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YZ phangorn/man/ 0000755 0001762 0000144 00000000000 14140463604 012643 5 ustar ligges users phangorn/man/read.aa.Rd 0000644 0001762 0000144 00000003255 13766736055 014452 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phyDat.R
\name{read.aa}
\alias{read.aa}
\title{Read Amino Acid Sequences in a File}
\usage{
read.aa(file, format = "interleaved", skip = 0, nlines = 0,
comment.char = "#", seq.names = NULL)
}
\arguments{
\item{file}{a file name specified by either a variable of mode character, or
a double-quoted string.}
\item{format}{a character string specifying the format of the DNA sequences.
Three choices are possible: \code{"interleaved"}, \code{"sequential"}, or
\code{"fasta"}, or any unambiguous abbreviation of these.}
\item{skip}{the number of lines of the input file to skip before beginning
to read data.}
\item{nlines}{the number of lines to be read (by default the file is read
until its end).}
\item{comment.char}{a single character, the remaining of the line after this
character is ignored.}
\item{seq.names}{the names to give to each sequence; by default the names
read in the file are used.}
}
\value{
a matrix of amino acid sequences.
}
\description{
This function reads amino acid sequences in a file, and returns a matrix
list of DNA sequences with the names of the taxa read in the file as row
names.
}
\references{
% Anonymous. FASTA format description. %
\url{https://en.wikipedia.org/wiki/FASTA_format}
Felsenstein, J. (1993) Phylip (Phylogeny Inference Package) version 3.5c.
Department of Genetics, University of Washington.
\url{https://evolution.genetics.washington.edu/phylip/phylip.html}
}
\seealso{
\code{\link[ape]{read.dna}}, \code{\link[ape]{read.GenBank}},
\code{\link[phangorn]{phyDat}}, \code{\link[seqinr]{read.alignment}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{IO}
phangorn/man/consensusNet.Rd 0000644 0001762 0000144 00000003555 13707232051 015627 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networx.R
\name{consensusNet}
\alias{consensusNet}
\title{Computes a consensusNetwork from a list of trees Computes a \code{networx}
object from a collection of splits.}
\usage{
consensusNet(obj, prob = 0.3, ...)
}
\arguments{
\item{obj}{An object of class multiPhylo.}
\item{prob}{the proportion a split has to be present in all trees to be
represented in the network.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
\code{consensusNet} returns an object of class networx. This is
just an intermediate to plot phylogenetic networks with igraph.
}
\description{
Computes a consensusNetwork, i.e. an object of class \code{networx} from a
list of trees, i.e. an class of class \code{multiPhylo}. Computes a
\code{networx} object from a collection of splits.
}
\examples{
data(Laurasiatherian)
set.seed(1)
bs <- bootstrap.phyDat(Laurasiatherian, FUN = function(x)nj(dist.hamming(x)),
bs=50)
cnet <- consensusNet(bs, .3)
plot(cnet)
\dontrun{
library(rgl)
open3d()
plot(cnet, type = "3D", show.tip.label=FALSE, show.nodes=TRUE)
plot(cnet, type = "equal angle", show.edge.label=TRUE)
tmpfile <- normalizePath(system.file("extdata/trees/RAxML_bootstrap.woodmouse", package="phangorn"))
trees <- read.tree(tmpfile)
cnet_woodmouse <- consensusNet(trees, .3)
plot(cnet_woodmouse, type = "equal angle", show.edge.label=TRUE)
}
}
\references{
Holland B.R., Huber K.T., Moulton V., Lockhart P.J. (2004) Using
consensus networks to visualize contradictory evidence for species
phylogeny. \emph{Molecular Biology and Evolution}, \bold{21}, 1459--61
}
\seealso{
\code{\link{splitsNetwork}}, \code{\link{neighborNet}},
\code{\link{lento}}, \code{\link{distanceHadamard}},
\code{\link{plot.networx}}, \code{\link{maxCladeCred}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{hplot}
phangorn/man/allTrees.Rd 0000644 0001762 0000144 00000001476 13707232051 014713 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treeManipulation.R
\name{allTrees}
\alias{allTrees}
\title{Compute all trees topologies.}
\usage{
allTrees(n, rooted = FALSE, tip.label = NULL)
}
\arguments{
\item{n}{Number of tips (<=10).}
\item{rooted}{Rooted or unrooted trees (default: rooted).}
\item{tip.label}{Tip labels.}
}
\value{
an object of class \code{multiPhylo}.
}
\description{
\code{allTrees} computes all tree topologies for rooted or unrooted trees
with up to 10 tips. \code{allTrees} returns bifurcating trees.
}
\examples{
trees <- allTrees(5)
old.par <- par(no.readonly = TRUE)
par(mfrow = c(3,5))
for(i in 1:15)plot(trees[[i]])
par(old.par)
}
\seealso{
\code{\link[ape]{rtree}}, \code{\link{nni}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/writeDist.Rd 0000644 0001762 0000144 00000003610 13707232051 015106 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distSeq.R
\name{writeDist}
\alias{writeDist}
\alias{write.nexus.dist}
\alias{readDist}
\alias{read.nexus.dist}
\alias{unique.dist}
\title{Writing and reading distances in phylip and nexus format}
\usage{
writeDist(x, file = "", format = "phylip", ...)
write.nexus.dist(x, file = "", append = FALSE, upper = FALSE,
diag = TRUE, digits = getOption("digits"), taxa = !append)
readDist(file, format = "phylip")
read.nexus.dist(file)
\method{unique}{dist}(x, incomparables, ...)
}
\arguments{
\item{x}{A \code{dist} object.}
\item{file}{A file name.}
\item{format}{file format, default is "phylip", only other option so far is
"nexus".}
\item{\dots}{Further arguments passed to or from other methods.}
\item{append}{logical. If TRUE the nexus blocks will be added to a file.}
\item{upper}{logical value indicating whether the upper triangle of the
distance matrix should be printed.}
\item{diag}{logical value indicating whether the diagonal of the distance
matrix should be printed.}
\item{digits}{passed to format inside of \code{write.nexus.dist}.}
\item{taxa}{logical. If TRUE a taxa block is added.}
\item{incomparables}{Not used so far.}
}
\value{
an object of class \code{dist}
}
\description{
\code{readDist}, \code{writeDist} and \code{write.nexus.dist} are useful to
exchange distance matrices with other phylogenetic programs.
}
\examples{
data(yeast)
dm <- dist.ml(yeast)
writeDist(dm)
write.nexus.dist(dm)
}
\references{
Maddison, D. R., Swofford, D. L. and Maddison, W. P. (1997)
NEXUS: an extensible file format for systematic information.
\emph{Systematic Biology}, \bold{46}, 590--621.
}
\seealso{
To compute distance matrices see \code{\link{dist.ml}}
\code{\link[ape]{dist.dna}} and \code{\link{dist.p}} for pairwise
polymorphism p-distances
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/SOWH.test.Rd 0000644 0001762 0000144 00000004147 13707232051 014674 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SOWH.R
\name{SOWH.test}
\alias{SOWH.test}
\title{Swofford-Olsen-Waddell-Hillis Test}
\usage{
SOWH.test(x, n = 100, restricted = list(optNni = FALSE), optNni = TRUE,
trace = 1, ...)
}
\arguments{
\item{x}{an object of class \code{"pml"}.}
\item{n}{the number of bootstrap replicates.}
\item{restricted}{list of restricted parameter settings.}
\item{optNni}{Logical value indicating whether topology gets optimized
(NNI).}
\item{trace}{Show output during computations.}
\item{\dots}{Further arguments passed to \code{"optim.pml"}.}
}
\value{
an object of class SOWH. That is a list with three elements, one is
a matrix containing for each bootstrap replicate the (log-) likelihood of
the restricted and unrestricted estimate and two pml objects of the
restricted and unrestricted model.
}
\description{
This function computes the Swofford--Olsen--Waddell--Hillis (SOWH) test, a
parametric bootstrap test. The function is computational very demanding and
likely to be very slow.
}
\details{
\code{SOWH.test} performs a parametric bootstrap test to compare two trees.
It makes extensive use \code{simSeq} and \code{optim.pml} and can take quite
long.
}
\examples{
# in real analysis use larger n, e.g. 500 preferably more
\dontrun{
data(Laurasiatherian)
dm <- dist.logDet(Laurasiatherian)
tree <- NJ(dm)
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit, TRUE)
set.seed(6)
tree <- rNNI(fit$tree, 1)
fit <- update(fit, tree = tree)
(res <- SOWH.test(fit, n=100))
summary(res)
}
}
\references{
Goldman, N., Anderson, J. P., and Rodrigo, A. G. (2000)
Likelihood -based tests of topologies in phylogenetics. \emph{Systematic
Biology} \bold{49} 652-670.
Swofford, D.L., Olsen, G.J., Waddell, P.J. and Hillis, D.M. (1996)
Phylogenetic Inference in Hillis, D.M., Moritz, C. and Mable, B.K. (Eds.)
