Fpop_w: Fpop_w

In fpopw: Weighted Segmentation using Functional Pruning and Optimal Partioning

Fpop_w

Description

Function to run the Fpop algorithm (Maidstone et al. 2016) with weights. It uses functional pruning and optimal partionning. It optimizes the weighted L2-loss (w_i (x_i - μ)2) for a penalty lambda per change.

Usage

Fpop_w(x, w, lambda, mini = min(x), maxi = max(x))

Arguments

x	a numerical vector to segment.
w	a numerical vector of weights (values should be larger than 0).
lambda	the penalty per changepoint (see Maidstone et al. 2016).
mini	minimum mean segment value to consider in the optimisation.
maxi	maximum mean segment value to consider in the optimisation.

Value

return a list with a vector t.est containing the position of the change-points, the number of changes K and, the cost J.est.

Examples

x <- c(rnorm(100), rnorm(10^3)+2, rnorm(1000)+1)
est.sd <- sdDiff(x) ## rough estimate of std-deviation
res <- Fpop_w(x=x, w=rep(1, length(x)), lambda=2*est.sd^2*log(length(x)))
smt <- getSMT(res)

fpopw documentation built on June 7, 2022, 1:09 a.m.