As described in Pearse et al. (2014), a dissimilarity metric
compares diversity between communities. WARNING: Phylosor is
presented as a distance matrix here, i.e. it is *not* the fraction
of shared branch length among communities, but rather '1 - shared
branch length'. This means dissimilarity always returns a
*distance* object, not a similarity object; this is a different
convention from other packages.
pez.dissimilarity(data, metric = c("all", "unifrac", "pcd", "phylosor",
"comdist"), abundance.weighted = FALSE, permute = 1000,
sqrt.phy = FALSE, traitgram = NULL, traitgram.p = 2,
ext.dist = NULL, ...)comparative.comm object
default (all) calculates everything;
individually call-able metrics are: unifrac, pcd,
phylosor, comdist.
If TRUE (default is FALSE) metrics are
calculated incorporating species abundances; only comdist
can incorporate abundances
Number of permutations for metric (currently only
for pcd)
If TRUE (default is FALSE) your phylogenetic distance matrix will be square-rooted; specifying TRUE will force the square-root transformation on phylogenetic distance matrices (in the spirit of Leitten and Cornwell, 2014). See `details' for details about different metric calculations when a distance matrix is used.
If not NULL (default), a number to be passed to
funct.phylo.dist (phyloWeight; the `a' parameter),
causing analysis on a distance matrix reflecting both traits and
phylogeny (0 --> only phylogeny, 1 --> only traits; see
funct.phylo.dist). Unlike other metric wrapper functions,
dissimilarity does not accept a vector of traitgram values;
call the function many times to get these. This is simply because
it can take so long: you're probably better off
looping/apply-ing over this function yourself.
A value for `p' to be used in conjunction with
traitgram when calling funct.phylo.dist.
Supply an external species-level distance matrix for use in calculations. See `details' for comments on the use of distance matrices in different metric calculations.
additional parameters to be passed to `metric function(s) you are calling
list object of metric values.
Using square-rooted distance matrices, or distance matrices that
incorporate trait information, can be an excellent thing to do, but
(for the above reasons), pez won't give you an answer for
metrics for which WDP thinks it makes no sense. All results from
this other than comdist *will always be wrong* if you do not
have an ultrametric tree and square-root (branch lengths
proportional to time) and you will be warned about this. WDP
strongly feels you should only be using ultrametric phylogenies in
any case, but code to fix this bug is welcome.
Pearse W.D., Purvis A., Cavender-Bares J. & Helmus M.R. (2014). Metrics and Models of Community Phylogenetics. In: Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Springer Berlin Heidelberg, pp. 451-464.
unifrac Lozupone C.A. & Knight
R. (2005). UniFrac: a new phylogenetic method for comparing
microbial communities. Applied and Environmental Microbiology, 71,
8228-8235.
pcd Ives A.R. & Helmus M.R. (2010). Phylogenetic
metrics of community similarity. The American Naturalist, 176,
E128-E142.
phylosor Bryant J.A., Lamanna C., Morlon H.,
Kerkhoff A.J., Enquist B.J. & Green J.L. (2008). Microbes on
mountainsides: Contrasting elevational patterns of bacterial and
plant diversity. Proceedings of the National Academy of Sciences of
the United States of America, 105, 11505-11511.
comdist C.O. Webb, D.D. Ackerly, and
S.W. Kembel. 2008. Phylocom: software for the analysis of
phylogenetic community structure and trait
evolution. Bioinformatics 18:2098-2100.
# NOT RUN {
data(laja)
data <- comparative.comm(invert.tree, river.sites, invert.traits)
# }
# NOT RUN {
dissim <- pez.dissimilarity(data)
# }
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