diss {dissUtils}  R Documentation 
Many Different Ways to Quantify Dissimilarities Among
Multivariate Data
Description
this function will create a distance object corresponding to the
dissimilarities between rows in a matrix X
, or a matrix of
dissimilarities between the rows of matrices X
and Y
Usage
diss(X, Y = NULL, method = "euclidean", init.info = NULL)
Arguments
X 
a matrix of numeric data

Y 
a second matrix of numeric data, which must have the same number of
columns as X

method 
a character string that uniquely matches one of the following:
braycurtis  BrayCurtis difference, should use proportions 
canberra  Canberra difference, should use proportions 
chebyshev  Largest difference in any one dimension, like in chess 
covariance  You may want to transpose the data before using this 
euclidean  multivariate 2norm 
equality  the sum of exactly equal elements in each row 
hellinger  Hellinger difference 
jaccard  Jaccard distance 
mahalanobis  Euclidean distance after scaling and removing
covariance, which you can supply with init.info 
manhattan  The sum of each dimension, no diagonal movement allowed 
minkowski  arbitrary nnorm, so that init.info=2 yields
"euclidean" and init.info = Inf yields "chebyshev" (but don't do the latter!) 
pearson  Pearson productmoment correlation, you may want to
transpose the data 
procrustes  Doesn't scale or rotate, just treats the vectors
as matrices with init.info columns and calculates total
distance between homologous points 


init.info 
some method s require additional information. see above

Value
if is.null(Y)
, returns a distance object containing pairwise
dissimilarities between the points in X
.
if is.matrix(Y)
, returns a nrow(X)
by nrow(Y)
matrix containing pairwise dissimilarities between each point in X
and each point in Y
.
[Package
dissUtils version 1.0
Index]