X, these functions compute the pair-wise distances
between all variables (rows) in X, across all observations
(columns) of X. Each function uses a different distance metric,
i.e. definition of what it means for two variables to be similar. In
hoapch version >=2.0.0, these functions return an object of class hdist
rather than a matrix.disscosangle(X, na.rm = TRUE)
disseuclid(X, na.rm = TRUE)
disscor(X, na.rm = TRUE)
dissabscosangle(X, na.rm = TRUE)
dissabscor(X, na.rm = TRUE)
vdisscosangle(X, y, na.rm = TRUE)
vdisseuclid(X, y, na.rm = TRUE)
vdisscor(X, y, na.rm = TRUE)
vdissabscosangle(X, y, na.rm = TRUE)
vdissabseuclid(X, y, na.rm = TRUE)
vdissabscor(X, y, na.rm = TRUE)ncol(X).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:
distancematrix.
as.matrix(hdist).
(See hdist for more details.)For the vector versions (e.g. vdisscosangle), a numeric vector of
nrow(X) pair-wise distances between each variable (row) in
X and the vector y.
disseuclid) defines two variables
to be close if they are similar in magnitude across observations.
Correlation distance (disscor), in contrast, defines similarity
to mean having the same pattern, but not necessarily the same magnitude.
Cosine-angle (disscosangle) distance is a correlation distance
that also accounts for magnitude. Cosine-angle distance is also known as
uncentered correlation distance. The distance metrics with 'abs' in
their names are absolute versions of each metric; the absolute value is
applied to the data before computing the distance.In hopach versions <2.0.0, these="" functions="" returned="" the="" square="" root="" of="" usual="" distance="" for="" 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.
http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/hopach.pdf
http://www.bepress.com/ucbbiostat/paper107/
http://www.stat.berkeley.edu/~laan/Research/Research_subpages/Papers/jsmpaper.pdf
distancematrixdata<-matrix(rnorm(50),nr=5)
disscosangle(data)
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