Fpop {fpopw} | R Documentation |
Fpop
Description
Function to run the Fpop algorithm (Maidstone et al. 2016). It uses functional pruning and optimal partionning. It optimizes the L2-loss for a penalty lambda per change.
Usage
Fpop(x, lambda, mini = min(x), maxi = max(x))
Arguments
x |
a numerical vector to segment |
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(x=x,lambda=2*est.sd^2*log(length(x)))
smt <- getSMT(res)
[Package fpopw version 1.1 Index]