designTree: 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", ...)

Value

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

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".

Author

Klaus Schliep klaus.schliep@gmail.com

Examples

Run this code


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)

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