designTree | R Documentation |
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".
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", ...)
tree |
an object of class |
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". |
nnls.tree
return a tree, i.e. an object of class
phylo
. designTree
and designSplits
a matrix, possibly
sparse.
Klaus Schliep klaus.schliep@gmail.com
fastme
, rtt
,
distanceHadamard
,
splitsNetwork
, upgma
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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