diss {dissUtils}R Documentation

Many Different Ways to Quantify Dissimilarities Among Multivariate Data


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


diss(X, Y = NULL, method = "euclidean", init.info = NULL)



a matrix of numeric data


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


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

some methods require additional information. see above


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]