RPANDA (version 2.4)

Posdef: Positive definite symmetric matrices

Description

Generates a positive definite and symmetric matrix with specified eigen-values

Usage

Posdef(p, ev = rexp(p, 1/100))

Value

Returns a symmetric positive-definite matrix with eigen-values = ev.

Arguments

p

The dimension of the matrix

ev

The eigenvalues. If not specified, eigenvalues are taken from an exponential distribution.

Author

J. Clavel

Details

Posdef generates random positive definite covariance matrices with specified eigen-values that can be used to simulate multivariate datasets (see Uyeda et al. 2015 - and supplied R codes).

References

Uyeda J.C., Caetano D.S., Pennell M.W. 2015. Comparative Analysis of Principal Components Can be Misleading. Syst. Biol. 64:677-689.

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

GIC.fit_pl.rpanda, fit_t_pl phyl.pca_pl

Examples

# \donttest{
test = FALSE
if(test){
if(require(mvMORPH)){
set.seed(123)
n <- 32 # number of species
p <- 40 # 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))

test <- fit_t_pl(Y, tree, model="BM", method="RidgeAlt")
GIC(test)
}
}
# }