detection_check: Function for detection performance check

In VARDetect: Multiple Change Point Detection in Structural VAR Models

Function for detection performance check

Description

Function for detection performance check

Usage

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.

Value

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

VARDetect documentation built on June 22, 2024, 10 a.m.