\emph{Molecular Systematics} (2nd ed.) 407-514, Sunderland, MA: Sinauer
}
\seealso{
\code{\link{pml}}, \code{\link{pmlPart}}, \code{\link{pmlCluster}},
\code{\link{simSeq}}, \code{\link{SH.test}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{models}
phangorn/man/ancestral.pml.Rd 0000644 0001762 0000144 00000005442 13707232051 015700 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ancestral_pml.R
\name{ancestral.pml}
\alias{ancestral.pml}
\alias{ancestral.pars}
\alias{pace}
\alias{plotAnc}
\title{Ancestral character reconstruction.}
\usage{
ancestral.pml(object, type = "marginal", return = "prob")
ancestral.pars(tree, data, type = c("MPR", "ACCTRAN"), cost = NULL,
return = "prob")
pace(tree, data, type = c("MPR", "ACCTRAN"), cost = NULL, return = "prob")
plotAnc(tree, data, i = 1, site.pattern = TRUE, col = NULL,
cex.pie = par("cex"), pos = "bottomright", ...)
}
\arguments{
\item{object}{an object of class pml}
\item{type}{method used to assign characters to internal nodes, see details.}
\item{return}{return a \code{phyDat} object or matrix of probabilities.}
\item{tree}{a tree, i.e. an object of class pml}
\item{data}{an object of class phyDat}
\item{cost}{A cost matrix for the transitions between two states.}
\item{i}{plots the i-th site pattern of the \code{data}.}
\item{site.pattern}{logical, plot i-th site pattern or i-th site}
\item{col}{a vector containing the colors for all possible states.}
\item{cex.pie}{a numeric defining the size of the pie graphs}
\item{pos}{a character string defining the position of the legend}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
%A matrix containing the the estimates character states. An object
of class "phyDat", containing the ancestral states of all nodes.
}
\description{
Marginal reconstruction of the ancestral character states.
}
\details{
The argument "type" defines the criterion to assign the internal nodes. For
\code{ancestral.pml} so far "ml" and (empirical) "bayes" and for
\code{ancestral.pars} "MPR" and "ACCTRAN" are possible.
With parsimony reconstruction one has to keep in mind that there will be
often no unique solution.
For further details see vignette("Ancestral").
}
\examples{
example(NJ)
fit <- pml(tree, Laurasiatherian)
anc.ml <- ancestral.pml(fit, type = "ml")
anc.p <- ancestral.pars(tree, Laurasiatherian)
\dontrun{
require(seqLogo)
seqLogo( t(subset(anc.ml, 48, 1:20)[[1]]), ic.scale=FALSE)
seqLogo( t(subset(anc.p, 48, 1:20)[[1]]), ic.scale=FALSE)
}
# plot the first site pattern
plotAnc(tree, anc.ml, 1)
# plot the third character
plotAnc(tree, anc.ml, attr(anc.ml, "index")[3])
}
\references{
Felsenstein, J. (2004). \emph{Inferring Phylogenies}. Sinauer
Associates, Sunderland.
Swofford, D.L., Maddison, W.P. (1987) Reconstructing ancestral character
states under Wagner parsimony. \emph{Math. Biosci.} \bold{87}: 199--229
Yang, Z. (2006). \emph{Computational Molecular evolution}. Oxford University
Press, Oxford.
}
\seealso{
\code{\link{pml}}, \code{\link{parsimony}}, \code{\link[ape]{ace}},
\code{\link[ape]{root}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/dfactorial.Rd 0000644 0001762 0000144 00000001230 13707232051 015234 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hadamard.R
\name{ldfactorial}
\alias{ldfactorial}
\alias{dfactorial}
\title{Arithmetic Operators}
\usage{
ldfactorial(x)
dfactorial(x)
}
\arguments{
\item{x}{a numeric scalar or vector}
}
\value{
\code{dfactorial(x)} returns the double factorial, that is
\eqn{x\!\! = 1 * 3 * 5 * \ldots * x } and \code{ldfactorial(x)} is the
natural logarithm of it.
}
\description{
double factorial function
}
\examples{
dfactorial(1:10)
}
\seealso{
\code{\link[base:Special]{factorial}},
\code{\link[ape]{howmanytrees}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{classif}
phangorn/man/plot.networx.Rd 0000644 0001762 0000144 00000007413 13707232051 015620 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networx.R
\name{plot.networx}
\alias{plot.networx}
\title{plot phylogenetic networks}
\usage{
\method{plot}{networx}(x, type = "equal angle", use.edge.length = TRUE,
show.tip.label = TRUE, show.edge.label = FALSE, edge.label = NULL,
show.node.label = FALSE, node.label = NULL, show.nodes = FALSE,
tip.color = "black", edge.color = "black", edge.width = 3,
edge.lty = 1, split.color = NULL, split.width = NULL,
split.lty = NULL, font = 3, cex = par("cex"), cex.node.label = cex,
cex.edge.label = cex, col.node.label = tip.color,
col.edge.label = tip.color, font.node.label = font,
font.edge.label = font, ...)
}
\arguments{
\item{x}{an object of class \code{"networx"}}
\item{type}{"3D" to plot using rgl or "2D" in the normal device.}
\item{use.edge.length}{a logical indicating whether to use the edge weights
of the network to draw the branches (the default) or not.}
\item{show.tip.label}{a logical indicating whether to show the tip labels on
the graph (defaults to \code{TRUE}, i.e. the labels are shown).}
\item{show.edge.label}{a logical indicating whether to show the tip labels
on the graph.}
\item{edge.label}{an additional vector of edge labels (normally not needed).}
\item{show.node.label}{a logical indicating whether to show the node labels
(see example).}
\item{node.label}{an additional vector of node labels (normally not needed).}
\item{show.nodes}{a logical indicating whether to show the nodes (see
example).}
\item{tip.color}{the colors used for the tip labels.}
\item{edge.color}{the colors used to draw edges.}
\item{edge.width}{the width used to draw edges.}
\item{edge.lty}{a vector of line types.}
\item{split.color}{the colors used to draw edges.}
\item{split.width}{the width used to draw edges.}
\item{split.lty}{a vector of line types.}
\item{font}{an integer specifying the type of font for the labels: 1 (plain
text), 2 (bold), 3 (italic, the default), or 4 (bold italic).}
\item{cex}{a numeric value giving the factor scaling of the labels.}
\item{cex.node.label}{a numeric value giving the factor scaling of the node
labels.}
\item{cex.edge.label}{a numeric value giving the factor scaling of the edge
labels.}
\item{col.node.label}{the colors used for the node labels.}
\item{col.edge.label}{the colors used for the edge labels.}
\item{font.node.label}{the font used for the node labels.}
\item{font.edge.label}{the font used for the edge labels.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\description{
So far not all parameters behave the same on the the \code{rgl} \code{"3D"} and
basic graphic \code{"2D"} device.
}
\details{
Often it is easier and safer to supply vectors of graphical parameters for
splits (e.g. splits.color) than for edges. These overwrite values edge.color.
}
\note{
The internal representation is likely to change.
}
\examples{
set.seed(1)
tree1 <- rtree(20, rooted=FALSE)
sp <- as.splits(rNNI(tree1, n=10))
net <- as.networx(sp)
plot(net)
\dontrun{
# also see example in consensusNet
example(consensusNet)
}
}
\references{
Dress, A.W.M. and Huson, D.H. (2004) Constructing Splits Graphs
\emph{IEEE/ACM Transactions on Computational Biology and Bioinformatics
(TCBB)}, \bold{1(3)}, 109--115
Schliep, K., Potts, A. J., Morrison, D. A. and Grimm, G. W. (2017),
Intertwining phylogenetic trees and networks. \emph{Methods Ecol Evol}.
\bold{8}, 1212--1220. doi:10.1111/2041-210X.12760
}
\seealso{
\code{\link{consensusNet}}, \code{\link{neighborNet}},
\code{\link{splitsNetwork}}, \code{\link{hadamard}},
\code{\link{distanceHadamard}}, \code{\link{as.networx}},
\code{\link[ape]{evonet}}, \code{\link[ape]{as.phylo}},
\code{\link{densiTree}}, \code{\link[ape]{nodelabels}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{plot}
phangorn/man/pmlPart.Rd 0000644 0001762 0000144 00000004665 14006563476 014575 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pmlPart.R
\name{multiphyDat2pmlPart}
\alias{multiphyDat2pmlPart}
\alias{pmlPart2multiPhylo}
\alias{pmlPart}
\title{Partition model.}
\usage{
multiphyDat2pmlPart(x, rooted = FALSE, ...)
pmlPart2multiPhylo(x)
pmlPart(formula, object, control = pml.control(epsilon = 1e-08, maxit = 10,
trace = 1), model = NULL, rooted = FALSE, ...)
}
\arguments{
\item{x}{an object of class \code{pmlPart}}
\item{rooted}{Are the gene trees rooted (ultrametric) or unrooted.}
\item{\dots}{Further arguments passed to or from other methods.}
\item{formula}{a formula object (see details).}
\item{object}{an object of class \code{pml} or a list of objects of class
\code{pml} .}
\item{control}{A list of parameters for controlling the fitting process.}
\item{model}{A vector containing the models containing a model for each
partition.}
}
\value{
\code{kcluster} returns a list with elements
\item{logLik}{log-likelihood of the fit} \item{trees}{a list of all trees
during the optimization.} \item{object}{an object of class \code{"pml"} or
\code{"pmlPart"}}
}
\description{
Model to estimate phylogenies for partitioned data.
}
\details{
The \code{formula} object allows to specify which parameter get optimized.
