The function calculates dissimilarity or distance between
rows of a matrix of
observations according to a specific index. Three indices convert the data to
presence/absence automatically. In contingency table notation, they are: ll{
steinhaus $1 - a / (a + b + c)$
sorensen $1 - 2a / (2a + b +c)$
ochiai $1 - a / \sqrt{(a+b) \times (a+c)}$
}
Others are quantitative. For variable i in samples x and y:
ll{
ruzicka $1 - \sum min(x_i,y_i) / \sum max(x_i,y_i)$
bray/curtis $1 - \sum[2 \times min(x_i,y_i)] / \sum x_i + y_i$
roberts $1 - [(x_i+y_i) \times (min(x_i,y_i) / max(x_i,y_i)] / (x_i + y_i)$
chisq $(exp - obs) / \sqrt(exp)$
}
The weights allow the assignment of weights to individual species in the calculation
of plot-to-plot similarity. The weights can be assigned by life-form, indicator value,
or for other investigator specific reasons. For the presence/absence indices the
weights should be integers; for the quantitative indices the weights should be in the interval [0,1].
The default (rep(1,ncol(x)) is to set all species = 1.
The threshold dissimilarity step sets all values greater than "step" to 9999.9 and
then solves for the shortest path distance connecting plots to other non-9999.9 values
in the matrix. Step = 0.0 (the default) is a flag for "no shortest-path correction".