Description Usage Arguments Details Value Warning Author(s) References See Also Examples
The function distancematrix
is applied to a matrix of data to
compute the pair wise distances between all rows of the matrix. In
hopach versions >= 2.0.0 these distance functions are calculated in C,
rather than R, to improve run time performance.
function distancevector
is applied to a matrix and a vector
to compute the pair wise distances between each row of the matrix and
the vector. Both functions allow different choices of distance metric.
The functions dissmatrix
and dissvector
allow one to
convert between a distance matrix and a vector of the upper triangle. The
function vectmatrix
is used internally.
1 2 3 4 5 6 7 8 9  distancematrix(X, d, na.rm=TRUE)
distancevector(X, y, d, na.rm=TRUE)
dissmatrix(v)
dissvector(M)
vectmatrix(index, p)

X 
a numeric matrix. Missing values will be ignored if na.rm=TRUE. 
y 
a numeric vector, possibly a row of X. Missing values will be ignoredif na.rm=TRUE. 
na.rm 
an indicator of whether or not to remove missing values. If na.rm=TRUE (default), then distances are computed over all pairwise nonmissing values. Else missing values are propagated through the distance computation. 
d 
character string specifying the metric to be used for calculating dissimilarities between vectors. The currently available options are "cosangle" (cosine angle or uncentered correlation distance), "abscosangle" (absolute cosine angle or absolute uncentered correlation distance), "euclid" (Euclidean distance), "abseuclid" (absolute Euclidean distance), "cor" (correlation distance), and "abscor" (absolute correlation distance). Advanced users can write their own distance functions and add these. 
M 
a symmetric matrix of pair wise distances. 
v 
a vector of pair wise distances corresponding to the upper triangle of a distance matrix, stored by rows. 
index 
index in a distance vector, like that returned by 
p 
number of elements, e.g. the number of rows in a distance matrix. 
In hopach versions <2.0.0, these functions returned the square root of
the usual distance for d="cosangle"
, d="abscosangle"
,
d="cor"
, and d="abscor"
. Typically, this transformation makes
the dissimilarity correspond more closely with the norm. In order to
agree with the dist
function, the square root is no longer used
in versions >=2.0.0.
For versions >= 2.0.0 distancematrix
, a hdist
object of of all pair wise distances between the rows of the data matrix 'X',
i.e. the value of hdist[i,j]
is the distance between rows 'i' and 'j'
of 'X', as defined by 'd'. A hdist
object is an S4 class containing
four slots:
Data 
representing the lower triangle of the symmetric distance matrix. 
Size 
the number of objects (i.e. rows of the data matrix). 
Labels 
labels for the objects, usually the numbers 1 to Size. 
Call 
the distance used in the call to

A hdist object and can be converted to a matrix using as.matrix(hdist)
.
(See hdist
for more details.)
For distancevector
, a vector of all pair wise distances between
rows of 'X' and the vector 'y'. Entry 'j' is the distance between row 'j'
of 'X' and the vector 'y'.
For distancevector
, a vector of all pair wise distances between
rows of 'X' and the vector 'y'. Entry 'j' is the distance between row 'j'
of 'X' and the vector 'y'.
For dissmatrix
, the corresponding distance vector. For
dissvector
, the corresponding distance matrix. If 'M' has
'p' rows (and columns), then 'v' is length 'p*(p1)/2'.
For vectmatrix
, the indices of the row and column of a distance
matrix corresponding to entry index
in the corresponding
distance vector.
The
correlation and absolute correlation distance functions call the cor
function, and will therefore fail if there are missing values in the data and na.rm!=TRUE.
Katherine S. Pollard <[email protected]> and Mark J. van der Laan <[email protected]>, with Greg Walll
van der Laan, M.J. and Pollard, K.S. A new algorithm for hybrid hierarchical clustering with visualization and the bootstrap. Journal of Statistical Planning and Inference, 2003, 117, pp. 275303.
http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf
hopach
, correlationordering
, disscosangle
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  mydata<matrix(rnorm(50),nrow=10)
deuclid<distancematrix(mydata,d="euclid")
# old method vdeuclid<dissvector(deuclid)
vdeuclid<deuclid@Data
ddaisy<daisy(mydata)
vdeuclid
ddaisy/sqrt(length(mydata[1,]))
d1<distancematrix(mydata,d="abscosangle")
d2<distancevector(mydata,mydata[1,],d="abscosangle")
d1[1,]
d2 #equal to d1[1,]
# old method d3<dissvector(d1)
d3<d1@Data
pair<vectmatrix(5,10)
d1[pair[1],pair[2]]
d3[5]

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