The formula is generally of the form \code{edge + bf + Q ~ rate + shape +
\dots{}}, on the left side are the parameters which get optimized over all
partitions, on the right the parameter which are optimized specific to each
partition. The parameters available are \code{"nni", "bf", "Q", "inv",
"shape", "edge", "rate"}. Each parameters can be used only once in the
formula. \code{"rate"} is only available for the right side of the formula.
For partitions with different edge weights, but same topology, \code{pmlPen}
can try to find more parsimonious models (see example).
\code{pmlPart2multiPhylo} is a convenience function to extract the trees out
of a \code{pmlPart} object.
}
\examples{
data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fits <- optim.pml(fit)
weight=xtabs(~ index+genes,attr(yeast, "index"))[,1:10]
sp <- pmlPart(edge ~ rate + inv, fits, weight=weight)
sp
\dontrun{
sp2 <- pmlPart(~ edge + inv, fits, weight=weight)
sp2
AIC(sp2)
sp3 <- pmlPen(sp2, lambda = 2)
AIC(sp3)
}
}
\seealso{
\code{\link{pml}},\code{\link{pmlCluster}},\code{\link{pmlMix}},\code{\link{SH.test}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/pmlCluster.Rd 0000644 0001762 0000144 00000005060 13707232051 015263 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pmlPart.R
\name{pmlCluster}
\alias{pmlCluster}
\title{Stochastic Partitioning}
\usage{
pmlCluster(formula, fit, weight, p = 1:5, part = NULL, nrep = 10,
control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1), ...)
}
\arguments{
\item{formula}{a formula object (see details).}
\item{fit}{an object of class \code{pml}.}
\item{weight}{\code{weight} is matrix of frequency of site patterns for all
genes.}
\item{p}{number of clusters.}
\item{part}{starting partition, otherwise a random partition is generated.}
\item{nrep}{number of replicates for each p.}
\item{control}{A list of parameters for controlling the fitting process.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
\code{pmlCluster} returns a list with elements
\item{logLik}{log-likelihood of the fit} \item{trees}{a list of all trees
during the optimization.} \item{fits}{fits for the final partitions}
}
\description{
Stochastic Partitioning of genes into p cluster.
}
\details{
The \code{formula} object allows to specify which parameter get optimized.
The formula is generally of the form \code{edge + bf + Q ~ rate + shape +
\dots{}}, on the left side are the parameters which get optimized over all
cluster, on the right the parameter which are optimized specific to each
cluster. The parameters available are \code{"nni", "bf", "Q", "inv",
"shape", "edge", "rate"}. Each parameter can be used only once in the
formula. There are also some restriction on the combinations how parameters
can get used. \code{"rate"} is only available for the right side. When
\code{"rate"} is specified on the left hand side \code{"edge"} has to be
specified (on either side), if \code{"rate"} is specified on the right hand
side it follows directly that \code{edge} is too.
}
\examples{
\dontrun{
data(yeast)
dm <- dist.logDet(yeast)
tree <- NJ(dm)
fit <- pml(tree,yeast)
fit <- optim.pml(fit)
weight <- xtabs(~ index+genes,attr(yeast, "index"))
set.seed(1)
sp <- pmlCluster(edge~rate, fit, weight, p=1:4)
sp
SH.test(sp)
}
}
\references{
K. P. Schliep (2009). Some Applications of statistical
phylogenetics (PhD Thesis)
Lanfear, R., Calcott, B., Ho, S.Y.W. and Guindon, S. (2012) PartitionFinder:
Combined Selection of Partitioning Schemes and Substitution Models for
Phylogenetic Analyses. \emph{Molecular Biology and Evolution}, \bold{29(6)},
1695-1701
}
\seealso{
\code{\link{pml}},\code{\link{pmlPart}},\code{\link{pmlMix}},\code{\link{SH.test}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/phyDat.Rd 0000644 0001762 0000144 00000012460 13707232051 014364 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phyDat.R
\name{phyDat}
\alias{phyDat}
\alias{as.phyDat.character}
\alias{as.phyDat.data.frame}
\alias{as.phyDat.matrix}
\alias{as.MultipleAlignment}
\alias{as.MultipleAlignment.phyDat}
\alias{cbind.phyDat}
\alias{c.phyDat}
\alias{acgt2ry}
\alias{removeUndeterminedSites}
\alias{phyDat2MultipleAlignment}
\alias{as.phyDat}
\alias{as.phyDat.factor}
\alias{as.phyDat.DNAbin}
\alias{as.phyDat.alignment}
\alias{phyDat2alignment}
\alias{as.phyDat.MultipleAlignment}
\alias{as.character.phyDat}
\alias{as.data.frame.phyDat}
\alias{as.DNAbin.phyDat}
\alias{as.AAbin.phyDat}
\alias{baseFreq}
\alias{subset.phyDat}
\alias{[.phyDat}
\alias{unique.phyDat}
\alias{allSitePattern}
\alias{genlight2phyDat}
\alias{image.phyDat}
\title{Conversion among Sequence Formats}
\usage{
phyDat(data, type = "DNA", levels = NULL, return.index = TRUE, ...)
as.phyDat(x, ...)
\method{as.phyDat}{factor}(x, ...)
\method{as.phyDat}{DNAbin}(x, ...)
\method{as.phyDat}{alignment}(x, type = "DNA", ...)
phyDat2alignment(x)
\method{as.phyDat}{MultipleAlignment}(x, ...)
\method{as.MultipleAlignment}{phyDat}(x, ...)
acgt2ry(obj)
\method{as.character}{phyDat}(x, allLevels = TRUE, ...)
\method{as.data.frame}{phyDat}(x, ...)
\method{as.DNAbin}{phyDat}(x, ...)
\method{as.AAbin}{phyDat}(x, ...)
baseFreq(obj, freq = FALSE, all = FALSE, drop.unused.levels = FALSE)
\method{subset}{phyDat}(x, subset, select, site.pattern = TRUE, ...)
\method{[}{phyDat}(x, i, j, ..., drop = FALSE)
\method{unique}{phyDat}(x, incomparables = FALSE, identical = TRUE, ...)
removeUndeterminedSites(x, ...)
allSitePattern(n, levels = c("a", "c", "g", "t"), names = NULL)
genlight2phyDat(x, ambiguity = NA)
\method{image}{phyDat}(x, ...)
}
\arguments{
\item{data}{An object containing sequences.}
\item{type}{Type of sequences ("DNA", "AA", "CODON" or "USER").}
\item{levels}{Level attributes.}
\item{return.index}{If TRUE returns a index of the site patterns.}
\item{...}{further arguments passed to or from other methods.}
\item{x}{An object containing sequences.}
\item{obj}{as object of class phyDat}
\item{allLevels}{return original data.}
\item{freq}{logical, if 'TRUE', frequencies or counts are returned otherwise
proportions}
\item{all}{all a logical; if all = TRUE, all counts of bases, ambiguous
codes, missing data, and alignment gaps are returned as defined in the
contrast.}
\item{drop.unused.levels}{logical, drop unused levels}
\item{subset}{a subset of taxa.}
\item{select}{a subset of characters.}
\item{site.pattern}{select site pattern or sites.}
\item{i, j}{indices of the rows and/or columns to select or to drop. They
may be numeric, logical, or character (in the same way than for standard R
objects).}
\item{drop}{for compatibility with the generic (unused).}
\item{incomparables}{for compatibility with unique.}
\item{identical}{if TRUE (default) sequences have to be identical, if FALSE
sequences are considered duplicates if distance between sequences is zero
(happens frequently with ambiguous sites).}
\item{n}{Number of sequences.}
\item{names}{Names of sequences.}
\item{ambiguity}{character for ambiguous character and no contrast is
provided.}
}
\value{
The functions return an object of class \code{phyDat}.
}
\description{
These functions transform several DNA formats into the \code{phyDat} format.
\code{allSitePattern} generates an alignment of all possible site patterns.
}
\details{
If \code{type} "USER" a vector has to be give to \code{levels}. For example
c("a", "c", "g", "t", "-") would create a data object that can be used in
phylogenetic analysis with gaps as fifth state. There is a more detailed
example for specifying "USER" defined data formats in the vignette
"phangorn-specials".
\code{allSitePattern} returns all possible site patterns and can be useful
in simulation studies. For further details see the vignette
phangorn-specials.
The generic function \code{c} can be used to to combine sequences and
\code{unique} to get all unique sequences or unique haplotypes.
\code{acgt2ry} converts a \code{phyDat} object of nucleotides into an binary
ry-coded dataset.
