confusion: Evaluate the Sparsity Estimation Based on Confusion Matrix

In bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Evaluate the Sparsity Estimation Based on Confusion Matrix

Description

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.

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.

Value

Confusion table as following.

True-estimate	Positive (0)	Negative (1)
Positive (0)	TP	FN
Negative (1)	FP	TN

References

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

bvhar documentation built on April 4, 2025, 5:22 a.m.