RPANDA (version 1.9)

phyl.pca_pl: Regularized Phylogenetic Principal Component Analysis (PCA).

Description

Performs a principal component analysis (PCA) on a regularized evolutionary variance-covariance matrix obtained using the fit_t_pl function.

Usage

phyl.pca_pl(object, plot=TRUE, ...)

Arguments

object

A penalized likelihood model fit obtained by the fit_t_pl function.

plot

Plot of the PC's axes. Default is TRUE (see details).'

...

Options to be passed through. (e.g., axes=c(1,2), col, pch, cex, mode="cov" or "corr", etc.)

Value

a list with the following components

values

the eigenvalues of the evolutionary variance-covariance matrix

scores

the PC scores

loadings

the component loadings

nodes_scores

the scores for the ancestral states at the nodes (projected on the space of the tips)

mean

the mean/ancestral value used to center the data

vectors

the eigenvectors of the evolutionary variance-covariance matrix

Details

phyl.pca_pl allows computing a phylogenetic principal component analysis (following Revell 2009) using a regularized evolutionary variance-covariance matrix from penalized likelihood models fit to high-dimensional datasets (where the number of variables p is potentially larger than n; see details for the models options in fit_t_pl). Models estimates are more accurate than maximum likelihood methods, particularly in the high-dimensional case. Ploting options, the number of axes to display (axes=c(1,2) is the default), and whether the covariance (mode="cov") or correlation (mode="corr") should be used can be specified through the ellipsis "..." argument.

References

Revell, L.J., 2009. Size-correction and principal components for intraspecific comparative studies. Evolution, 63:3258-3268.

Clavel, J., Aristide, L., Morlon, H., 2019. A Penalized Likelihood framework for high-dimensional phylogenetic comparative methods and an application to new-world monkeys brain evolution. Syst. Biol. 68: 93-116.

See Also

fit_t_pl, ancestral, GIC.fit_pl.rpanda, gic_criterion

Examples

# NOT RUN {
require(mvMORPH)
set.seed(1)
n <- 32 # number of species
p <- 31 # number of traits

tree <- pbtree(n=n) # phylogenetic tree
R <- Posdef(p)      # a random symmetric matrix (covariance)

# simulate a dataset
Y <- mvSIM(tree, model="BM1", nsim=1, param=list(sigma=R))

# fit a multivariate Pagel lambda model with Penalized likelihood
fit <- fit_t_pl(Y, tree, model="lambda", method="RidgeAlt")

# Perform a phylogenetic PCA using the model fit (Pagel lambda model)
pca_results <- phyl.pca_pl(fit, plot=T) 

# retrieve the scores
head(pca_results$scores)
# }