Dissimilarity Analysis
Dissimilarity analysis is a graphical analysis of the distribution of values in a dissimilarity matrix
disana(x, panel='all')
Calculates three vectors: the minimum, mean, and maximum dissimilarity for each sample in a dissimilarity matrix. By default it produces three plots: the sorted dissimilarity values, the sorted min, mean, and maximum dissimilarity for each sample, and the mean dissimilarity versus the minimum dissimilarity for each sample. Optionally, you can identify sample plots in the last panel with the mouse.
Plots three graphs to the current graphical device, and returns an (invisible) list with four components:
min |
the minimum dissimilarity of each sample to all others |
mean |
the mean dissimilarity of each sample to all others |
max |
the maximum dissimilarity of each sample to all others |
plots |
a vector of samples identified in the last panel |
Dissimilarity matrices are often large, and difficult to visualize directly. ‘disana’ is designed to highlight aspects of interest in these large matrices. If the first panel shows a long limb of constant maximum value, you should consider recalculating the dissimilarity with a step-across adjustment. The third panel is useful for identifying outliers, which are plots more than 0.5 dissimilar to their nearest neighbor.
data(bryceveg) # returns a data.frame called veg dis.bc <- dsvdis(bryceveg,'bray/curtis') disana(dis.bc)
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