}
\examples{
data(Laurasiatherian)
class(Laurasiatherian)
Laurasiatherian
# base frequencies
baseFreq(Laurasiatherian)
baseFreq(Laurasiatherian, all=TRUE)
baseFreq(Laurasiatherian, freq=TRUE)
# subsetting phyDat objects
# the first 5 sequences
subset(Laurasiatherian, subset=1:5)
# the first 5 characters
subset(Laurasiatherian, select=1:5, site.pattern = FALSE)
# subsetting with []
Laurasiatherian[1:5, 1:20]
# short for
subset(Laurasiatherian, subset=1:5, select=1:20, site.pattern = FALSE)
# the first 5 site patterns (often more than 5 characters)
subset(Laurasiatherian, select=1:5, site.pattern = TRUE)
# transform into old ape format
LauraChar <- as.character(Laurasiatherian)
# and back
Laura <- phyDat(LauraChar)
all.equal(Laurasiatherian, Laura)
# Compute all possible site patterns
# for nucleotides there $4 ^ (number of tips)$ patterns
allSitePattern(5)
}
\seealso{
\code{\link{DNAbin}}, \code{\link{as.DNAbin}},
\code{\link{read.dna}}, \code{\link{read.aa}}, \code{\link{read.nexus.data}}
and the chapter 1 in the \code{vignette("phangorn-specials",
package="phangorn")} and the example of \code{\link{pmlMix}} for the use of
\code{allSitePattern}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/hadamard.Rd 0000644 0001762 0000144 00000005006 14140463611 014672 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hadamard.R
\name{hadamard}
\alias{hadamard}
\alias{fhm}
\alias{h4st}
\alias{h2st}
\title{Hadamard Matrices and Fast Hadamard Multiplication}
\usage{
hadamard(x)
fhm(v)
h4st(obj, levels = c("a", "c", "g", "t"))
h2st(obj, eps = 0.001)
}
\arguments{
\item{x}{a vector of length \eqn{2^n}, where n is an integer.}
\item{v}{a vector of length \eqn{2^n}, where n is an integer.}
\item{obj}{a data.frame or character matrix, typical a sequence alignment.}
\item{levels}{levels of the sequences.}
\item{eps}{Threshold value for splits.}
}
\value{
\code{hadamard} returns a Hadamard matrix. \code{fhm} returns the
fast Hadamard multiplication.
}
\description{
A collection of functions to perform Hadamard conjugation. %Hv of a
Hadamard matrix H with a vector v using fast Hadamard multiplication.
}
\details{
\code{h2st} and \code{h4st} perform Hadamard conjugation for 2-state
(binary, RY-coded) or 4-state (DNA/RNA) data. \code{write.nexus.splits}
writes splits returned from \code{h2st} or
\code{\link[phangorn]{distanceHadamard}} to a nexus file, which can be
processed by Spectronet or SplitsTree.
}
\examples{
H <- hadamard(3)
v <- 1:8
H \%*\% v
fhm(v)
data(yeast)
# RY-coding
dat_ry <- acgt2ry(yeast)
fit2 <- h2st(dat_ry)
lento(fit2)
# write.nexus.splits(fit2, file = "test.nxs")
# read this file into Spectronet or SplitsTree to show the network
\dontrun{
dat <- as.character(yeast)
dat4 <- phyDat(dat, type="USER", levels=c("a","c", "g", "t"), ambiguity=NULL)
fit4 <- h4st(dat4)
old.par <- par(no.readonly = TRUE)
par(mfrow=c(3,1))
lento(fit4[[1]], main="Transversion")
lento(fit4[[2]], main="Transition 1")
lento(fit4[[3]], main="Transition 2")
par(old.par)
}
}
\references{
Hendy, M.D. (1989). The relationship between simple evolutionary
tree models and observable sequence data. \emph{Systematic Zoology},
\bold{38} 310--321.
Hendy, M. D. and Penny, D. (1993). Spectral Analysis of Phylogenetic Data.
\emph{Journal of Classification}, \bold{10}, 5--24.
Hendy, M. D. (2005). Hadamard conjugation: an analytical tool for
phylogenetics. In O. Gascuel, editor, \emph{Mathematics of evolution and
phylogeny}, Oxford University Press, Oxford
Waddell P. J. (1995). Statistical methods of phylogenetic analysis:
Including hadamard conjugation, LogDet transforms, and maximum likelihood.
\emph{PhD thesis}.
}
\seealso{
\code{\link{distanceHadamard}}, \code{\link{lento}},
\code{\link{plot.networx}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/designTree.Rd 0000644 0001762 0000144 00000004225 14140462313 015222 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distTree.R
\name{designTree}
\alias{designTree}
\alias{nnls.tree}
\alias{nnls.phylo}
\alias{nnls.splits}
\alias{nnls.networx}
\alias{designSplits}
\title{Compute a design matrix or non-negative LS}
\usage{
designTree(tree, method = "unrooted", sparse = FALSE, ...)
nnls.tree(dm, tree, rooted = FALSE, trace = 1, weight = NULL, balanced = FALSE)
nnls.phylo(x, dm, rooted = FALSE, trace = 0, ...)
nnls.splits(x, dm, trace = 0)
nnls.networx(x, dm)
designSplits(x, splits = "all", ...)
}
\arguments{
\item{tree}{an object of class \code{phylo}}
\item{method}{design matrix for an "unrooted" or "rooted" ultrametric tree.}
\item{sparse}{return a sparse design matrix.}
\item{\dots}{further arguments, passed to other methods.}
\item{dm}{a distance matrix.}
\item{rooted}{compute a "rooted" or "unrooted" tree.}
\item{trace}{defines how much information is printed during optimization.}
\item{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.}
\item{balanced}{use weights as in balanced fastME}
\item{x}{number of taxa.}
\item{splits}{one of "all", "star".}
}
\value{
\code{nnls.tree} return a tree, i.e. an object of class
\code{phylo}. \code{designTree} and \code{designSplits} a matrix, possibly
sparse.
}
\description{
\code{nnls.tree} estimates the branch length using non-negative least
squares given a tree and a distance matrix. \code{designTree} and
\code{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. %\code{designTree} also
computes a contrast matrix if the method is "rooted".
}
\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)
}
\seealso{
\code{\link[ape]{fastme}},
\code{\link[phangorn]{distanceHadamard}},
\code{\link[phangorn]{splitsNetwork}}, \code{\link[phangorn]{upgma}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/densiTree.Rd 0000644 0001762 0000144 00000010210 14140455403 015044 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Densi.R
\name{densiTree}
\alias{densiTree}
\title{Plots a densiTree.}
\usage{
densiTree(x, type = "cladogram", alpha = 1/length(x), consensus = NULL,
direction = "rightwards", optim = FALSE, scaleX = FALSE, col = 1,
width = 1, lty = 1, cex = 0.8, font = 3, tip.color = 1, adj = 0,
srt = 0, underscore = FALSE, label.offset = 0, scale.bar = TRUE,
jitter = list(amount = 0, random = TRUE), ...)
}
\arguments{
\item{x}{an object of class \code{multiPhylo}.}
\item{type}{a character string specifying the type of phylogeny, so far
"cladogram" (default) or "phylogram" are supported.}
\item{alpha}{parameter for semi-transparent colors.}
\item{consensus}{A tree or character vector which is used to define the order
of the tip labels.}
\item{direction}{a character string specifying the direction of the tree.
Four values are possible: "rightwards" (the default), "leftwards", "upwards",
and "downwards".}
\item{optim}{not yet used.}
\item{scaleX}{scale trees to have identical heights.}
\item{col}{a scalar or vector giving the colours used to draw the edges for
each plotted phylogeny. These are taken to be in the same order than input
trees x. If fewer colours are given than the number of trees, then the
colours are recycled.}
\item{width}{edge width.}
\item{lty}{line type.}
\item{cex}{a numeric value giving the factor scaling of the tip labels.}
\item{font}{an integer specifying the type of font for the labels: 1 (plain text),
2 (bold), 3 (italic, the default), or 4 (bold italic).}
\item{tip.color}{color of the tip labels.}
\item{adj}{a numeric specifying the justification of the text strings of the
labels: 0 (left-justification), 0.5 (centering), or 1 (right-justification).}
\item{srt}{a numeric giving how much the labels are rotated in degrees.}
\item{underscore}{a logical specifying whether the underscores in tip labels
should be written as spaces (the default) or left as are (if TRUE).}
\item{label.offset}{a numeric giving the space between the nodes and the tips of the
phylogeny and their corresponding labels.}
\item{scale.bar}{a logical specifying whether add scale.bar to the plot.}
\item{jitter}{allows to shift trees. a list with two arguments: the amount of
jitter and random or equally spaced (see details below)}
\item{\dots}{further arguments to be passed to plot.}
}
\description{
An R function to plot trees similar to those produced by DensiTree.
}
\details{
If no consensus tree is provided \code{densiTree} computes a consensus tree,
and if the input trees have different labels a mrp.supertree as a backbone.
This should avoid too many unnecessary crossings of edges.
Trees should be rooted, other wise the output may not be visually pleasing.
\code{jitter} shifts trees a bit so that they are not exactly on top of each other.
If \code{amount == 0}, it is ignored. If \code{random=TRUE} the result of the permutation is
\code{runif(n, -amount, amount)}, otherwise \code{seq(-amount, amount, length=n)}, where \code{n <- length(x)}.
}
\examples{
data(Laurasiatherian)
set.seed(1)
bs <- bootstrap.phyDat(Laurasiatherian, FUN =
function(x) upgma(dist.hamming(x)), bs=25)
# cladogram nice to show topological differences
densiTree(bs, type="cladogram", col="blue")
densiTree(bs, type="phylogram", col="green", direction="downwards", width=2)
# plot five trees slightly shifted, no transparent color
densiTree(bs[1:5], type="phylogram", col=1:5, width=2, jitter=
list(amount=.3, random=FALSE), alpha=1)
\dontrun{
# phylograms are nice to show different age estimates
require(PhyloOrchard)
data(BinindaEmondsEtAl2007)
BinindaEmondsEtAl2007 <- .compressTipLabel(BinindaEmondsEtAl2007)
densiTree(BinindaEmondsEtAl2007, type="phylogram", col="red")
}
}
\references{
densiTree is inspired from the great
\href{https://www.cs.auckland.ac.nz/~remco/DensiTree/}{DensiTree} program of Remco
Bouckaert.
