tree_phylm {sensiPhy}

Phylogenetic uncertainty - Phylogenetic Linear Regression

Description

Performs Phylogenetic linear regression evaluating uncertainty in trees topology.

Usage

tree_phylm(formula, data, phy, n.tree = 2, model = "lambda", track = TRUE, ...)

Arguments

formula

The model formula

data

Data frame containing species traits with species as row names.

phy

A phylogeny (class 'multiPhylo', see ?ape).

n.tree

Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. If NULL, n.tree = 2

model

The phylogenetic model to use (see Details). Default is lambda.

track

Print a report tracking function progress (default = TRUE)

...

Further arguments to be passed to phylolm

Details

This function fits a phylogenetic linear regression model using phylolm to n trees, randomly picked in a multiPhylo file.

All phylogenetic models from phylolm can be used, i.e. BM, OUfixedRoot, OUrandomRoot, lambda, kappa, delta, EB and trend. See ?phylolm for details.

Currently, this function can only implement simple linear models (i.e. trait~ predictor). In the future we will implement more complex models.

Output can be visualised using sensi_plot.

Value

The function tree_phylm returns a list with the following components:

formula: The formula

data: Original full dataset

sensi.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for each regression with a different phylogenetic tree.

N.obs: Size of the dataset after matching it with tree tips and removing NA's.

stats: Main statistics for model parameters.CI_low and CI_high are the lower and upper limits of the 95

all.stats: Complete statistics for model parameters. sd_intra is the standard deviation due to intraspecific variation. CI_low and CI_high are the lower and upper limits of the 95

Author(s)

Caterina Penone & Pablo Ariel Martinez

References

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

Donoghue, M.J. & Ackerly, D.D. (1996). Phylogenetic Uncertainties and Sensitivity Analyses in Comparative Biology. Philosophical Transactions: Biological Sciences, pp. 1241-1249.

Ho, L. S. T. and Ane, C. 2014. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

See Also

phylolm, sensi_plot, tree_phyglm

Examples

# Load data:
data(alien)
# This analysis needs a multiphylo file:
class(alien$phy)
alien$phy
# run PGLS accounting for phylogenetic uncertain:
tree <- tree_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, 
data = alien$data, n.tree = 30)
# To check summary results:
summary(tree)
# Visual diagnostics
sensi_plot(tree)
# You can specify which graph to print: 
sensi_plot(tree, graphs = 3)