Description Usage Arguments Value References See Also Examples
Compute V-measure, a segmentation evaluation measure, which is based upon two criteria for clustering usefulness, homogeneity and completeness.
1 |
sig |
true signal; a numeric vector |
est |
estimator; a numeric vector |
beta |
parameter in definition of V-measure, see (Rosenberg and Hirschberg, 2007) for details |
A scalar takes value in [0, 1], with a larger value indicating higher accuracy.
Rosenberg, A., and Hirschberg, J. (2007). V-measure: a conditional entropy-based external cluster evaluation measures. Proc. Conf. Empirical Methods Natural Lang. Process., (June):410–420.
computeFdp
, smuce
, fdrseg
, evalStepFun
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | # simulate data
u0 <- c(rep(1, 50), rep(5, 50))
Y <- rnorm(100, u0)
# compute FDRSeg
uh <- fdrseg(Y)
plot(Y, pch = 20, col = "grey", xlab = "", ylab = "")
lines(u0, type = "s", col = "blue")
lines(evalStepFun(uh), type = "s", col = "red")
legend("topleft", c("Truth", "FDRSeg"), lty = c(1, 1), col = c("blue", "red"))
# compute V-measure
vm <- v_measure(u0, evalStepFun(uh))
print(vm)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.