Remco R. Bouckaert (2010) DensiTree: making sense of sets of phylogenetic
trees \emph{Bioinformatics}, \bold{26 (10)}, 1372-1373.
}
\seealso{
\code{\link{plot.phylo}}, \code{\link{plot.networx}}, \code{\link{jitter}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{plot}
phangorn/man/pml.fit.Rd 0000644 0001762 0000144 00000005122 14107665636 014517 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phylo.R
\name{lli}
\alias{lli}
\alias{edQt}
\alias{pml.free}
\alias{pml.init}
\alias{pml.fit}
\title{Internal maximum likelihood functions.}
\usage{
lli(data, tree = NULL, ...)
edQt(Q = c(1, 1, 1, 1, 1, 1), bf = c(0.25, 0.25, 0.25, 0.25))
pml.free()
pml.init(data, k = 1L)
pml.fit(tree, data, bf = rep(1/length(levels), length(levels)), shape = 1,
k = 1, Q = rep(1, length(levels) * (length(levels) - 1)/2),
levels = attr(data, "levels"), inv = 0, rate = 1, g = NULL,
w = NULL, eig = NULL, INV = NULL, ll.0 = NULL, llMix = NULL,
wMix = 0, ..., site = FALSE, Mkv = FALSE, site.rate = "gamma")
}
\arguments{
\item{data}{An alignment, object of class \code{phyDat}.}
\item{tree}{A phylogenetic \code{tree}, object of class \code{phylo}.}
\item{\dots}{Further arguments passed to or from other methods.}
\item{Q}{A vector containing the lower triangular part of the rate matrix.}
\item{bf}{Base frequencies.}
\item{k}{Number of intervals of the discrete gamma distribution.}
\item{shape}{Shape parameter of the gamma distribution.}
\item{levels}{The alphabet used e.g. c("a", "c", "g", "t") for DNA}
\item{inv}{Proportion of invariable sites.}
\item{rate}{Rate.}
\item{g}{vector of quantiles (default is NULL)}
\item{w}{vector of probabilities (default is NULL)}
\item{eig}{Eigenvalue decomposition of Q}
\item{INV}{Sparse representation of invariant sites}
\item{ll.0}{default is NULL}
\item{llMix}{default is NULL}
\item{wMix}{default is NULL}
\item{site}{return the log-likelihood or vector of sitewise likelihood
values}
\item{Mkv}{indicate if Lewis' Mkv should be estimated.}
\item{site.rate}{Indicates what type of gamma distribution to use. Options
are "gamma" approach of Yang 1994 (default), "quadrature" after the Laguerre
quadrature approach of Felsenstein 2001 and "freerate" .}
}
\value{
\code{pml.fit} returns the log-likelihood.
}
\description{
These functions are internally used for the likelihood computations in
\code{pml} or \code{optim.pml}.
}
\details{
These functions are exported to be used in different packages so far only in
the package coalescentMCMC, but are not intended for end user. Most of the
functions call C code and are far less forgiving if the import is not what
they expect than \code{pml}.
}
\references{
Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a
maximum likelihood approach. \emph{Journal of Molecular Evolution},
\bold{17}, 368--376.
}
\seealso{
\code{\link{pml}, \link{pmlPart}, \link{pmlMix}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/nni.Rd 0000644 0001762 0000144 00000002256 13707232051 013721 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treeRearrangement.R
\name{nni}
\alias{nni}
\alias{rNNI}
\alias{rSPR}
\title{Tree rearrangements.}
\usage{
nni(tree)
rNNI(tree, moves = 1, n = length(moves))
rSPR(tree, moves = 1, n = length(moves), k = NULL)
}
\arguments{
\item{tree}{A phylogenetic \code{tree}, object of class \code{phylo}.}
\item{moves}{Number of tree rearrangements to be transformed on a tree. Can
be a vector}
\item{n}{Number of trees to be simulated.}
\item{k}{If defined just SPR of distance k are performed.}
}
\value{
an object of class multiPhylo.
}
\description{
\code{nni} returns a list of all trees which are one nearest neighbor
interchange away. \code{rNNI} and \code{rSPR} are two methods which simulate
random trees which are a specified number of rearrangement apart from the
input tree. Both methods assume that the input tree is bifurcating. These
methods may be useful in simulation studies.
}
\examples{
tree <- rtree(20, rooted = FALSE)
trees1 <- nni(tree)
trees2 <- rSPR(tree, 2, 10)
}
\seealso{
\code{\link{allTrees}}, \code{\link{SPR.dist}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/simSeq.Rd 0000644 0001762 0000144 00000007250 14017247617 014406 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/simSeq.R
\name{simSeq}
\alias{simSeq}
\alias{simSeq.phylo}
\alias{simSeq.pml}
\title{Simulate sequences.}
\usage{
simSeq(x, ...)
\method{simSeq}{phylo}(x, l = 1000, Q = NULL, bf = NULL,
rootseq = NULL, type = "DNA", model = NULL, levels = NULL,
rate = 1, ancestral = FALSE, code = 1, ...)
\method{simSeq}{pml}(x, ancestral = FALSE, ...)
}
\arguments{
\item{x}{a phylogenetic tree \code{tree}, i.e. an object of class
\code{phylo} or and object of class \code{pml}.}
\item{\dots}{Further arguments passed to or from other methods.}
\item{l}{The length of the sequence to simulate.}
\item{Q}{The rate matrix.}
\item{bf}{Base frequencies.}
\item{rootseq}{A vector of length \code{l} containing the root sequence.
If not provided, the root sequence is randomly generated.}
\item{type}{Type of sequences ("DNA", "AA", "CODON" or "USER").}
\item{model}{Amino acid model of evolution to employ, for example "WAG",
"JTT", "Dayhoff" or "LG". For a full list of supported models, type
\code{phangorn:::.aamodels}. Ignored if type is not equal to "AA".}
\item{levels}{A character vector of the different character tokens.
Ignored unless type = "USER".}
\item{rate}{A numerical value greater than zero giving the mutation rate
or scaler for edge lengths.}
\item{ancestral}{Logical specifying whether to return ancestral sequences.}
\item{code}{The ncbi genetic code number for translation (see details). By
default the standard genetic code is used.}
}
\value{
\code{simSeq} returns an object of class phyDat.
}
\description{
Simulate sequences from a given evolutionary tree.
}
\details{
\code{simSeq} is a generic function to simulate sequence alignments
along a phylogeny. It is quite flexible and can generate DNA, RNA,
amino acids, codon, morphological or binary sequences.
simSeq can take as input a phylogenetic tree of class \code{phylo},
or a \code{pml} object; it will return an object of class \code{phyDat}.
There is also a more low level
version, which lacks rate variation, but one can combine different
alignments with their own rates (see example). The rate parameter acts like
a scaler for the edge lengths.
For codon models \code{type="CODON"}, two additional arguments \code{dnds}
for the dN/dS ratio and \code{tstv} for the transition transversion ratio
can be supplied.
\strong{Defaults:}
If \code{x} is a tree of class \code{phylo}, then sequences will be generated
with the default Jukes-Cantor DNA model (\code{"JC"}).
If \code{bf} is not specified, then all states will be treated as equally
probable.
If \code{Q} is not specified, then a uniform rate matrix will be employed.
}
\examples{
\dontrun{
data(Laurasiatherian)
tree <- nj(dist.ml(Laurasiatherian))
fit <- pml(tree, Laurasiatherian, k=4)
fit <- optim.pml(fit, optNni=TRUE, model="GTR", optGamma=TRUE)
data <- simSeq(fit)
}
tree <- rtree(5)
plot(tree)
nodelabels()
# Example for simple DNA alignment
data <- simSeq(tree, l = 10, type="DNA", bf=c(.1,.2,.3,.4), Q=1:6,
ancestral=TRUE)
as.character(data)
# Example to simulate discrete Gamma rate variation
rates <- discrete.gamma(1,4)
data1 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[1])
data2 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[2])
data3 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[3])
data4 <- simSeq(tree, l = 100, type="AA", model="WAG", rate=rates[4])
data <- c(data1,data2, data3, data4)
write.phyDat(data, file="temp.dat", format="sequential", nbcol = -1,
colsep = "")
unlink("temp.dat")
}
\seealso{
\code{\link{phyDat}}, \code{\link{pml}}, \code{\link{SOWH.test}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/phangorn-package.Rd 0000644 0001762 0000144 00000003017 14134551471 016343 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phangorn-package.R
\docType{package}
\name{phangorn-package}
\alias{phangorn}
\alias{phangorn-package}
\title{phangorn: Phylogenetic Reconstruction and Analysis}
\description{
Allows for estimation of phylogenetic trees and networks using Maximum Likelihood, Maximum Parsimony, distance methods and Hadamard conjugation. Offers methods for tree comparison, model selection and visualization of phylogenetic networks as described in Schliep et al. (2017) .
