# metricsThreshold: Performance metrics for estimate of connectiviy matrix A. In backShift: Learning Causal Cyclic Graphs from Unknown Shift Interventions

## Description

Computes various performance metrics for estimate of connectiviy matrix A.

## Usage

 `1` ```metricsThreshold(trueA, est, thres = seq(0.01, 1, by = 0.01)) ```

## Arguments

 `trueA` True connectivity matrix `est` Estimated connectivity matrix `thres` Value at which the point estimate should be thresholded, i.e. edges with coefficients smaller than thres are discarded. Can be a sequence of values.

## Value

A data frame with the following columns:

• `Threshold` Value at which point estimate `est` was thresholded.

• `SHD` Structural Hamming distance between `trueA` and `est`.

• `TPR.Recall` True positive rate / recall value

• `FPR` False positive rate

• `Precision` Precision value

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16``` ```# true A p <- 3 A <- diag(p)*0 A[1,2] <- 0.8 A[2,3] <- -0.8 A[3,1] <- 0.8 # say an estimated connectivity matrix is given by: A.est <- matrix(rnorm(p*p, 1e-3, 1e-3), ncol = p) diag(A.est) <- 0 A.est[1,2] <- 0.76 A.est[2,3] <- -0.68 A.est[3,1] <- 0.83 # compute metrics with threshold 0.25 metricsThreshold(A, A.est, thres = 0.25) ```

backShift documentation built on May 1, 2019, 9:25 p.m.