detection_check function

Function for detection performance check

detection_check(pts.final, brk, nob, critval = 5)

Arguments

• pts.final: a list of estimated change points
• brk: the true change points
• nob: length of time series
• critval: critical value for selection rate. Default value is 5. Specifically, to compute the selection rate, a selected break point is counted as a ``success'' for the $j$-th true break point, $t_j$, if it falls in the interval $[t_j - {(t_{j} - t_{j-1})}/{critval}, t_j + {(t_{j+1} - t_{j})}/{critval}]$, $j = 1,\dots, m_0$.

Returns

a matrix of detection summary results, including the absolute error, selection rate and relative location. The absolute error of the locations of the estimated break points is defined as ${error}_j =|\tilde{t}_j^f - t_j|$, $j = 1,\dots, m_0$.

Examples

# an example of 10 replicates result
set.seed(1)
nob <- 1000
brk <- c(333, 666, nob+1)
cp.list <- vector('list', 10)
for(i in 1:10){
    cp.list[[i]] <-  brk[1:2] + sample(c(-50:50),1)
}
# some replicate fails to detect all the change point
cp.list[[2]] <- cp.list[[2]][1]
cp.list[4] <- list(NULL)      # setting 4'th element to NULL.
# some replicate overestimate the number of change point
cp.list[[3]] <- c(cp.list[[3]], 800)
cp.list
res <- detection_check(cp.list, brk, nob, critval = 5)
res
# use a stricter critical value
res <- detection_check(cp.list, brk, nob, critval = 10)
res