Evaluate the Sparsity Estimation Based on FDR

This function computes false discovery rate (FDR) for sparse element of the true coefficients given threshold.

Usage

conf_fdr(x, y, ...)

# S3 method for class 'summary.bvharsp'
conf_fdr(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

FDR value in confusion table

Details

When using this function, the true coefficient matrix \(\Phi\) should be sparse. False discovery rate (FDR) is computed by $$FDR = \frac{FP}{TP + FP}$$ where TP is true positive, and FP is false positive.

References

Bai, R., & Ghosh, M. (2018). High-dimensional multivariate posterior consistency under global-local shrinkage priors. Journal of Multivariate Analysis, 167, 157-170.

See also

confusion()