View source: R/fingerprint.regression.R
fingerprint.regression | R Documentation |
Calculates traits' phylogenetic inertia and regresses this against trait similarity among co-existing species (sensu Cavender-Bares et al. 2004 Figure 6)
fingerprint.regression( data, eco.rnd = c("taxa.labels", "richness", "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap"), eco.method = c("quantile", "lm", "mantel"), eco.permute = 1000, evo.method = c("lambda", "delta", "kappa", "blom.k"), eco.swap = 1000, abundance = TRUE, ... ) ## S3 method for class 'fingerprint.regression' print(x, ...) ## S3 method for class 'fingerprint.regression' summary(object, ...) ## S3 method for class 'fingerprint.regression' plot( x, eco = c("slope", "corrected"), xlab = "Community Trait Similarity", ylab = "Phylogenetic inertia", ... )
data |
|
eco.rnd |
null distribution with which to compare your
community data, one of: |
eco.method |
how to compare distance matrices (only the lower
triangle;), one of: |
eco.permute |
number of permutations for ecological null model
( |
evo.method |
how to measure phylogenetic inertia, one of:
|
eco.swap |
number of independent swap iterations to perform
(if specified in |
abundance |
whether to incorporate species' abundances (default: TRUE) |
... |
additional parameters to pass on to model fitting functions and plotting functions |
x |
|
object |
|
eco |
plot the observed slopes (DEFAULT: |
xlab |
label for x-axis (default "Ecological Trait Coexistence") |
ylab |
label for y-axis (default "Phylogenetic inertia") |
While the term ‘fingerprint regression’ is new to pez, the method is very similar to that employed in Cavender-Bares et al. 2004 Figure 6. For each trait, the phylogenetic inertia of species traits is regressed against their co-occurrence in the community matrix. Note that Pagel's lambda, delta, and kappa, and Blomberg's K, can be used, unlike the original where a mantel test was employed. Moreover, note also that Pianka's distance (as described in the manuscript) is used to measure species overlap.
Like eco.xxx.regression
, this is a data-hungry
method. Warnings will be generated if any of the methods cannot be
fitted properly (the examples below give toy examples of this). In
such cases the summary and plot methods of these functions may
generate errors; perhaps using traceback
to examine
where these are coming from, and consider whether you want to be
working with the data generating these errors. I am loathe to hide
these errors or gloss over them, because they represent the reality
of your data!
WDP loves quantile regressions, and advises that you check
different quantiles using the tau
options.
Will Pearse and Jeannine Cavender-Bares
Cavender-Bares J., Ackerly D.D., Baum D.A. & Bazzaz F.A. (2004) Phylogenetic overdispersion in Floridian oak communities. The Americant Naturalist 163(6): 823–843.
Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H., Ackerly, D.D., Blomberg, S.P. & Webb, C.O. Picante: R tools for integrating phylogenies and ecology. Bioinformatics 26(11): 1463–1464.
Pagel M. Inferring the historical patterns of biological evolution. Nature 401(6756): 877–884.
eco.xxx.regression
phy.signal
data(laja) data <- comparative.comm(invert.tree, river.sites, invert.traits, river.env) fingerprint.regression(data, eco.permute=10) plot(fingerprint.regression(data, permute=10, method="lm"))
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