distance {analogue}R Documentation

Flexibly calculate dissimilarity or distance measures


Flexibly calculates distance or dissimilarity measures between a training set x and a fossil or test set y. If y is not supplied then the pairwise dissimilarities between samples in the training set, x, are calculated.


distance(x, ...)

## Default S3 method:
distance(x, y, method = "euclidean", weights = NULL,
         R = NULL, dist = FALSE, double.zero = FALSE, ...)

## S3 method for class 'join'
distance(x, ...)

oldDistance(x, ...)
## Default S3 method:
oldDistance(x, y, method = c("euclidean", "SQeuclidean",
            "chord", "SQchord", "bray", "chi.square",
            "SQchi.square", "information", "chi.distance",
            "manhattan", "kendall", "gower", "alt.gower",
            fast = TRUE,
            weights = NULL, R = NULL, ...)
## S3 method for class 'join'
oldDistance(x, ...)



data frame or matrix containing the training set samples, or and object of class join.


data frame or matrix containing the fossil or test set samples.


character; which choice of dissimilarity coefficient to use. One of the listed options. See Details below.


numeric; vector of weights for each descriptor.


numeric; vector of ranges for each descriptor.


logical; should the dissimilarity matrix be returned as an object of class "dist"? Ignored if y is supplied.


logical; if FALSE, the default, double zeroes are not counted in the distance calculation. If TRUE, absences of a variable in both samples counts as a similarity between the two samples. Currently this only affects methods "mixed" and "metric.mixed" forms of Gower's general coefficient.


logical; should fast versions of the dissimilarities be calculated? See details below.


arguments passed to other methods


A range of dissimilarity coefficients can be used to calculate dissimilarity between samples. The following are currently available:

euclidean d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2}
SQeuclidean d_{jk} = \sum_i (x_{ij}-x_{ik})^2
chord d_{jk} = \sqrt{\sum_i (\sqrt{x_{ij}}-\sqrt{x_{ik}})^2}
SQchord d_{jk} = \sum_i (\sqrt{x_{ij}}-\sqrt{x_{ik}})^2
bray d_{jk} = \frac{\sum_i |x_{ij} - x_{ik}|}{\sum_i (x_{ij} + x_{ik})}
chi.square d_{jk} = \sqrt{\sum_i \frac{(x_{ij} - x_{ik})^2}{x_{ij} + x_{ik}}}
SQchi.square d_{jk} = \sum_i \frac{(x_{ij} - x_{ik})^2}{x_{ij} + x_{ik}}
information d_{jk} = \sum_i (p_{ij}log(\frac{2p_{ij}}{p_{ij} + p_{ik}}) + p_{ik}log(\frac{2p_{ik}}{p_{ij} + p_{ik}}))
chi.distance d_{jk} = \sqrt{\sum_i (x_{ij}-x_{ik})^2 / (x_{i+} / x_{++})}
manhattan d_{jk} = \sum_i (|x_{ij}-x_{ik}|)
kendall d_{jk} = \sum_i MAX_i - minimum(x_{ij}, x_{ik})
gower d_{jk} = \sum_i\frac{|p_{ij} - p_{ik}|}{R_i}
alt.gower d_{jk} = \sqrt{2\sum_i\frac{|p_{ij} - p_{ik}|}{R_i}}
where R_i is the range of proportions for descriptor (variable) i
mixed d_{jk} = \frac{\sum_{i=1}^p w_{i}s_{jki}}{\sum_{i=1}^p w_{i}}
where w_i is the weight for descriptor i and s_{jki} is the similarity
between samples j and k for descriptor (variable) i.
metric.mixed as for mixed but with ordinal variables converted to ranks and handled as quantitative variables in Gower's mixed coefficient.

Argument fast determines whether fast C versions of some of the dissimilarity coefficients are used. The fast versions make use of dist for methods "euclidean", "SQeuclidean", "chord", "SQchord", and vegdist for method == "bray". These fast versions are used only when x is supplied, not when y is also supplied. Future versions of distance will include fast C versions of all the dissimilary coefficients and for cases where y is supplied.


A matrix of dissimilarities where columns are the samples in y and the rows the samples in x. If y is not provided then a square, symmetric matrix of pairwise sample dissimilarities for the training set x is returned, unless argument dist is TRUE, in which case an object of class "dist" is returned. See dist.

