Hi, and welcome!
Please see the FAQ: What's a reproducible example (`reprex`) and how do I do one? Using a reprex, complete with representative data will attract quicker and more answers. It can be difficult to reverse engineer a question without one, especially with a function that one hasn't yet used. Making it easy for the community works best both ways.
The example in help(cpt.np) is helpful:
suppressPackageStartupMessages(library(changepoint.np))
set.seed(12)
J <- function(x){
(1+sign(x))/2
}
n <- 1000
tau <- c(0.1,0.13,0.15,0.23,0.25,0.4,0.44,0.65,0.76,0.78,0.81)*n
h <- c(2.01, -2.51, 1.51, -2.01, 2.51, -2.11, 1.05, 2.16, -1.56, 2.56, -2.11)
sigma <- 0.5
t <- seq(0,1,length.out = n)
data <- array()
for (i in 1:n){
data[i] <- sum(h*J(n*t[i] - tau)) + (sigma * rnorm(1))
}
out <- cpt.np(data, penalty = "SIC",method="PELT",test.stat="empirical_distribution",
class=TRUE,minseglen=2, nquantiles =4*log(length(data)))
cpts(out)
#> [1] 100 130 150 230 250 400 440 650 760 780 810
Created on 2020-04-04 by the reprex package (v0.3.0)
Along with the documentation, I take it that the purpose of the function is to process the entire argument to find the optimal changepoints. Returning 11 changepoints from a 1,000 element array doesn't seem like something that needs rationing.
But I could be missing something, the motivation for limiting the number of changepoints?