Calculates the shortest euclidean distance found for every point of one matrix among those of a second.

edm_nearest calculates the shortest euclidean distance found for every point of one matrix among those of a second. In other words, if m1, m2 have n rows, the result will be the shortest distance for the first point of m1 to any point of m2 and so on, n times. This function is used internally but may be of interest for other analyses.

Usage

edm_nearest(m1, m2, full = FALSE)

Arguments

m1: The first list or matrix of coordinates.
m2: The second list or matrix of coordinates.
full: logical. Whether to returns a condensed version of the results.

Value

If full is TRUE, returns a list with two components: d which is for every point of m1 the shortest distance found between it and any point in m2, and pos the (m2) row indices of these points. Otherwise returns d as a numeric vector of the shortest distances.

Details

So far this function is quite time consumming since it performs \( n \times n \) euclidean distance computation. If one wishes to align two (or more shapes) Procrustes surimposition may provide a better solution.

Examples

x <- matrix(1:10, nc=2)
edm_nearest(x, x+rnorm(10))
#> [1] 0.9276974 0.2276210 1.3132366 0.3104909 1.5710566
edm_nearest(x, x+rnorm(10), full=TRUE)
#> $d
#> [1] 1.2055581 1.0015449 1.1344017 1.1321094 0.5926906
#> 
#> $pos
#> [1] 1 1 3 5 5
#>