The dissimilarity coefficient used (method) is returned as attribute "method". Attribute "type" indicates whether the object was computed on a single data matrix ("symmetric") or across two matrices (i.e. the dissimilarties between the rows of two matrices; "asymmetric".


For method = "mixed" it is essential that a factor in x and y have the same levels in the two data frames. Previous versions of analogue would work even if this was not the case, which will have generated incorrect dissimilarities for method = "mixed" for cases where factors for a given species had different levels in x to y.

distance now checks for matching levels for each species (column) recorded as a factor. If the factor for any individual species has different levels in x and y, an error will be issued.


The dissimilarities are calculated in native R code. As such, other implementations (see See Also below) will be quicker. This is done for one main reason - it is hoped to allow a user defined function to be supplied as argument "method" to allow for user-extension of the available coefficients.

The other advantage of distance over other implementations, is the simplicity of calculating only the required pairwise sample dissimilarities between each fossil sample (y) and each training set sample (x). To do this in other implementations, you would need to merge the two sets of samples, calculate the full dissimilarity matrix and then subset it to achieve similar results.


Gavin L. Simpson and Jari Oksanen (improvements leading to method "metric.mixed" and proper handling of ordinal data via Podani's (1999) modification of Gower's general coefficient in method "mixed").


Faith, D.P., Minchin, P.R. and Belbin, L. (1987) Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69, 57–68.

Gavin, D.G., Oswald, W.W., Wahl, E.R. and Williams, J.W. (2003) A statistical approach to evaluating distance metrics and analog assignments for pollen records. Quaternary Research 60, 356–367.

Kendall, D.G. (1970) A mathematical approach to seriation. Philosophical Transactions of the Royal Society of London - Series B 269, 125–135.

Legendre, P. and Legendre, L. (1998) Numerical Ecology, 2nd English Edition. Elsevier Science BV, The Netherlands.

Overpeck, J.T., Webb III, T. and Prentice I.C. (1985) Quantitative interpretation of fossil pollen spectra: dissimilarity coefficients and the method of modern analogues. Quaternary Research 23, 87–108.

Podani, J. (1999) Extending Gower's General Coefficient of Similarity to Ordinal Characters. Taxon 48, 331–340).

Prentice, I.C. (1980) Multidimensional scaling as a research tool in Quaternary palynology: a review of theory and methods. Review of Palaeobiology and Palynology 31, 71–104.

See Also

vegdist in package vegan, daisy in package cluster, and dist provide comparable functionality for the case of missing y.


## simple example using dummy data
train <- data.frame(matrix(abs(runif(200)), ncol = 10))
rownames(train) <- LETTERS[1:20]
colnames(train) <- as.character(1:10)
fossil <- data.frame(matrix(abs(runif(100)), ncol = 10))
colnames(fossil) <- as.character(1:10)
rownames(fossil) <- letters[1:10]

## calculate distances/dissimilarities between train and fossil
## samples
test <- distance(train, fossil)

## using a different coefficient, chi-square distance
test <- distance(train, fossil, method = "chi.distance")

## calculate pairwise distances/dissimilarities for training
## set samples
test2 <- distance(train)

## Using distance on an object of class join
dists <- distance(join(train, fossil))

## calculate Gower's general coefficient for mixed data
## first, make a couple of variables factors

## fossil[,4] <- factor(sample(rep(1:4, length = 10), 10))
## train[,4] <- factor(sample(rep(1:4, length = 20), 20))
## ## now fit the mixed coefficient
## test3 <- distance(train, fossil, "mixed")

## ## Example from page 260 of Legendre & Legendre (1998)
x1 <- t(c(2,2,NA,2,2,4,2,6))
x2 <- t(c(1,3,3,1,2,2,2,5))
Rj <- c(1,4,2,4,1,3,2,5) # supplied ranges

## 1 - distance(x1, x2, method = "mixed", R = Rj)

## note this gives ~0.66 as Legendre & Legendre describe the
## coefficient as a similarity coefficient. Hence here we do
## 1 - Dij here to get the same answer.

## Tortula example from Podani (1999)
Dij <- distance(tortula[, -1], method = "mixed") # col 1 includes Taxon ID

## Only one ordered factor
data(mite.env, package = "vegan")
Dij <- distance(mite.env, method = "mixed")

## Some variables are constant
data(BCI.env, package = "vegan")
Dij <- distance(BCI.env, method = "mixed")

[Package analogue version 0.17-6 Index]