pwr: Phylogenetically weighted regression: a method for modeling...
In willpearse/pez: Phylogenetics for the Environmental Sciences

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

pwr.cv performs model cross validation on the holdout set holdout, K-fold cross validation can be specified using fold. pwr.treePlot plots the phylogeny and allows for plotting of PWR model coefficients at the tips of the tree. PWR model coefficients to be pllotted must be appended to the phylo4d object. pwr.phylobubbles plots either circles or squares corresponding to the magnitude of each cell of a phylo4d object, and allows the plotting of PWR moel coefficients. Model coefficients to be plotted must be appended to the phylo4d object

pwr(formula, phy4d, which.tips, bw, wfun, verbose = FALSE)

## S3 method for class 'pwr'
coef(object, ...)

pwr.cv(formula, phy4d, holdout, coef = c("formula", "slope", "all"),
  model = c("pwr", "pgls"), wfun, method)

## S3 method for class 'pwr'
plot(x, ...)

`formula`	Model formula to be fit
`phy4d`	`phylobase` format object to be used for fitting
`which.tips`	vector of integers indicating position of tips on the tree to be used, defaults to all species on the tree
`bw`	Bandwidth for model fit
`wfun`	Weighting function to be used; myust be one of `brownian`, `martins`, `gaussian`, or `Gaus`
`verbose`	Whether to be verbose during model fitting (default `FALSE`)
`object`	`pwr` object to have its coefficients extracted
`...`	Optional argument passed on to internal `phylobase` plotting functions
`holdout`	vector of integers indicating position of tips on the tree to be held out during model fitting
`coef`	coefficients to extract from the model; must be one of `formula`, `slope`, or `all`
`model`	Model type to be fit; must be either `pwr`, or `pgls`
`method`	for estimating weighting; should be one of subplex, optimize, L-BFGS-B,
`x`	output from

Phylogenetically weighted model object; use coef.pwr to extract coefficients, and pwr.tp and pwr.phylobubbles for plotting.

Some information about this

Jim Regetz, T. Jonathan Davies, Elizabeth M. Wolkovich, Brian J. McGill, and a tiny bit of cosmetic tidying from Will Pearse. Plotting code (pwr.tp and pwr.phylobubbles) only slightly adapted from code in phylobase; thank you to the authors of phylobase!

Davies, T. J., Regetz, J., Wolkovich, E. M., & McGill, B. J. (2019). Phylogenetically weighted regression: A method for modelling non-stationarity on evolutionary trees. Global Ecology and Biogeography, 28(2), 275-285.

caper:pgls phylobase:tp phylobase:phylobubbles

# Below are some examples in order of increasing complexity: (1) a
# simple demonstration, and (2) some cross-validation examples.

##########################################
# (1) A simple example ###################
##########################################
# Make up some data
# - Note the deep bifurcation in the slope term
if(FALSE){
tree <- read.tree(text=
    "(((A:1,B:1):1,(C:1,D:1):1):1,((E:1,F:1):1,(G:1,H:1):1):1);")
dat <- data.frame(
   x = c(0, 1, 2, 3, 0, 1, 2, 3),
   y = rnorm(8, c(1, 2, 3, 4, 2, 1.5, 1, 0.5), sd=0.25)
)

# Combine in phylobase format, and then run a scatterplot
c.data <- phylo4d(tree, dat)
plot(tipData(c.data)$x, tipData(c.data)$y, pch=rep(c(1,16), each=4),
    xlab="Predictor", ylab="Response", bty="l")
text(tipData(c.data)$x, tipData(c.data)$y, labels=rownames(tipData(c.data)),
    pos=3, cex=0.8)

# Phylogenetically weighted-regression with various weighting functions
pwr.b <- coef(pwr(y ~ x, combined.data, wfun="brownian"))
pwr.m <- coef(pwr(y ~ x, combined.data, wfun="martins"))
pwr.g <- coef(pwr(y ~ x, combined.data, wfun="gaussian"))
pwr.G <- coef(pwr(y ~ x, combined.data, wfun="Gauss"))

# Run a PGLS
pgls <- gls(y ~ x, data=dat, correlation=corBrownian(phy=tree))

# Contrast the PWR and PGLS estimates
pwr.treePlot(addData(c.data, data.frame(gest=coef(pgls)[2], glb=confint(pgls)[2,1],
   gub=confint(pgls)[2,2], pwr.m))[,c("est", "lb", "ub", "gest", "glb",
   "gub")], show.tip.label=TRUE, lower=-1., upper=1.5, pex=2, aex=3.5)
}