stepR (version 2.0-4)

compareBlocks: Compare fit blockwise with ground truth

Description

Blockwise comparison of a fitted step function with a known ground truth using different criteria.

Usage

compareBlocks(truth, estimate, dist = 5e3)

Arguments

truth

an object of class stepblock giving the ground truth, or a list of such objects

estimate

corresponding estimated object(s) of class stepblock

dist

a single numeric specifying the distance for at which jumps will be considered as having matched in the qualitative criterion

Value

A data.frame, containing just one row if two single stepblock were given, with columns

true.num, est.num

the true / estimated number of blocks

true.pos, false.pos, false.neg, sens.rate, prec.rate

the number of true / false positive, false negatives, as well as the corresponding sensitivity and precision rates, where an estimated block is considered a true positive if it there is a corresponding block in the ground truth with both endpoints within dist of each other

fpsle

false positive sensitive localization error: for each estimated block's midpoint find into which true block it falls, and sum distances of the respective borders

fnsle

false negative sensitive localization error: for each true block's mid-point find into which estimated block it falls, and sum distances of the respective borders

total.le

total localization error: sum of fpsle and fnsle

References

Elhaik, E., Graur, D., Josi<U+0107>, K. (2010) Comparative testing of DNA segmentation algorithms using benchmark simulations. Molecular Biology and Evolution 27(5), 1015-24.

Futschik, A., Hotz, T., Munk, A. Sieling, H. (2014) Multiresolution DNA partitioning: statistical evidence for segments. Bioinformatics, 30(16), 2255--2262.

See Also

stepblock, stepfit, contMC

Examples

# NOT RUN {
# simulate two Gaussian hidden Markov models of length 1000 with 2 states each
# with identical transition rates being 0.01 and 0.05, resp, signal-to-noise ratio is 5
sim <- lapply(c(0.01, 0.05), function(rate)
  contMC(1e3, 0:1, matrix(c(0, rate, rate, 0), 2), param=1/5))
plot(sim[[1]]$data)
lines(sim[[1]]$cont, col="red")
# use smuceR to estimate fit
fit <- lapply(sim, function(s) smuceR(s$data$y, s$data$x))
lines(fit[[1]], col="blue")
# compare fit with (discretised) ground truth
compareBlocks(lapply(sim, function(s) s$discr), fit)
# }