designTree {phangorn}

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,
  ...)

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)

nnls.networx(x, dm)

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 vector of sampling times associated to the tips of 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.

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)