K-Fold: Choose a number of folds, typically 5 or 10, based on your data size.
3. Split Data: Divide data into "K" non-overlapping folds.
- Validate in K-Folds:
- Treat one fold as validation and the rest as training.
- Use linear regression on the training set to model "Tb" from "Ts" accounting for bias.
- Estimate m and n to minimize E = Tb - Ts - m*Ts - n.
- Optionally, validate these estimates using the validation set.
4. Average Results: Calculate the mean of m and n estimates from all folds for final values.
Here is the MATLAB Code Sample for the same:
% Assuming Tb and Ts data are defined
K = 5; % Example fold number
cv = cvpartition(length(Tb), 'KFold', K);
m_values = zeros(K, 1);
n_values = zeros(K, 1);
for i = 1:K
trainIdx = training(cv, i);
model = fitlm(Ts(trainIdx), Tb(trainIdx), 'Intercept', true);
m_values(i) = model.Coefficients.Estimate(2); % m estimate
n_values(i) = model.Coefficients.Estimate(1); % n estimate
end
m_final = mean(m_values);
n_final = mean(n_values);
disp(['Estimated m: ', num2str(m_final)]);
disp(['Estimated n: ', num2str(n_final)]);
Additionally, you can refer the the following MATLAB fileexchange article:
I hope this helps!
Thanks
Chetan
