diss: Many Different Ways to Quantify Dissimilarities Among...

In dissUtils: Utilities for making pairwise comparisons of 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 methods 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.

dissUtils documentation built on May 1, 2019, 7:28 p.m.