designTree: Compute a design matrix or non-negative LS

In KlausVigo/phangorn: Phylogenetic Reconstruction and Analysis

Compute a design matrix or non-negative LS

Description

nnls.tree estimates the branch length using non-negative least squares given a tree and a distance matrix. designTree and designSplits compute design matrices for the estimation of edge length of (phylogenetic) trees using linear models. For larger trees a sparse design matrix can save a lot of memory. computes a contrast matrix if the method is "rooted".

Usage

designTree(tree, method = "unrooted", sparse = FALSE, tip.dates = NULL,
  calibration = NULL, ...)

nnls.tree(dm, tree, method = c("unrooted", "ultrametric", "tipdated"),
  rooted = NULL, trace = 1, weight = NULL, balanced = FALSE,
  tip.dates = NULL)

nnls.phylo(x, dm, method = "unrooted", trace = 0, ...)

nnls.splits(x, dm, trace = 0, eps = 1e-08)

nnls.networx(x, dm, eps = 1e-08)

designSplits(x, splits = "all", ...)

Arguments

tree	an object of class phylo
method	compute an "unrooted", "ultrametric" or "tipdated" tree.
sparse	return a sparse design matrix.
tip.dates	a named vector of sampling times associated to the tips of the tree.
calibration	a named vector of calibration times associated to nodes of the tree.
...	further arguments, passed to other methods.
dm	a distance matrix.
rooted	compute a "ultrametric" or "unrooted" tree (better use method).
trace	defines how much information is printed during optimization.
weight	vector of weights to be used in the fitting process. Weighted least squares is used with weights w, i.e., sum(w * e^2) is minimized.
balanced	use weights as in balanced fastME
x	number of taxa.
eps	minimum edge length (default s 1e-8).
splits	one of "all", "star".

Value

nnls.tree return a tree, i.e. an object of class phylo. designTree and designSplits a matrix, possibly sparse.

Author(s)

Klaus Schliep [e-mail]

See Also

fastme, rtt, distanceHadamard, splitsNetwork, upgma

Examples

example(NJ)
dm <-  as.matrix(dm)
y <- dm[lower.tri(dm)]
X <- designTree(tree)
lm(y~X-1)
# avoids negative edge weights
tree2 <- nnls.tree(dm, tree)

KlausVigo/phangorn documentation built on Nov. 20, 2024, 12:47 p.m.