the distance measure to be used. This must be one of
"euclidean", "maximum", "manhattan",
"canberra", "binary" or "minkowski".
Any unambiguous substring can be given.
diag
logical value indicating whether the diagonal of the
distance matrix should be printed by print.dist.
upper
logical value indicating whether the upper triangle of the
distance matrix should be printed by print.dist.
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 non-negative 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 non-zero 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*(i-1) - i*(i-1)/2 + j-i].
The length of the vector is n*(n-1)/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 diag
and upper above, specifying how the object should be printed.
call
optional, the call used to create the
object.
method
optional, the distance measure used; resulting from
distmc(), the (match.arg()ed) method
argument.
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.
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Rlof)
Loading required package: doParallel
Loading required package: foreach
Loading required package: iterators
Loading required package: parallel
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Rlof/distmc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: distmc
> ### Title: Distance Matrix Computation with multi-threads
> ### Aliases: distmc
> ### Keywords: distmc lof
>
> ### ** Examples
>
> data(iris)
> df<-iris[-5]
> dist.data<-distmc(df,'manhattan')
>
>
>
>
>
> dev.off()
null device
1
>