Calculate the lagged correlation between numeric vectors x and y. Vectors x and y are assumed to be captured at the same resolution and, similarly, successive values in x and y are assumed to be equi-distant. Missing values are allowed in each vector, correlations are calculated based on the complete cases.
Arguments
- x
vector, assumption is that x is longer than y
- y
vector
- min.overlap
integer value: what is the minimal number of values between x and y that should be considered?
Details
This version of the cross correlation function is different from the stats
implementation of ccf in two ways:
We consider the full region of correlations between vectors x and y as specified by min.overlap rather than just the overlap. The two vectors can be of very different length (e.g. when y is just a snippet recovered from a crime scene and x is from the full length object in the lab).
We do not use a Fourier transformation to calculate cross-correlation. This makes the evaluation slower, but prevents any edge effects.
Examples
library(dplyr)
x <- runif(20)
get_ccf(x, lead(x, 5))
#> $lag
#> [1] -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
#> [20] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
#>
#> $ccf
#> [1] NA NA NA NA NA 1.00000000
#> [7] -0.71278879 0.73747884 -0.81553757 -0.40332017 0.40602963 0.01703219
#> [13] 0.37985375 -0.41284958 -0.32528179 -0.55644623 0.15730063 0.18121184
#> [19] 0.30569062 -0.22447315 -0.58790382 -0.15320563 0.08106094 1.00000000
#> [25] 0.10985504 -0.04989051 -0.47084961 -0.03283750 0.62469144 0.27733019
#> [31] 0.17847443 -0.60628277 -0.07197082 0.08886414 0.50477083 0.17928673
#> [37] -1.00000000
#>
get_ccf(x, lag(x, 5), min.overlap = 3)
#> $lag
#> [1] -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1
#> [20] 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
#>
#> $ccf
#> [1] -0.14545732 0.70441246 -0.52446047 -0.40087762 -0.41598936 0.12376067
#> [7] 0.41565620 0.17353735 -0.18985838 -0.69158655 -0.02530303 0.10073960
#> [13] 1.00000000 0.20480769 -0.13516059 -0.50532498 -0.36700355 0.30569062
#> [19] 0.18121184 0.15730063 -0.55644623 -0.32528179 -0.41284958 0.37985375
#> [25] 0.01703219 0.40602963 -0.40332017 -0.81553757 0.73747884 -0.71278879
#> [31] NA NA NA NA NA
#>
x <- runif(100)
get_ccf(x[45:50], x, min.overlap = 6)
#> $lag
#> [1] -94 -93 -92 -91 -90 -89 -88 -87 -86 -85 -84 -83 -82 -81 -80 -79 -78 -77 -76
#> [20] -75 -74 -73 -72 -71 -70 -69 -68 -67 -66 -65 -64 -63 -62 -61 -60 -59 -58 -57
#> [39] -56 -55 -54 -53 -52 -51 -50 -49 -48 -47 -46 -45 -44 -43 -42 -41 -40 -39 -38
#> [58] -37 -36 -35 -34 -33 -32 -31 -30 -29 -28 -27 -26 -25 -24 -23 -22 -21 -20 -19
#> [77] -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0
#>
#> $ccf
#> [1] 0.748550154 -0.047674378 -0.460038519 -0.185925184 0.754964035
#> [6] 0.011531413 -0.635501372 0.056527757 0.561974218 -0.743626060
#> [11] 0.001720930 0.190317998 0.661270855 -0.674425046 -0.196502859
#> [16] 0.568160334 0.013621767 -0.487902334 0.492458264 -0.380229213
#> [21] 0.347067625 0.127015886 -0.389416721 -0.206698028 0.423753339
#> [26] -0.051284106 -0.682006660 0.333530816 0.263136113 -0.559043800
#> [31] 0.362198540 0.645897368 -0.118024020 -0.794772736 0.762121993
#> [36] 0.282202458 -0.604902891 0.276824457 0.475978060 -0.896691534
#> [41] 0.055865717 0.622206059 -0.235890127 -0.166695421 -0.408108917
#> [46] 0.426900838 0.411359843 -0.122889597 -0.538335140 0.100507223
#> [51] 1.000000000 -0.244365027 -0.314534171 0.069220830 0.447154686
#> [56] -0.063037672 0.130674716 -0.792678080 0.284160279 0.228151131
#> [61] 0.005378847 -0.925613719 0.823572455 -0.097171428 -0.337984181
#> [66] 0.024846466 0.028884465 0.177797394 -0.555305064 0.553760641
#> [71] -0.330372674 0.177578362 0.203187637 -0.267371096 -0.417195876
#> [76] 0.709702298 -0.527422343 -0.421044196 0.083233734 0.811268195
#> [81] -0.282221953 -0.047584501 0.266370006 0.504874921 -0.602352088
#> [86] -0.202356472 0.479220970 -0.693687782 0.100292971 -0.149798452
#> [91] -0.069943172 0.332179268 -0.215646113 0.121370279 -0.096661556
#>