fpopw (version 1.1)

Fpop: 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

# NOT RUN {
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)
# }