}
\seealso{
Useful links:
\itemize{
\item \url{https://github.com/KlausVigo/phangorn}
\item Report bugs at \url{https://github.com/KlausVigo/phangorn/issues}
}
}
\author{
\strong{Maintainer}: Klaus Schliep \email{klaus.schliep@gmail.com} (\href{https://orcid.org/0000-0003-2941-0161}{ORCID})
Authors:
\itemize{
\item Emmanuel Paradis (\href{https://orcid.org/0000-0003-3092-2199}{ORCID})
\item Leonardo de Oliveira Martins (\href{https://orcid.org/0000-0001-5247-1320}{ORCID})
\item Alastair Potts
\item Tim W. White
}
Other contributors:
\itemize{
\item Cyrill Stachniss [contributor]
\item Michelle Kendall \email{m.kendall@imperial.ac.uk} [contributor]
\item Keren Halabi [contributor]
\item Richel Bilderbeek [contributor]
\item Kristin Winchell [contributor]
\item Liam Revell [contributor]
\item Mike Gilchrist [contributor]
\item Jeremy Beaulieu [contributor]
\item Brian O'Meara [contributor]
\item Long Qu [contributor]
}
}
\keyword{internal}
phangorn/man/read.nexus.splits.Rd 0000644 0001762 0000144 00000004605 14140463611 016526 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read.nexus.splits.R
\name{read.nexus.splits}
\alias{read.nexus.splits}
\alias{write.nexus.splits}
\alias{write.nexus.networx}
\alias{read.nexus.networx}
\alias{write.splits}
\title{Function to import and export splits and networks}
\usage{
read.nexus.splits(file)
write.nexus.splits(obj, file = "", weights = NULL, taxa = TRUE, append = FALSE)
write.nexus.networx(obj, file = "", taxa = TRUE, splits = TRUE, append = FALSE)
read.nexus.networx(file, splits = TRUE)
write.splits(x, file = "", zero.print = ".", one.print = "|",
print.labels = TRUE, ...)
}
\arguments{
\item{file}{a file name.}
\item{obj}{An object of class splits.}
\item{weights}{Edge weights.}
\item{taxa}{logical. If TRUE a taxa block is added}
\item{append}{logical. If TRUE the nexus blocks will be added to a file.}
\item{splits}{logical. If TRUE the nexus blocks will be added to a file.}
\item{x}{An object of class splits.}
\item{zero.print}{character which should be printed for zeros.}
\item{one.print}{character which should be printed for ones.}
\item{print.labels}{logical. If TRUE labels are printed.}
\item{\dots}{Further arguments passed to or from other methods.}
\item{labels}{names of taxa.}
}
\value{
\code{write.nexus.splits} and \code{write.nexus.networx} write out
the \code{splits} and \code{networx} object to read with
other software like SplitsTree.
\code{read.nexus.splits} and \code{read.nexus.networx} return an \code{splits}
and \code{networx} object.
}
\description{
\code{read.nexus.splits}, \code{write.nexus.splits}, \code{read.nexus.networx}, \code{write.nexus.networx}
can be used to import and export splits and networks with nexus format
and allow to exchange these object with other software like SplitsTree.
\code{write.splits} returns a human readable output.
}
\note{
\code{read.nexus.splits} reads in the splits block of a nexus file. It
assumes that different co-variables are tab delimited and the bipartition
are separated with white-space. Comments in square brackets are ignored.
}
\examples{
(sp <- as.splits(rtree(5)))
write.nexus.splits(sp)
spl <- allCircularSplits(5)
plot(as.networx(spl), "2D")
write.splits(spl, print.labels = FALSE)
}
\seealso{
\code{\link{prop.part}}, \code{\link{lento}},
\code{\link{as.splits}}, \code{\link{as.networx}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/parsimony.Rd 0000644 0001762 0000144 00000007326 14140462313 015157 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ancestral_pml.R, R/fitch64.R, R/parsimony.R,
% R/sankoff.R
\name{acctran}
\alias{acctran}
\alias{fitch}
\alias{random.addition}
\alias{parsimony}
\alias{optim.parsimony}
\alias{sankoff}
\alias{pratchet}
\title{Parsimony tree.}
\usage{
acctran(tree, data)
fitch(tree, data, site = "pscore")
random.addition(data, tree = NULL, method = "fitch")
parsimony(tree, data, method = "fitch", cost = NULL, site = "pscore")
optim.parsimony(tree, data, method = "fitch", cost = NULL, trace = 1,
rearrangements = "SPR", ...)
pratchet(data, start = NULL, method = "fitch", maxit = 1000,
minit = 10, k = 10, trace = 1, all = FALSE, rearrangements = "SPR",
perturbation = "ratchet", ...)
sankoff(tree, data, cost = NULL, site = "pscore")
}
\arguments{
\item{tree}{tree to start the nni search from.}
\item{data}{A object of class phyDat containing sequences.}
\item{site}{return either 'pscore' or 'site' wise parsimony scores.}
\item{method}{one of 'fitch' or 'sankoff'.}
\item{cost}{A cost matrix for the transitions between two states.}
\item{trace}{defines how much information is printed during optimization.}
\item{rearrangements}{SPR or NNI rearrangements.}
\item{...}{Further arguments passed to or from other methods (e.g.
model="sankoff" and cost matrix).}
\item{start}{a starting tree can be supplied.}
\item{maxit}{maximum number of iterations in the ratchet.}
\item{minit}{minimum number of iterations in the ratchet.}
\item{k}{number of rounds ratchet is stopped, when there is no improvement.}
\item{all}{return all equally good trees or just one of them.}
\item{perturbation}{whether to use "ratchet", "random_addition" or
"stochastic" (nni) for shuffling the tree.}
}
\value{
\code{parsimony} returns the maximum parsimony score (pscore).
\code{optim.parsimony} returns a tree after NNI rearrangements.
\code{pratchet} returns a tree or list of trees containing the best tree(s)
found during the search. \code{acctran} returns a tree with edge length
according to the ACCTRAN criterion.
}
\description{
\code{parsimony} returns the parsimony score of a tree using either the
sankoff or the fitch algorithm. \code{optim.parsimony} tries to find the
maximum parsimony tree using either Nearest Neighbor Interchange (NNI)
rearrangements or sub tree pruning and regrafting (SPR). \code{pratchet}
implements the parsimony ratchet (Nixon, 1999) and is the preferred way to
search for the best tree. \code{random.addition} can be used to produce
starting trees.
}
\details{
The "SPR" rearrangements are so far only available for the "fitch" method,
"sankoff" only uses "NNI". The "fitch" algorithm only works correct for
binary trees.
}
\examples{
set.seed(3)
data(Laurasiatherian)
dm <- dist.hamming(Laurasiatherian)
tree <- NJ(dm)
parsimony(tree, Laurasiatherian)
treeRA <- random.addition(Laurasiatherian)
treeNNI <- optim.parsimony(tree, Laurasiatherian)
treeRatchet <- pratchet(Laurasiatherian, start=tree, maxit=100,
minit=5, k=5, trace=0)
# assign edge length
treeRatchet <- acctran(treeRatchet, Laurasiatherian)
plot(midpoint(treeRatchet))
add.scale.bar(0,0, length=100)
parsimony(c(tree,treeNNI, treeRatchet), Laurasiatherian)
}
\references{
Felsenstein, J. (2004). \emph{Inferring Phylogenies}. Sinauer
Associates, Sunderland.
Nixon, K. (1999) The Parsimony Ratchet, a New Method for Rapid Parsimony
Analysis. \emph{Cladistics} \bold{15}, 407-414
}
\seealso{
\code{\link{bab}}, \code{\link{CI}}, \code{\link{RI}}, \code{\link{ancestral.pml}},
\code{\link{nni}}, \code{\link{NJ}}, \code{\link{pml}}, \code{\link{getClans}}
,\code{\link{ancestral.pars}}, \code{\link{bootstrap.pml}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/discrete.gamma.Rd 0000644 0001762 0000144 00000005152 14030677772 016033 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/discrete.gamma.R
\name{discrete.gamma}
\alias{discrete.gamma}
\alias{plot_gamma_plus_inv}
\alias{plotRates}
\title{Discrete Gamma function}
\usage{
discrete.gamma(alpha, k)
plot_gamma_plus_inv(shape = 1, inv = 0, k = 4, discrete = TRUE,
cdf = TRUE, append = FALSE, xlab = "x", ylab = ifelse(cdf, "F(x)",
"f(x)"), xlim = NULL, verticals = FALSE, edge.length = NULL,
site.rate = "gamma", ...)
plotRates(obj, cdf.color = "blue", main = "cdf", ...)
}
\arguments{
\item{alpha}{Shape parameter of the gamma distribution.}
\item{k}{Number of intervals of the discrete gamma distribution.}
\item{shape}{Shape parameter of the gamma distribution.}
\item{inv}{Proportion of invariable sites.}
\item{discrete}{logical whether to plot discrete (default) or continuous pdf or
cdf.}
\item{cdf}{logical whether to plot the cumulative distribution function
or density / probability function.}
\item{append}{logical; if TRUE only add to an existing plot.}
\item{xlab}{a label for the x axis, defaults to a description of x.}
\item{ylab}{a label for the y axis, defaults to a description of y.}
\item{xlim}{the x limits of the plot.}
\item{verticals}{logical; if TRUE, draw vertical lines at steps.}
\item{edge.length}{Total edge length (sum of all edges in a tree).}
\item{site.rate}{Indicates what type of gamma distribution to use. Options
are "gamma" (Yang 1994) and "gamma_quadrature" using Laguerre quadrature
approach of Felsenstein (2001)}
\item{\dots}{Further arguments passed to or from other methods.}
\item{obj}{an object of class pml}
\item{cdf.color}{color of the cdf.}
\item{main}{a main title for the plot.}
}
\value{
\code{discrete.gamma} returns a matrix.
