Distance Matrix Computation with multithreads
Description
This function is similar to dist()
in stats, with additional support of multithreading.
Usage
1 
Arguments
x 
a numeric matrix, data frame or 
method 
the distance measure to be used. This must be one of

diag 
logical value indicating whether the diagonal of the
distance matrix should be printed by 
upper 
logical value indicating whether the upper triangle of the
distance matrix should be printed by 
p 
The power of the Minkowski distance. 
Details
Available distance measures are (written for two vectors x and y):
euclidean
:Usual square distance between the two vectors (2 norm).
maximum
:Maximum distance between two components of x and y (supremum norm)
manhattan
:Absolute distance between the two vectors (1 norm).
canberra
:
sum(x_i  y_i / x_i + y_i). Terms with zero numerator and denominator are omitted from the sum and treated as if the values were missing.
This is intended for nonnegative values (e.g. counts): taking the absolute value of the denominator is a 1998 R modification to avoid negative distances.
binary
:(aka asymmetric binary): The vectors are regarded as binary bits, so nonzero elements are ‘on’ and zero elements are ‘off’. The distance is the proportion of bits in which only one is on amongst those in which at least one is on.
minkowski
:The p norm, the pth root of the sum of the pth powers of the differences of the components.
Missing values are allowed, and are excluded from all computations
involving the rows within which they occur.
Further, when Inf
values are involved, all pairs of values are
excluded when their contribution to the distance gave NaN
or
NA
.
If some columns are excluded in calculating a Euclidean, Manhattan,
Canberra or Minkowski distance, the sum is scaled up proportionally
to the number of columns used. If all pairs are excluded when calculating a
particular distance, the value is NA
.
The "distmc"
method of as.matrix()
and as.dist()
can be used for conversion between objects of class "dist"
and conventional distance matrices.
as.dist()
is a generic function. Its default method handles
objects inheriting from class "dist"
, or coercible to matrices
using as.matrix()
. Support for classes representing
distances (also known as dissimilarities) can be added by providing an
as.matrix()
or, more directly, an as.dist
method
for such a class.
Value
distmc
returns an object of class "dist"
.
The lower triangle of the distance matrix stored by columns in a
vector, say do
. If n
is the number of
observations, i.e., n < attr(do, "Size")
, then
for i < j ≤ n, the dissimilarity between (row) i and j is
do[n*(i1)  i*(i1)/2 + ji]
.
The length of the vector is n*(n1)/2, i.e., of order n^2.
The object has the following attributes (besides "class"
equal
to "dist"
):
Size 
integer, the number of observations in the dataset. 
Labels 
optionally, contains the labels, if any, of the observations of the dataset. 
Diag, Upper 
logic, corresponding to the arguments 
call 
optional, the 
method 
optional, the distance measure used; resulting from

References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
Mardia, K. V., Kent, J. T. and Bibby, J. M. (1979) Multivariate Analysis. Academic Press.
Borg, I. and Groenen, P. (1997) Modern Multidimensional Scaling. Theory and Applications. Springer.
See Also
dist()
in the stats package
Examples
1 2 3 