phylolm (version 2.1)

phyloglm: Phylogenetic Generalized Linear Model

Description

Fits the phylogenetic logistic regression described in Ives and Garland (2010). The computation uses an algorithm that is linear in the number of tips in the tree.

Usage

phyloglm(formula, data, phy, method = c("MPLE","IG10"), btol = 10,
         log.alpha.bound = 4, start.beta=NULL, start.alpha=NULL)

Arguments

formula

a model formula.

data

a data frame containing variables in the model. If not found in data, the variables are taken from the current environment.

phy

a phylogenetic tree of type phylo with branch lengths.

method

The "IG10" method optimizes a GEE approximation to the penalized likelihood. "MPLE" maximizes the penalized likelihood. In both cases, the penalty is Firth's correction.

btol

bound on the linear predictor to bound the searching space.

log.alpha.bound

bound for the log of the parameter alpha

start.beta

starting values for beta coefficients

start.alpha

starting values for alpha (phylogenetic correlation)

Value

• coefficients
  the named vector of coefficients.
• alpha
  the phylogenetic correlation parameter.
• sd
  standard deviation for the regression coefficients, given the phylogenetic correlation parameter $\alpha$.
• vcov
  covariance matrix for the regression coefficients, given the phylogenetic correlation parameter.
• logLik
  log likelihood
• aic
  AIC
• penlogLik
  penalized log likelihood, using Firth's penalty for coefficients
• y
  response
• n
  number of observations (tips in the tree)
• d
  number of dependent variables
• formula
  the model formula
• call
  the original call to the function
• method
  the estimation method
• convergence
  An integer code with '0' for successful optimization. With MPLE, this is the convergence code from the optim routine.

encoding

latin1

Details

This function uses an algorithm that is linear in the number of tips in the tree.

References

Ho, L. S. T. and An�, C. "A linear-time algorithm for Gaussian and non-Gaussian trait evolution models". Systematic Biology 63(3):397-408.

Ives, A. R. and T. Garland, Jr. 2010. "Phylogenetic logistic regression for binary dependent variables". Systematic Biology 59:9-26.

Examples

set.seed(123456)
tre = rtree(50)
x = rTrait(n=1,phy=tre)
X = cbind(rep(1,50),x)
y = rbinTrait(n=1,phy=tre, beta=c(-1,0.5), alpha=1 ,X=X)
dat = data.frame(trait01 = y, predictor = x)
fit = phyloglm(trait01~predictor,phy=tre,data=dat)
summary(fit)
coef(fit)
vcov(fit)