}
\description{
\code{discrete.gamma} internally used for the likelihood computations in
\code{pml} or \code{optim.pml}. It is useful to understand how it works
for simulation studies or in cases where .
}
\details{
These functions are exported to be used in different packages so far only in
the package coalescentMCMC, but are not intended for end user. Most of the
functions call C code and are far less forgiving if the import is not what
they expect than \code{pml}.
}
\examples{
discrete.gamma(1, 4)
old.par <- par(no.readonly = TRUE)
par(mfrow = c(2,1))
plot_gamma_plus_inv(shape=2, discrete = FALSE, cdf=FALSE)
plot_gamma_plus_inv(shape=2, append = TRUE, cdf=FALSE)
plot_gamma_plus_inv(shape=2, discrete = FALSE)
plot_gamma_plus_inv(shape=2, append = TRUE)
par(old.par)
}
\seealso{
\code{\link{pml.fit}, \link{stepfun}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/pml.Rd 0000644 0001762 0000144 00000022376 14140462313 013730 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phylo.R
\name{pml.control}
\alias{pml.control}
\alias{pml}
\alias{optim.pml}
\title{Likelihood of a tree.}
\usage{
pml.control(epsilon = 1e-08, maxit = 10, trace = 1, tau = 1e-08)
pml(tree, data, bf = NULL, Q = NULL, inv = 0, k = 1, shape = 1,
rate = 1, model = NULL, site.rate = "gamma", ...)
optim.pml(object, optNni = FALSE, optBf = FALSE, optQ = FALSE,
optInv = FALSE, optGamma = FALSE, optEdge = TRUE, optRate = FALSE,
optRooted = FALSE, control = pml.control(epsilon = 1e-08, maxit = 10,
trace = 1L, tau = 1e-08), model = NULL, rearrangement = ifelse(optNni,
"NNI", "none"), subs = NULL, ratchet.par = list(iter = 20L, maxit = 200L,
minit = 100L, prop = 1/2), ...)
}
\arguments{
\item{epsilon}{Stop criterion for optimization (see details).}
\item{maxit}{Maximum number of iterations (see details).}
\item{trace}{Show output during optimization (see details).}
\item{tau}{minimal edge length.}
\item{tree}{A phylogenetic \code{tree}, object of class \code{phylo}.}
\item{data}{An alignment, object of class \code{phyDat}.}
\item{bf}{Base frequencies (see details).}
\item{Q}{A vector containing the lower triangular part of the rate matrix.}
\item{inv}{Proportion of invariable sites.}
\item{k}{Number of intervals of the discrete gamma distribution.}
\item{shape}{Shape parameter of the gamma distribution.}
\item{rate}{Rate.}
\item{model}{allows to choose an amino acid models or nucleotide model, see
details.}
\item{site.rate}{Indicates what type of gamma distribution to use. Options
are "gamma" approach of Yang 1994 (default), "quadrature" after the Laguerre
quadrature approach of Felsenstein 2001 or "freerate".}
\item{\dots}{Further arguments passed to or from other methods.}
\item{object}{An object of class \code{pml}.}
\item{optNni}{Logical value indicating whether topology gets optimized (NNI).}
\item{optBf}{Logical value indicating whether base frequencies gets
optimized.}
\item{optQ}{Logical value indicating whether rate matrix gets optimized.}
\item{optInv}{Logical value indicating whether proportion of variable size
gets optimized.}
\item{optGamma}{Logical value indicating whether gamma rate parameter gets
optimized.}
\item{optEdge}{Logical value indicating the edge lengths gets optimized.}
\item{optRate}{Logical value indicating the overall rate gets optimized.}
\item{optRooted}{Logical value indicating if the edge lengths of a rooted
tree get optimized.}
\item{control}{A list of parameters for controlling the fitting process.}
\item{rearrangement}{type of tree tree rearrangements to perform, one of
"none", "NNI", "stochastic" or "ratchet"}
\item{subs}{A (integer) vector same length as Q to specify the optimization
of Q}
\item{ratchet.par}{search parameter for stochastic search}
}
\value{
\code{pml} or \code{optim.pml} return a list of class \code{pml},
some are useful for further computations like \item{tree}{the phylogenetic
tree.} \item{data}{the alignment.} \item{logLik}{Log-likelihood of the
tree.} \item{siteLik}{Site log-likelihoods.} \item{weight}{Weight of the
site patterns.}
}
\description{
\code{pml} computes the likelihood of a phylogenetic tree given a sequence
alignment and a model. \code{optim.pml} optimizes the different model
parameters.
}
\details{
Base frequencies in \code{pml} can be supplied in different ways.
For amino acid they are usually defined through specifying a model, so the
argument bf does not need to be specified. Otherwise if \code{bf=NULL},
each state is given equal probability. It can be a numeric vector given the
frequencies. Last but not least \code{bf} can be string "equal", "empirical"
and for codon models additionally "F3x4".
The topology search uses a nearest neighbor interchange (NNI) and the
implementation is similar to phyML. The option model in pml is only used
for amino acid models. The option model defines the nucleotide model which
is getting optimized, all models which are included in modeltest can be
chosen. Setting this option (e.g. "K81" or "GTR") overrules options optBf
and optQ. Here is a overview how to estimate different phylogenetic models
with \code{pml}: \tabular{lll}{ model \tab optBf \tab optQ \cr Jukes-Cantor
\tab FALSE \tab FALSE \cr F81 \tab TRUE \tab FALSE \cr symmetric \tab FALSE
\tab TRUE \cr GTR \tab TRUE \tab TRUE } Via model in optim.pml the following
nucleotide models can be specified: JC, F81, K80, HKY, TrNe, TrN, TPM1, K81,
TPM1u, TPM2, TPM2u, TPM3, TPM3u, TIM1e, TIM1, TIM2e, TIM2, TIM3e, TIM3,
TVMe, TVM, SYM and GTR. These models are specified as in Posada (2008).
So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff",
"cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb",
"FLU", "Blosum62", "Dayhoff_DCMut" and "JTT_DCMut") and additionally rate
matrices and amino acid frequencies can be supplied.
It is also possible to estimate codon models (e.g. YN98), for details see
also the chapter in vignette("phangorn-specials").
If the option 'optRooted' is set to TRUE than the edge lengths of rooted
tree are optimized. The tree has to be rooted and by now ultrametric!
Optimising rooted trees is generally much slower.
\code{pml.control} controls the fitting process. \code{epsilon} and
\code{maxit} are only defined for the most outer loop, this affects
\code{pmlCluster}, \code{pmlPart} and \code{pmlMix}. \code{epsilon} is
defined as (logLik(k)-logLik(k+1))/logLik(k+1), this seems to be a good
heuristics which works reasonably for small and large trees or alignments.
If \code{trace} is set to zero than no out put is shown, if functions are
called internally than the trace is decreased by one, so a higher of trace
produces more feedback.
If \code{rearrangement} is set to \code{stochastic} a stochastic search
algorithm similar to Nguyen et al. (2015). and for \code{ratchet} the
likelihood ratchet as in Vos (2003). This should helps often to find better
tree topologies, especially for larger trees.
}
\examples{
example(NJ)
# Jukes-Cantor (starting tree from NJ)
fitJC <- pml(tree, Laurasiatherian)
# optimize edge length parameter
fitJC <- optim.pml(fitJC)
fitJC
\dontrun{
# search for a better tree using NNI rearrangements
fitJC <- optim.pml(fitJC, optNni=TRUE)
fitJC
plot(fitJC$tree)
# JC + Gamma + I - model
fitJC_GI <- update(fitJC, k=4, inv=.2)
# optimize shape parameter + proportion of invariant sites
fitJC_GI <- optim.pml(fitJC_GI, optGamma=TRUE, optInv=TRUE)
# GTR + Gamma + I - model
fitGTR <- optim.pml(fitJC_GI, rearrangement = "stochastic",
optGamma=TRUE, optInv=TRUE, model="GTR")
}
# 2-state data (RY-coded)
dat <- acgt2ry(Laurasiatherian)
fit2ST <- pml(tree, dat)
fit2ST <- optim.pml(fit2ST,optNni=TRUE)
fit2ST
# show some of the methods available for class pml
methods(class="pml")
}
\references{
Felsenstein, J. (1981) Evolutionary trees from DNA sequences: a
maximum likelihood approach. \emph{Journal of Molecular Evolution},
\bold{17}, 368--376.
Felsenstein, J. (2004). \emph{Inferring Phylogenies}. Sinauer Associates,
Sunderland.
Yang, Z. (2006). \emph{Computational Molecular evolution}. Oxford University
Press, Oxford.
