Calculate various measures to evaluate the performance of the estimated precision matrix.
Value
A list containing the following components:
- Fnorm
Frobenius (Hilbert-Schmidt) norm between the true and estimated precision matrices.
- KL
Kullback-Leibler divergence between the true and estimated precision matrices.
- Snorm
Spectral (operator) norm of the difference between the true and estimated precision matrices.
- precision
Precision measure, the ratio of true positives to the total predicted positives.
- recall
Recall measure, also known as Sensitivity, the ratio of true positives to the total actual positives.
- specificity
Specificity measure, the ratio of true negatives to the total actual negatives.
- F1
F1 score, the harmonic mean of Precision and Recall.
- MCC
Matthews correlation coefficient, a measure of the quality of binary classifications.
- sparsity
The proportion of zeros among edges in the estimated precision matrix.