sensiPhy (version 0.8.4)

tree_influ_phylm: Interaction between phylogenetic uncertainty and influential species detection - Phylogenetic Linear Regression

Description

Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species while evaluating uncertainty in trees topology.

Usage

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

Arguments

formula

The model formula

data

Data frame containing species traits with row names matching tips in phy.

phy

A phylogeny (class 'phylo') matching data.

n.tree

Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file.

cutoff

The cutoff value used to identify for influential species (see Details)

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

Value

The function influ_phylm returns a list with the following components:

cutoff: The value selected for cutoff

formula: The formula

full.model.estimates: Coefficients, aic and the optimised value of the phylogenetic parameter (e.g. lambda) for the full model without deleted species.

influential_species: List of influential species, both based on standardised difference in intercept and in the slope of the regression. Species are ordered from most influential to less influential and only include species with a standardised difference > cutoff.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade for a given random tree. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), the standardised difference (sDIFintercept), the percentage of change in intercept compared to the full model (intercept.perc) and intercept p-value (pval.intercept). All these parameters are also reported for the regression slope (DIFestimate etc.). Additionally, model aic value (AIC) and the optimised value (optpar) of the phylogenetic parameter (e.g. kappa or lambda, depending on the phylogenetic model used) are reported.

data: Original full dataset.

errors: Species where deletion resulted in errors.

Details

This function sequentially removes one species at a time, fits a phylogenetic linear regression model using phylolm, stores the results and detects influential species. It repeats this operation using 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.

influ_phylm detects influential species based on the standardised difference in intercept and/or slope when removing a given species compared to the full model including 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 change.

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.

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

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, tree_phylm, influ_phylm, tree_influ_phyglm, sensi_plot

Examples

# NOT RUN {
# Load data:
data(alien)
# run analysis:
tree_influ <- tree_influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, 
data = alien$data, n.tree = 5)
# To check summary results:
summary(tree_influ)
# Visual diagnostics
sensi_plot(tree_influ)
sensi_plot(tree_influ, graphs = 1)
sensi_plot(tree_influ, graphs = 2)

data(alien)
tree_influ <- tree_influ_phylm(log(gestaLen) ~ log(adultMass), phy = alien$phy, 
data = alien$data[1:25, ], n.tree = 2)
summary(tree_influ)
# }