sensiPhy (version 0.8.4)

intra_phyglm: Intraspecific variability - Phylogenetic Logistic Regression

Description

Performs Phylogenetic logistic regression evaluating intraspecific variability in predictor variables.

Usage

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

Arguments

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

Value

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).

Warning

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.

Details

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.

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

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.

See Also

phyloglm, sensi_plot

Examples

# NOT RUN {
# 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)
# }