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