Returned the 2D statistics to the original equations, ensuring calculations are done for each model; added unittests for the 2D statistics#20
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Three bugs when fitting PCE with multiple response columns (e.g., results file with >1 column): 1. model.py get_press_stats(): np.delete(responses, idx) without axis flattens the 2D array. Added axis=0 to delete rows, matching the var_basis delete on the preceding line. 2. statistics.py calc_R_sq(): scalar comparison 'if R_sq > 1' fails when R_sq is an array. Use np.atleast_1d, np.any, and np.clip for element-wise handling. Returns scalar for single response. 3. pce.py check_fit(): press_stat[m] and R_sq[m] index into arrays whose shape depends on atleast_2d wrapping. Use .flat[m] to reliably extract the m-th scalar regardless of shape. Single-response behavior is unchanged.
calc_error_sum_of_sq and calc_total_sum_of_sq used matrix algebra forms (y'y - β'X'y and y'y - (Σy)²/n) that produce (m×m) matrices for m responses. R² was then computed from the full matrix instead of just the diagonal, giving incorrect values (always ~1.0). Additionally, calc_total_sum_of_sq had np.sum(responses)**2 which sums ALL elements across responses into one scalar instead of summing each column independently. Replaced both with direct residual-based computation: SSE = Σ(y - ŷ)² per column SST = Σ(y - ȳ)² per column Returns 1D arrays for multi-response, scalars for single-response. Equivalent to the matrix form for single response; correct for both.
…_workflow.yml and tests_workflow.yml files
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