designdist {vegan} | R Documentation |
You can define your own dissimilarities using terms for shared and total quantities, number of rows and number of columns. The shared and total quantities can be binary, quadratic or minimum terms. In binary terms, the shared component is number of shared species, and totals are numbers of species on sites. The quadratic terms are crossproducts and sums of squares, and minimum terms are sums of parallel minima and row totals.
designdist(x, method = "(A+B-2*J)/(A+B)", terms = c("binary", "quadratic", "minimum"), name)
x |
Input data. |
method |
Equation for your dissimilarities. This can use terms
J for shared quantity, A and B for totals,
N for the number of rows (sites) and P for the number of
columns (species). The equation also can contain any R functions accepting vector
arguments and returning vectors of the same length. |
terms |
How shared and total components are found. For vectors
x and y the "quadratic" terms are J = sum(x*y) ,
A = sum(x^2) , B = sum(y^2) , and "minimum" terms
are J = sum(pmin(x,y)) , A = sum(x) and B =
sum(y) , and "binary" terms are either of these after transforming
data into binary form (shared number of species, and number of
species for each row). |
name |
The name you want to use for your index. The default is to
combine the method equation and terms argument. |
Most popular dissimilarity measures in ecology can be expressed with
the help of terms J
, A
and B
, and some also involve
matrix dimensions N
and P
. Some examples you can define in
designdist
are:
A+B-2*J | "quadratic" | squared Euclidean |
A+B-2*J | "minimum" | Manhattan |
(A+B-2*J)/(A+B) | "minimum" | Bray-Curtis |
(A+B-2*J)/(A+B) | "binary" | Sørensen |
(A+B-2*J)/(A+B-J) | "binary" | Jaccard |
(A+B-2*J)/(A+B-J) | "minimum" | Ružička |
(A+B-2*J)/(A+B-J) | "quadratic" | (dis)similarity ratio |
1-J/sqrt(A*B) | "binary" | Ochiai |
1-J/sqrt(A*B) | "quadratic" | cosine complement |
1-phyper(J-1, A, P-A, B) | "binary" | Raup-Crick |
The function designdist
can implement most dissimilarity
indices in vegdist
or elsewhere, and it also can be
used to implement many other indices, among them (most of) those in
Legendre & Legendre (1998). It also can used to implement all indices
of beta diversity of Koleff et al. (2003).
If you want to implement binary dissimilarities based on 2x2
contingency table notation, then a = J
, b =
A-J
, c = B-J
, d = P-A-B+J
, b+c
= A+B-2*J
, and a+b+c = A+B-J
.
Function returns an object of class dist
.
Function does not use compiled code, and may be slow or use plenty of memory in large data sets. It is very easy to make errors when defining a function by hand. If an index is available in a function using compiled code, it is better to use the canned alternative.
Jari Oksanen
Koleff, P., Gaston, K.J. and Lennon, J.J. (2003) Measuring beta diversity for presence–absence data. J. Animal Ecol. 72, 367–382.
Legendre, P. and Legendre, L. (1998) Numerical Ecology. 2nd English ed. Elsevier
## Arrhenius dissimilarity: the value of z in the species-area model ## S = c*A^z when combining two sites of equal areas, where S is the ## number of species, A is the area, and c and z are model parameters. ## The A below is not the area (which cancels out), but number of ## species in one of the sites, as defined in designdist(). data(BCI) dis <- designdist(BCI, "(log(A+B-J)-log(A+B)+log(2))/log(2)") ## This can be used in clustering or ordination... ordiplot(cmdscale(dis)) ## ... or in analysing beta diversity (without gradients) summary(dis)