Calculate vector scores from Procrustes-aligned landmark templates

Source: R/calc_shapevs.R

Function projects data onto vector from anchor one (score of 0) to anchor two (score of 1). See calc_vs

calc_shapevs(
  data,
  anchor1_index,
  anchor2_index,
  symm = FALSE,
  mirroredlandmarks
)

Arguments

data

Output from facefuns
anchor1_index

Vector specifying indices of faces which will constitute lower anchor point
anchor2_index

Vector specifying indices of faces which will constitute upper anchor point
symm

Symmetrize templates prior to calculating vector scores
mirroredlandmarks

Vector specifiying order of mirrored landmarks (required for symmetrizing)

Value

Returns tibble with columns "id" and "VS". If data contained rownames, these will be saved as ids.

Examples


# READ AND PREP DATA
path_to_tem <- system.file("extdata", "tem", package="facefuns")

shapedata <- facefuns(data = read_lmdata(lmdata = path_to_tem,
                                         plot = FALSE),
                      remove_points = "frlgmm",
                      plot_sample = FALSE,
                      quiet = TRUE)

# CREATE ANCHORS, e.g. female and male averages
data("LondonSet_info")

# Specify indices of faces that will constitute male and female averages
fem_i <- gsub("^ID=","", dimnames(shapedata$aligned)[[3]]) %in%
LondonSet_info$face_id[which(LondonSet_info$face_sex == "female")]

mal_i <- gsub("^ID=", "", dimnames(shapedata$aligned)[[3]]) %in%
LondonSet_info$face_id[which(LondonSet_info$face_sex == "male")]

# CALCULATE FEMALE-MALE VECTOR SCORES
calc_shapevs(shapedata, fem_i, mal_i)

#> # A tibble: 102 x 2
#>    id          VS
#>    <chr>    <dbl>
#>  1 001   -1.06   
#>  2 002    0.00299
#>  3 003   -0.250  
#>  4 004    0.855  
#>  5 005    0.548  
#>  6 006    0.582  
#>  7 007   -1.16   
#>  8 008    1.50   
#>  9 009   -1.16   
#> 10 010    0.391  
#> # … with 92 more rows


# CALCULATE FEMALE-MALE VECTOR SCORES FROM SYMMETRIZED FACES
# To symmetrize faces before calculating vector scores,
# you will have to provide indices of landmarks after mirroring

data("mirroredlandmarks")

calc_shapevs(shapedata, fem_i, mal_i,
             symm = TRUE, mirroredlandmarks = mirroredlandmarks)

#> # A tibble: 102 x 2
#>    id        VS
#>    <chr>  <dbl>
#>  1 001   -0.990
#>  2 002    0.121
#>  3 003   -0.205
#>  4 004    0.748
#>  5 005    0.555
#>  6 006    0.526
#>  7 007   -1.20 
#>  8 008    1.48 
#>  9 009   -1.14 
#> 10 010    0.473
#> # … with 92 more rows