influ_physig: Influential species detection - Phylogenetic signal
In sensiPhy: Sensitivity Analysis for Comparative Methods

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Performs leave-one-out deletion analysis for phylogenetic signal estimates, and detects influential species for K or lambda.

1	influ_physig(trait.col, data, phy, method = "K", cutoff = 2, track = TRUE, ...)

`trait.col`	The name of a column in the provided data frame with trait to be analyzed (e.g. "Body_mass").
`data`	Data frame containing species traits with row names matching tips in `phy`.
`phy`	A phylogeny (class 'phylo') matching `data`.
`method`	Method to compute signal: can be "K" or "lambda".
`cutoff`	The cutoff value used to identify for influential species (see Details)
`track`	Print a report tracking function progress (default = TRUE)
`...`	Further arguments to be passed to `phylosig`

This function sequentially removes one species at a time, ans estimates phylogenetic signal (K or lambda) using phylosig, stores the results and detects the most influential species.

influ_physig detects influential species based on the standardised difference in signal estimate (K or lambda) when removing a given species compared to the full data estimate (with all species). Species with a standardised difference above the value of cutoff are identified as influential. The default value for the cutoff is 2 standardised differences in signal estimate.

Output can be visualised using sensi_plot.

The function influ_physig returns a list with the following components:

cutoff: The value selected for cutoff

trait.col: Column name of the trait analysed

full.data.estimates: Phylogenetic signal estimate (K or lambda) and the P value (for the full data).

influential_species: List of influential species, based on standardised difference in K or lambda. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

influ.physig.estimates: A data frame with all simulation estimates. Each row represents a deleted species Columns report the calculated signal estimate (k) or (lambda), difference between signal estimation of the reduced and full data (DF), the percentage of change in signal compared to the full data signal (perc) and p-value for the phylogenetic signal test (pval)

data: Original full dataset.

The argument "se" from phylosig is not available in this function. Use the argument "V" instead with intra_physig to indicate the name of the column containing the standard deviation or the standard error of the trait variable instead.

Gustavo Paterno

Paterno, G. B., Penone, C. Werner, G. D. A. sensiPhy: An r-package for sensitivity analysis in phylogenetic comparative methods. Methods in Ecology and Evolution 2018, 9(6):1461-1467

Blomberg, S. P., T. Garland Jr., A. R. Ives (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745.

Pagel, M. (1999) Inferring the historical patterns of biological evolution. Nature, 401, 877-884.

Kamilar, J. M., & Cooper, N. (2013). Phylogenetic signal in primate behaviour, ecology and life history. Philosophical Transactions of the Royal Society B: Biological Sciences, 368: 20120341.

phylosig, influ_phylm,sensi_plot

## Not run: 
# Load data:
data(alien)
# Logtransform data
alien.data$logMass <- log(alien.data$adultMass) 
# Run sensitivity analysis:
influ <- influ_physig("logMass", data = alien.data, phy = alien.phy[[1]])
# To check summary results:
summary(influ)
# Most influential speciesL
influ$influential.species
# Visual diagnostics
sensi_plot(influ)
# You can specify which graph to print: 
sensi_plot(influ, graphs = 1)
sensi_plot(influ, graphs = 2)

## End(Not run)