This function computes FDR (false discovery rate) and FNR (false negative rate) for sparse element of the true coefficients given threshold.
confusion(x, y, ...)# S3 method for summary.bvharsp
confusion(x, y, truth_thr = 0, ...)
Confusion table as following.
| True-estimate | Positive (0) | Negative (1) |
| Positive (0) | TP | FN |
| Negative (1) | FP | TN |
summary.bvharsp object.
True inclusion variable.
not used
Threshold value when using non-sparse true coefficient matrix. By default, 0 for sparse matrix.
When using this function, the true coefficient matrix \(\Phi\) should be sparse.
In this confusion matrix, positive (0) means sparsity. FP is false positive, and TP is true positive. FN is false negative, and FN is false negative.
Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.