Hi Albert,
You can calculate the covariance between two matrices A and B using "cov" function. To calculate covariance for each row, iterate over each row using a loop. Here is the sample code:
A = [1 9 3; 4 5 6; 7 8 6]; % Replace with your matrix A
B = [6 8 7; 6 5 4; 3 2 1]; % Replace with your matrix B
[numRows, numCols] = size(A);
covarianceValues = zeros(numRows, 1);
R2Values = zeros(numRows, 1);
% Loop through each row
for i = 1:numRows
% Extract the i-th row from A and B
Ai = A(i, :);
Bi = B(i, :);
covMatrix = cov(Ai, Bi);
covarianceValues(i) = covMatrix(1, 2);
To calculate the determination coefficient R^2, you can calculate the standard deviation of each row using "std" function and apply the formula for 'r'.
sigmaA = std(Ai);
sigmaB = std(Bi);
% Calculate the determination coefficient R^2
R2Values(i) = (covarianceValues(i) / (sigmaA * sigmaB))^2;
end
disp(covarianceValues);
disp('Determination coefficient R^2 for each row:');
disp(R2Values);
Note - covariance matrix covMatrix returned by the cov function contains the variances of Ai and Bi on the diagonal and the covariance between Ai and Bi off the diagonal.
Refer to the following documentation for more information on the function:
