find.neighbor(x, quadtree=quad.tree(x), k=1, metric="euclidean",
              max.dist=NULL)

x:

a vector (or matrix) containing the multidimensional point(s) at which the nearest neighbors are desired. The vector must have the same number of elements as the number of columns in the numeric matrix used to construct quadtree. If a matrix is used, the matrix must have the same number of columns as the numeric matrix used to construct quadtree, and nearest neighbors are found for each row in the matrix.

quadtree:

an object of class "quad.tree" containing the sorted matrix of data for which a nearest neighbor search is desired. Defaults to quad.tree(x) if x is a matrix but it is required when x is a vector.

k:

the number of nearest neighbors to be found. If the data x is the same data that was used to construct the "quad.tree" object, then k = 1 results in each element having itself as its own nearest neighbor.

metric:

a character string giving the metric to be used when finding "nearest" neighbors. Partial matching is allowed. Possible values are: "euclidean", "least absolute value", and "city block" for the l_2, l_1, and l-infinity norm, respectively. For two vectors x and y, these are defined as

l_1 = sum | x[i] - y[i] |, L_2 = sqrt(sum ( x[i] - y[i] )^2), and L_infinity = max | x[i] - y[i] |

max.dist:

if max.dist is given, argument k is ignored, and all of the neighbors within distance max.dist of each row in x are found.

index1:

if x is a matrix, the row in x for this nearest neighbor. If x is not a matrix, the value 1.

index2:

the row in the matrix from which the quad tree was formed for this nearest neighbor. If the quad tree was formed from a matrix y, then x[index1[i],] and y[index2[i],] are neighbors.

distances:

the corresponding nearest neighbor distances.

x <- cbind(sids$easting, sids$northing)
sids.nhbr <- find.neighbor(x, max.dist = 30)