Performs leave-one-out deletion analysis for phylogenetic linear regression, and detects influential species while evaluating uncertainty in trees topology.
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The model formula
Data frame containing species traits with row names matching tips
A phylogeny (class 'phylo') matching
Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file.
The cutoff value used to identify for influential species (see Details)
The phylogenetic model to use (see Details). Default is
Print a report tracking function progress (default = TRUE)
Further arguments to be passed to
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.
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
influ_phylm returns a list with the following
cutoff: The value selected for
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 >
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
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
DIFestimate etc.). Additionally, model aic value (
the optimised value (
optpar) of the phylogenetic parameter
lambda, depending on the phylogenetic model used) are
data: Original full dataset.
errors: Species where deletion resulted in errors.
Gustavo Paterno, Caterina Penone & Gijsbert D.A. Werner
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.
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## 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) ## End(Not run)
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