Allow the calculation of any metric within
generic.null( data, metrics, null.model = c("taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap", "trait.asm"), permute = 1000, comp.fun = .ses, ... ) .ses(observed, null) .metric.null( data, metrics, null.model = c("taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap", "trait.asm"), permute = 1000, trait = -1, ... ) generic.metrics(data, metrics, ...)
vector of functions to be calculated on
one of "taxa.labels", "richness", "frequency",
"sample.pool", "phylogeny.pool", "independentswap", or
"independentswap". These correspond to the null models available in
number of null permutations to perform (default 1000)
comparison function to compare observed values with
null values. Default is
additional arguments (e.g,
observed metric values in site-metric matrix
null distributions (e.g., from
generic.null Calculate metrics and compare with null
distributions. Very light wrapper around the utility functions
is, itself, a very simple function!).
generic.null Output from
comp.fun, by default
an array (site-metric-type), where type is the observed value, the
mean of the null permutations, the Standard Error of that mean, the
Standard Effect Size of the metric (obs-null.mean)/SE, and then the
rank of the observed value in the permutations. The rank can be
considered a bootstrapped p-value of significance, but remember
that this is a rank: at the 95
would be significant.
.ses Vector of standard effect sizes
.metric.null site-metric-permutation array
generic.metrics site-metric matrix
comp.fun can be anything; much ink has been
written about the use of standard effect sizes in eco-phylogenetic
analyses (e.g., Kembel 2009). That this function makes it
easy for you to compute Standard Effect Sizes does not necessarily
mean that you should (see Pearse et al. 2013).
Calculating null permutations on a dispersion metric makes little sense, since (by definition; see Pearse et al. 2014) a dispersion metric require the use of a null distribution to be calculated. There is nothing to stop you doing so, however! The code makes no attempt to stop you calculating null dissimilarity metrics, but I am not certain that doing so is a good idea using this code as I don't know what to do with such null models!
wrapper functions go to some trouble to stop you calculating
metrics using inappropriate data (see their notes). These functions
give you access to the underlying code within
pez; there is
nothing I can do to stop you calculating a metric that, in my
opinion, doesn't make any sense. You have been warned :D
Kembel S.W. (2009) Disentangling niche and neutral influences on community assembly: assessing the performance of community phylogenetic structure tests. Ecology letters, 12(9), 949-960.
Pearse W.D., Jones F.A. & Purvis A. (2013) Barro Colorado Island's phylogenetic assemblage structure across fine spatial scales and among clades of different ages. Ecology, 94(12), 2861-2872.
#Setup data data(laja) data <- comparative.comm(invert.tree, river.sites, invert.traits) #Calculate some metrics generic.metrics(data, c(.mpd, .pse)) #Compare with a trait-based null model (trait.asm) generic.null(data, c(.mpd, .pse), "trait.asm", permute=10, trait="fish.pref") #...be patient when running large (e.g., 1000) sets of null simulations #You can also do this in pieces, giving even more flexibility observed <- generic.metrics(data, c(.mpd, .pse)) #null <- .metric.null(data, c(.mpd, .pse)) #ses <- .ses(observed, null) #...this is how everything works within generic.null #...and, as with everything in pez, all internal functions start with a "."
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