clade_phyglm: Estimate the impact on model estimates of phylogenetic...
In sensiPhy: Sensitivity Analysis for Comparative Methods

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

Estimate the impact on model estimates of phylogenetic logistic regression after removing clades from the analysis.

clade_phyglm(
  formula,
  data,
  phy,
  btol = 50,
  track = TRUE,
  clade.col,
  n.species = 5,
  n.sim = 100,
  ...
)

`formula`	The model formula
`data`	Data frame containing species traits with row names matching tips in `phy`.
`phy`	A phylogeny (class 'phylo') matching `data`.
`btol`	Bound on searching space. For details see `phyloglm`
`track`	Print a report tracking function progress (default = TRUE)
`clade.col`	The column in the provided data frame which specifies the clades (a character vector with clade names).
`n.species`	Minimum number of species in a clade for the clade to be included in the leave-one-out deletion analysis. Default is `5`.
`n.sim`	Number of simulations for the randomization test.
`...`	Further arguments to be passed to `phyloglm`

This function sequentially removes one clade at a time, fits a phylogenetic logistic regression model using phyloglm and stores the results. The impact of of a specific clade on model estimates is calculated by a comparison between the full model (with all species) and the model without the species belonging to a clade.

To account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species as in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

Currently, only logistic regression using the "logistic_MPLE"-method from phyloglm is implemented.

clade_phyglm detects influential clades based on difference in intercept and/or estimate when removing a given clade compared to the full model including all species.

Additionally, to account for the influence of the number of species on each clade (clade sample size), this function also estimates a null distribution expected for the number of species in a given clade. This is done by fitting models without the same number of species in the given clade. The number of simulations to be performed is set by 'n.sim'. To test if the clade influence differs from the null expectation for a clade of that size, a randomization test can be performed using 'summary(x)'.

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 clade_phyglm 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. alpha) for the full model without deleted species.

sensi.estimates: A data frame with all simulation estimates. Each row represents a deleted clade. 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 are reported.

null.dist: A data frame with estimates for the null distributions for all clades analysed.

data: Original full dataset.

errors: Clades where deletion resulted in errors.

clade.col: Which column was used to specify the clades?

Gustavo Paterno & Gijsbert 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.

phyloglm, clade_phylm, influ_phyglm, sensi_plot

## Not run: 
# Simulate Data:
set.seed(6987)
phy = rtree(150)
x = rTrait(n=1,phy=phy)
X = cbind(rep(1,150),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
cla <- rep(c("A","B","C","D","E"), each = 30)
dat = data.frame(y, x, cla)
# Run sensitivity analysis:
clade <- clade_phyglm(y ~ x, phy = phy, data = dat, n.sim = 30, clade.col = "cla")
# To check summary results and most influential clades:
summary(clade)
# Visual diagnostics for clade removal:
sensi_plot(clade)
# Specify which clade removal to plot:
sensi_plot(clade, "B")
sensi_plot(clade, "C")
sensi_plot(clade, "D") #The clade with the largest effect on slope and intercept

## End(Not run)