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 | Bray-Curtis 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 2-norm |
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 n-norm, so that init.info=2 yields
"euclidean" and init.info = Inf yields "chebyshev" (but don't do the latter!) |
pearson | Pearson product-moment 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]