intra_phyglm: Intraspecific variability - Phylogenetic Logistic Regression
In sensiPhy: Sensitivity Analysis for Comparative Methods

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

Performs Phylogenetic logistic regression evaluating intraspecific variability in predictor variables.

intra_phyglm(
  formula,
  data,
  phy,
  Vx = NULL,
  n.intra = 30,
  x.transf = NULL,
  distrib = "normal",
  btol = 50,
  track = TRUE,
  ...
)

`formula`	The model formula: `response~predictor`.
`data`	Data frame containing species traits with species as row names.
`phy`	A phylogeny (class 'phylo', see ?`ape`).
`Vx`	Name of the column containing the standard deviation or the standard error of the predictor variable. When information is not available for one taxon, the value can be 0 or `NA`
`n.intra`	Number of times to repeat the analysis generating a random value for the predictor variable. If NULL, `n.intra` = 2
`x.transf`	Transformation for the predictor variable (e.g. `log` or `sqrt`). Please use this argument instead of transforming data in the formula directly (see also details below).
`distrib`	A character string indicating which distribution to use to generate a random value for the predictor variable. Default is normal distribution: "normal" (function `rnorm`). Uniform distribution: "uniform" (`runif`) Warning: we recommend to use normal distribution with Vx = standard deviation of the mean.
`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 fits a phylogenetic logistic regression model using phyloglm. The regression is repeated n.intra times. At each iteration the function generates a random value for each row in the dataset using the standard deviation or error supplied and assuming a normal or uniform distribution. To calculate means and se for your raw data, you can use the summarySE function from the package Rmisc.

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

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 intra_phyglm 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.

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

sp.pb: Species that caused problems with data transformation (see details above).

When Vx exceeds X negative (or null) values can be generated, this might cause problems for data transformation (e.g. log-transformation). In these cases, the function will skip the simulation. This problem can be solved by increasing n.intra, changing the transformation type and/or checking the target species in output$sp.pb.

Caterina Penone & Pablo Ariel Martinez

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

Martinez, P. a., Zurano, J.P., Amado, T.F., Penone, C., Betancur-R, R., Bidau, C.J. & Jacobina, U.P. (2015). Chromosomal diversity in tropical reef fishes is related to body size and depth range. Molecular Phylogenetics and Evolution, 93, 1-4

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, sensi_plot

# Simulate Data:
set.seed(6987)
phy = rtree(150)
x = rTrait(n=1,phy=phy)
x_sd = rnorm(150,mean = 0.8,sd=0.2)
X = cbind(rep(1,150),x)
y = rbinTrait(n=1,phy=phy, beta=c(-1,0.5), alpha=.7 ,X=X)
dat = data.frame(y, x, x_sd)

# Run phylogenetic logistic regression accounting for intraspecific variation:
intra_glm <- intra_phyglm(y~x,Vx = "x_sd",data = dat,phy=phy,distrib = "normal")

#Print summary of sensitivity analysis
summary(intra_glm)
head(intra_glm$sensi.estimates)
#Visual output
sensi_plot(intra_glm)