I understand that you want to adjust variables (X1, Y1, Z1, X2, Y2, Z2) by small amounts (±0.001) in order to maximize the R-squared (R²) value from a fitlm regression. The goal is to iteratively tweak these variables until R² exceeds a target threshold, such as 0.8.
You can achieve this using a loop that tests small ±0.001 changes and updates the variables when an improvement in R² is found. Below is a sample MATLAB code to achieve the same. This approach iteratively improves R² by testing ±dx steps.
X1=1; Y1=1; Z1=1; X2=1; Y2=1; Z2=1; dx=0.001; bestR2=0;
while bestR2 < 0.8
improved = false;
for s = [-1 1]
A = sin((X1+s*dx)+(Y1+s*dx)+(Z1+s*dx));
B = sin((X2+s*dx)+(Y2+s*dx)+(Z2+s*dx));
R2 = fitlm(A', B').Rsquared.Ordinary;
if R2 > bestR2
[X1,Y1,Z1,X2,Y2,Z2] = deal(X1+s*dx, Y1+s*dx, Z1+s*dx, X2+s*dx, Y2+s*dx, Z2+s*dx);
bestR2 = R2; improved = true; break;
end
end
if ~improved, break; end
end
Please find attached the documentation of function used for reference:
I hope this assists in resolving the issue.
