sensiPhy (version 0.8.5)

tree_intra_phyglm: Interaction between phylogenetic uncertainty and intraspecific variability - Phylogenetic logistic Regression

Description

Performs Phylogenetic logistic regression evaluating intraspecific variability in response and/or predictor variables and uncertainty in trees topology.

Usage

tree_intra_phyglm(
  formula,
  data,
  phy,
  Vx = NULL,
  x.transf = NULL,
  n.intra = 10,
  n.tree = 2,
  distrib = "normal",
  track = TRUE,
  btol = 50,
  ...
)

Arguments

formula

The model formula: response~predictor.

data

Data frame containing species traits and species names 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

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).

n.intra

Number of times to repeat the analysis generating a random value for response and/or predictor variables. If NULL, n.intra = 30

n.tree

Number of times to repeat the analysis with n different trees picked randomly in the multiPhylo file. If NULL, n.tree = 2

distrib

A character string indicating which distribution to use to generate a random value for the response and/or predictor variables. Default is normal distribution: "normal" (function rnorm). Uniform distribution: "uniform" (runif) Warning: we recommend to use normal distribution with Vx or Vy = standard deviation of the mean.

track

Print a report tracking function progress (default = TRUE)

btol

Bound on searching space. For details see phyloglm

...

Further arguments to be passed to phyloglm

Value

The function tree_intra_phylm 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 using a value in the interval of variation and a different phylogenetic tree.

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. Fields coded using all describe statistics due to both intraspecific variation and phylogenetic uncertainty. Fields coded using intra describe statistics due to intraspecific variation only. Fields coded using tree describe statistics due to phylogenetic uncertainty only. sd is the standard deviation. 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 Vy or Vx exceed Y or X, respectively, 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 times, 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 to n trees (n.tree), randomly picked in a multiPhylo file. The regression is also repeated n.intra times. At each iteration the function generates a random value for each row in the dataset using the standard deviation or errors 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, tree_phyglm, intra_phyglm, tree_intra_phylm, sensi_plot

Examples

# NOT RUN {
# Simulate data
set.seed(6987)
mphy = ape::rmtree(150, N = 30)
x = phylolm::rTrait(n=1,phy=mphy[[1]])
x_sd = rnorm(150,mean = 0.8,sd=0.2)
X = cbind(rep(1,150),x)
y = rbinTrait(n=1,phy=mphy[[1]], beta=c(-1,0.5), alpha=.7 ,X=X)
dat = data.frame(y, x, x_sd)
intra.tree <- tree_intra_phyglm(y ~ x, data = dat, phy = mphy, n.intra = 3, 
                                           n.tree = 3, Vx = "x_sd")
# summary results:
summary(intra.tree)
# Visual diagnostics for phylogenetic uncertainty:
sensi_plot(intra.tree, uncer.type = "all") #or uncer.type = "tree", uncer.type = "intra"
# }