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

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]