pic.pgls: Fast PLGS estimation based on contrasts
In motmot: Models of Trait Macroevolution on Trees

Description Usage Arguments Value Author(s) See Also Examples

Estimates regression parameters for a phylogenetic generalised least-squares analysis using the fast constrasts method (Felsenstein 1973; 1985; Freckleton 2012). This implementation is applicable for continuous traits only and not factors

pic.pgls(
  formula,
  phy,
  y,
  lambda = "ML",
  return.intercept.stat = FALSE,
  meserr = NULL
)

`formula`	A model formula with continuous variables only
`phy`	An object of class `phylo` (see ape).
`y`	A matrix of trait values with rownames corresponding to the phy tip labels, and column names corresponding to the formula variable names
`lambda`	Default is "ML" meaning the phylogenetic signal of the response variable will be estimated using restricted Maximum likelihood. If a numeric value between 0-1 is provided this will be used in the calculation of regression coefficients
`return.intercept.stat`	Logical. If `TRUE` the standard errors, t value, and p value of the estimated Intercept is provided for comparison with output from `pgls` from caper etc.,. Default is `FALSE` as this slows the function as it involves constructing and calculating the inverse of the phy variance-covariance matrix, and based on contrasts the design matrix column of ones would have zero contrasts.
`meserr`	A vector (or matrix) of measurement error for each tip. This is only applicable to univariate analyses.

A list containing the model, model summary, intercept, estimate of Lambda, model log-Likelihood, model AIC

Mark N Puttick and Rob Freckleton

pgls

# Data and phylogeny
data(anolis.tree)
anolis.tree$node.label <- NULL
lm.data <- transformPhylo.sim(phy=anolis.tree, n=2, model="bm")
dat <- data.frame(x = lm.data[,1], y = lm.data[,2],
names = anolis.tree$tip, row.names = anolis.tree$tip)
picModel <- pic.pgls(formula=y ~  x,
phy=anolis.tree, y = dat, lambda=1,
return.intercept.stat=FALSE)