R/aro.clo.points.R

In Arothron: Geometric Morphometric Methods and Virtual Anthropology Tools

#' aro.clo.points
#'
#' Find the closest matches between a reference (2D or 3D matrix) and a target matrix (2D/3D) or mesh returning row indices and distances
#' @param target kxm matrix or object of class "mesh3d"
#' @param reference numeric: a kxm matrix (coordinates)
#' @return position numeric: a vector of the row indices 
#' @return distances numeric: a vector of the coordinates distances 
#' @author Antonio Profico, Costantino Buzi, Marina Melchionna, Paolo Piras, Pasquale Raia, Alessio Veneziano
#' @examples
#' #load an example: mesh, and L set
#' data(yoda_sur)
#' data(yoda_set)
#' sur<-yoda_sur
#' set<-yoda_set
#' ver_pos<-aro.clo.points(target=sur,reference=set) 
#' @export

aro.clo.points<-function (target, reference) 
{
if (is(target ,"mesh3d")) {
  target = t(target$vb)[, -4]
}
if (is(reference,"mesh3d")) {
  reference = t(reference$vb)[, -4]
}
if (dim(target)[2] != dim(reference)[2]) {
  stop("the number of columns (d) must be the same")
}
position <- NULL
distances <- NULL
for (i in 1:dim(reference)[1]) {
  distance <- sqrt((target[, 1] - reference[i, 1])^2 + 
                     (target[, 2] - reference[i, 2])^2 + (target[, 3] - 
                                                            reference[i, 3])^2)
  mindist_selector <- which(distance == min(distance))
  position <- c(position, mindist_selector)
  distances <- c(distances, min(distance))
}
out = list(position = position, distances = distances)
return(out)
}