Mirrors templates and runs GPA on original plus mirrored templates. Asymmetry scores are then calculated as Euclidean distance between original template and its symmetrized version (i.e. the mean of original and mirrored template)

calc_as(data, mirroredlandmarks)

Arguments

data

facefuns object or three-dimensional array of dimensions p, k, and n

mirroredlandmarks

Vector specifying order of mirrored landmarks

Value

Returns tibble containing ID and asymmetry scores

Details

NOTE: does not distinguish between directional and fluctuating asymmetry

Examples

data(LondonSet_aligned) data(mirroredlandmarks) calc_as(LondonSet_aligned, mirroredlandmarks)
#> # A tibble: 102 x 2 #> id asym #> <chr> <dbl> #> 1 001 0.0368 #> 2 002 0.0476 #> 3 003 0.0256 #> 4 004 0.0323 #> 5 005 0.0222 #> 6 006 0.0310 #> 7 007 0.0255 #> 8 008 0.0252 #> 9 009 0.0291 #> 10 010 0.0330 #> # … with 92 more rows