tree_samp_phyglm: Interaction between phylogenetic uncertainty and sensitivity...
In sensiPhy: Sensitivity Analysis for Comparative Methods

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

Performs analyses of sensitivity to species sampling by randomly removing species and detecting the effects on parameter estimates in phylogenetic logistic regression, while evaluating uncertainty in trees topology.

tree_samp_phyglm(
  formula,
  data,
  phy,
  n.sim = 30,
  n.tree = 2,
  breaks = seq(0.1, 0.5, 0.1),
  btol = 50,
  track = TRUE,
  ...
)

`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.sim`	The number of times species are randomly deleted for each `break`.
`n.tree`	Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file.
`breaks`	A vector containing the percentages of species to remove.
`btol`	Bound on searching space. For details see `phyloglm`
`track`	Print a report tracking function progress (default = TRUE)
`...`	Further arguments to be passed to `phyloglm`

This function randomly removes a given percentage of species (controlled by breaks) from the full phylogenetic logistic regression, fits a phylogenetic logistic regression model without these species using phyloglm, repeats this many times (controlled by times), stores the results and calculates the effects on model parameters. It repeats this operation using n trees, randomly picked in a multiPhylo file.

Only logistic regression using the "logistic_MPLE"-method from phyloglm is implemented.

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

Output can be visualised using sensi_plot.

The function samp_phylm returns a list with the following components:

formula: The formula

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

sensi.estimates: A data frame with all simulation estimates. Each row represents a model rerun with a given number of species n.remov removed, representing n.percent of the full dataset. Columns report the calculated regression intercept (intercept), difference between simulation intercept and full model intercept (DIFintercept), 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. Lastly we reported the standardised difference in intercept (sDIFintercept) and slope (sDIFestimate).

sign.analysis For each break (i.e. each percentage of species removed) this reports the percentage of statistically significant (at p<0.05) intercepts (perc.sign.intercept) over all repetitions as well as the percentage of statisticaly significant (at p<0.05) slopes (perc.sign.estimate).

data: Original full dataset.

Please be aware that dropping species may reduce power to detect significant slopes/intercepts and may partially be responsible for a potential effect of species removal on p-values. Please also consult standardised differences in the (summary) output.

Gustavo Paterno, Gijsbert D.A. Werner & Caterina Penone

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

Werner, G.D.A., Cornwell, W.K., Sprent, J.I., Kattge, J. & Kiers, E.T. (2014). A single evolutionary innovation drives the deep evolution of symbiotic N2-fixation in angiosperms. Nature Communications, 5, 4087.

#' 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.

phyloglm, samp_phyglm, tree_phyglm, tree_samp_phylm,sensi_plot

## Not run: 
# Simulate Data:
set.seed(6987)
mphy = rmtree(100, N = 30)
x = rTrait(n=1,phy=mphy[[1]])
X = cbind(rep(1,100),x)
y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X)
dat = data.frame(y, x)
# Run sensitivity analysis:
tree_samp <- tree_samp_phyglm(y ~ x, data = dat, phy = mphy, n.tree = 3, n.sim=10) 
summary(tree_samp)
sensi_plot(tree_samp)
sensi_plot(tree_samp, graphs = 1)
sensi_plot(tree_samp, graphs = 2)

## End(Not run)