Identify at most one consistent correlation peak (ccp)

If multi segment lengths strategy is being used, at most one consistent correlation peak (ccp) will be found for the corresponding basis segment. If the ccp cannot be identified, return NULL

get_ccp(ccr_list, Tx = 25)

Arguments

ccr_list

list, obtained by get_ccr_peaks

Tx

integer, the tolerance zone is +/- Tx

Value

integer, the position of the ccp if it is identified; NULL otherwise.

Examples

data("bullets")
land2_3 <- bullets$sigs[bullets$bulletland == "2-3"][[1]]
land1_2 <- bullets$sigs[bullets$bulletland == "1-2"][[1]]
x <- land2_3$sig
y <- land1_2$sig

segments <- get_segs(x, len = 50)

# identify the consistent correlation peak when ccf curves are computed
# based on y and segment 7 in 3 different scales;
# the number of peaks identified in each scale are 5, 3, and 1, respectively.
seg_scale_max <- 3
npeaks_set <- c(5,3,1)
outlength <- c(50, 100, 200)

ccr_list <- lapply(1:seg_scale_max, function(seg_scale) {
  get_ccr_peaks(y, segments, seg_outlength = outlength[seg_scale], nseg = 7, 
  npeaks = npeaks_set[seg_scale])
})

get_ccp(ccr_list, Tx = 25)
#> [1] -6