eco.xxx.regression

Description

Regression species co-existence against environmental tolerance, trait similarity, or phylogenetic relatedness.

Usage

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eco.env.regression(data, randomisation = c("taxa.labels", "richness",
  "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap"),
  permute = 0, method = c("quantile", "lm", "mantel"), altogether = TRUE,
  indep.swap = 1000, abundance = TRUE, ...)

eco.phy.regression(data, randomisation = c("taxa.labels", "richness",
  "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap"),
  permute = 0, method = c("quantile", "lm", "mantel"), indep.swap = 1000,
  abundance = TRUE, ...)

eco.trait.regression(data, randomisation = c("taxa.labels", "richness",
  "frequency", "sample.pool", "phylogeny.pool", "independentswap", "trialswap"),
  permute = 0, method = c("quantile", "lm", "mantel"), altogether = TRUE,
  indep.swap = 1000, abundance = TRUE, ...)

## S3 method for class 'eco.xxx.regression'
summary(object, ...)

## S3 method for class 'eco.xxx.regression'
print(x, ...)

## S3 method for class 'eco.xxx.regression'
plot(x, ...)

Arguments

data

comparative.comm for analysis

randomisation

null distribution with which to compare your community data, one of: taxa.labels (DEFAULT), richness, frequency, sample.pool, phylogeny.pool, independentswap, trialswap (as implemented in picante)

permute

the number of null permutations to perform (DEFAULT 0)

method

how to compare distance matrices (only the lower triangle;), one of: lm (linear regression), quantile (DEFAULT; quantreg::rq), mantel (mantel)

altogether

use distance matrix based on all traits (default TRUE), or perform separate regressions for each trait (returns a list, see details)

indep.swap

number of independent swap iterations to perform (if specified in randomisation; DEFAULT 1000)

abundance

whether to incorporate species' abundances (default: TRUE)

...

additional parameters to pass on to model fitting functions

object

eco.xxx.regression object

x

eco.xxx.regression object

Details

These methods are similar to those performed in Cavender-Bares et al. (2004). Each function regresses the species co-existence matrix of data (calculated using comm.dist) against either species' trait dissimilarity (eco.trait.regression), species' phylogenetic distance (eco.phy.regression), or species' shared environmental tolerances as measured by Pianka's distance (eco.env.regression).

If altogether is set to FALSE, each trait or environemntal variables in your data will have a separate eco.trait.regression or eco.env.regression applied to it. The functions will return a list of individual regressions; you can either examine/plot them as a group (see examples below), or extract an individual regression and work with that. These lists are of class eco.xxx.regression.list; a bit messy, but it does work!...

Note

Like fingerprint.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 use 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.

Author(s)

Will Pearse, Jeannine Cavender-Bares

References

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.

See Also

fingerprint.regression phy.signal

Examples

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data(laja)
#We wouldn't recommend only using ten permutations - this is just for speed!
data <- comparative.comm(invert.tree, river.sites, invert.traits, river.env)
eco.trait.regression(data, permute=10)
#Specify additional options
eco.trait.regression(data, tau=c(0.25,0.5,0.75), permute=10)
plot(eco.trait.regression(data, permute=10, method="lm"))
plot(eco.trait.regression(data, permute=10, method="lm", altogether=FALSE))

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