Adachi, J., P. J. Waddell, W. Martin, and M. Hasegawa (2000) Plastid genome
phylogeny and a model of amino acid substitution for proteins encoded by
chloroplast DNA. \emph{Journal of Molecular Evolution}, \bold{50}, 348--358
Rota-Stabelli, O., Z. Yang, and M. Telford. (2009) MtZoa: a general
mitochondrial amino acid substitutions model for animal evolutionary
studies. \emph{Mol. Phyl. Evol}, \bold{52(1)}, 268--72
Whelan, S. and Goldman, N. (2001) A general empirical model of protein
evolution derived from multiple protein families using a maximum-likelihood
approach. \emph{Molecular Biology and Evolution}, \bold{18}, 691--699
Le, S.Q. and Gascuel, O. (2008) LG: An Improved, General Amino-Acid
Replacement Matrix \emph{Molecular Biology and Evolution}, \bold{25(7)},
1307--1320
Yang, Z., R. Nielsen, and M. Hasegawa (1998) Models of amino acid
substitution and applications to Mitochondrial protein evolution.
\emph{Molecular Biology and Evolution}, \bold{15}, 1600--1611
Abascal, F., D. Posada, and R. Zardoya (2007) MtArt: A new Model of amino
acid replacement for Arthropoda. \emph{Molecular Biology and Evolution},
\bold{24}, 1--5
Kosiol, C, and Goldman, N (2005) Different versions of the Dayhoff rate
matrix - \emph{Molecular Biology and Evolution}, \bold{22}, 193--199
L.-T. Nguyen, H.A. Schmidt, A. von Haeseler, and B.Q. Minh (2015) IQ-TREE: A
fast and effective stochastic algorithm for estimating maximum likelihood
phylogenies. \emph{Molecular Biology and Evolution}, \bold{32}, 268--274.
Vos, R. A. (2003) Accelerated Likelihood Surface Exploration: The Likelihood
Ratchet. \emph{Systematic Biology}, \bold{52(3)}, 368--373
Yang, Z., and R. Nielsen (1998) Synonymous and nonsynonymous rate variation
in nuclear genes of mammals. \emph{Journal of Molecular Evolution},
\bold{46}, 409-418.
Lewis, P.O. (2001) A likelihood approach to estimating phylogeny from
discrete morphological character data. \emph{Systematic Biology} \bold{50},
913--925.
}
\seealso{
\code{\link{bootstrap.pml}}, \code{\link{modelTest}},
\code{\link{pmlPart}}, \code{\link{pmlMix}}, \code{\link{plot.phylo}},
\code{\link{SH.test}}, \code{\link{ancestral.pml}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/upgma.Rd 0000644 0001762 0000144 00000002011 13707232051 014233 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distTree.R
\name{upgma}
\alias{upgma}
\alias{wpgma}
\title{UPGMA and WPGMA}
\usage{
upgma(D, method = "average", ...)
wpgma(D, method = "mcquitty", ...)
}
\arguments{
\item{D}{A distance matrix.}
\item{method}{The agglomeration method to be used. This should be (an
unambiguous abbreviation of) one of "ward", "single", "complete", "average",
"mcquitty", "median" or "centroid". The default is "average".}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
A phylogenetic tree of class \code{phylo}.
}
\description{
UPGMA and WPGMA clustering. Just a wrapper function around
\code{\link[stats]{hclust}}.
}
\examples{
data(Laurasiatherian)
dm <- dist.ml(Laurasiatherian)
tree <- upgma(dm)
plot(tree)
}
\seealso{
\code{\link{hclust}}, \code{\link{dist.hamming}}, \code{\link{NJ}},
\code{\link{as.phylo}}, \code{\link{fastme}}, \code{\link{nnls.tree}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/NJ.Rd 0000644 0001762 0000144 00000002334 13707232051 013441 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distTree.R
\name{NJ}
\alias{NJ}
\alias{UNJ}
\title{Neighbor-Joining}
\usage{
NJ(x)
UNJ(x)
}
\arguments{
\item{x}{A distance matrix.}
}
\value{
an object of class \code{"phylo"}.
}
\description{
This function performs the neighbor-joining tree estimation of Saitou and
Nei (1987). UNJ is the unweighted version from Gascuel (1997).
}
\examples{
data(Laurasiatherian)
dm <- dist.ml(Laurasiatherian)
tree <- NJ(dm)
plot(tree)
}
\references{
Saitou, N. and Nei, M. (1987) The neighbor-joining method: a new
method for reconstructing phylogenetic trees. \emph{Molecular Biology and
Evolution}, \bold{4}, 406--425.
Studier, J. A and Keppler, K. J. (1988) A Note on the Neighbor-Joining
Algorithm of Saitou and Nei. \emph{Molecular Biology and Evolution},
\bold{6}, 729--731.
Gascuel, O. (1997) Concerning the NJ algorithm and its unweighted version,
UNJ. in Birkin et. al. \emph{Mathematical Hierarchies and Biology},
149--170.
}
\seealso{
\code{\link[ape]{nj}}, \code{\link[ape]{dist.dna}},
\code{\link[phangorn]{dist.hamming}}, \code{\link[phangorn]{upgma}},
\code{\link[ape]{fastme}}
}
\author{
Klaus P. Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/dist.hamming.Rd 0000644 0001762 0000144 00000005316 14130613657 015525 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/distSeq.R
\name{dist.hamming}
\alias{dist.hamming}
\alias{dist.ml}
\alias{dist.logDet}
\title{Pairwise Distances from Sequences}
\usage{
dist.hamming(x, ratio = TRUE, exclude = "none")
dist.ml(x, model = "JC69", exclude = "none", bf = NULL, Q = NULL,
k = 1L, shape = 1, ...)
dist.logDet(x)
}
\arguments{
\item{x}{An object of class \code{phyDat}}
\item{ratio}{Compute uncorrected ('p') distance or character difference.}
\item{exclude}{One of "none", "all", "pairwise" indicating whether to delete
the sites with missing data (or ambiguous states). The default is handle
missing data as in pml.}
\item{model}{One of "JC69", "F81" or one of 17 amino acid models see
details.}
\item{bf}{A vector of base frequencies.}
\item{Q}{A vector containing the lower triangular part of the rate matrix.}
\item{k}{Number of intervals of the discrete gamma distribution.}
\item{shape}{Shape parameter of the gamma distribution.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
an object of class \code{dist}
}
\description{
\code{dist.hamming}, \code{dist.ml} and \code{dist.logDet} compute pairwise
distances for an object of class \code{phyDat}. \code{dist.ml} uses DNA /
AA sequences to compute distances under different substitution models.
}
\details{
So far 17 amino acid models are supported ("WAG", "JTT", "LG", "Dayhoff",
"cpREV", "mtmam", "mtArt", "MtZoa", "mtREV24", "VT","RtREV", "HIVw", "HIVb",
"FLU", "Blosum62", "Dayhoff_DCMut" and "JTT_DCMut") and additional rate
matrices and frequencies can be supplied.
The "F81" model uses empirical base frequencies, the "JC69" equal base
frequencies. This is even the case if the data are not nucleotides.
}
\examples{
data(Laurasiatherian)
dm1 <- dist.hamming(Laurasiatherian)
tree1 <- NJ(dm1)
dm2 <- dist.logDet(Laurasiatherian)
tree2 <- NJ(dm2)
treedist(tree1,tree2)
# JC model
dm3 <- dist.ml(Laurasiatherian)
tree3 <- NJ(dm3)
treedist(tree1,tree3)
# F81 + Gamma
dm4 <- dist.ml(Laurasiatherian, model="F81", k=4, shape=.4)
tree4 <- NJ(dm4)
treedist(tree1,tree4)
treedist(tree3,tree4)
}
\references{
Lockhart, P. J., Steel, M. A., Hendy, M. D. and Penny, D. (1994)
Recovering evolutionary trees under a more realistic model of sequence
evolution. \emph{Molecular Biology and Evolution}, \bold{11}, 605--602.
Jukes TH and Cantor CR (1969). \emph{Evolution of Protein Molecules}. New
York: Academic Press. 21--132.
}
\seealso{
For more distance methods for nucleotide data see
\code{\link[ape]{dist.dna}} and \code{\link{dist.p}} for pairwise
polymorphism p-distances. \code{\link{writeDist}} for export and import
distances.
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/Laurasiatherian.Rd 0000644 0001762 0000144 00000000735 14107670671 016262 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phangorn-package.R
\docType{data}
\name{Laurasiatherian}
\alias{Laurasiatherian}
\title{Laurasiatherian data (AWCMEE)}
\source{
Data have been taken from the former repository of the Allan Wilson
Centre and were converted to R format by \email{klaus.schliep@gmail.com}.
}
\description{
Laurasiatherian RNA sequence data
}
\examples{
data(Laurasiatherian)
str(Laurasiatherian)
}
\keyword{datasets}
phangorn/man/lento.Rd 0000644 0001762 0000144 00000002574 13707232051 014261 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/lento.R
\name{lento}
\alias{lento}
\title{Lento plot}
\usage{
lento(obj, xlim = NULL, ylim = NULL, main = "Lento plot", sub = NULL,
xlab = NULL, ylab = NULL, bipart = TRUE, trivial = FALSE,
col = rgb(0, 0, 0, 0.5), ...)
}
\arguments{
\item{obj}{an object of class phylo, multiPhylo or splits}
\item{xlim}{graphical parameter}
\item{ylim}{graphical parameter}
\item{main}{graphical parameter}
\item{sub}{graphical parameter}
\item{xlab}{graphical parameter}
\item{ylab}{graphical parameter}
\item{bipart}{plot bipartition information.}
\item{trivial}{logical, whether to present trivial splits (default is
FALSE).}
\item{col}{color for the splits / bipartition.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
lento returns a plot.
}
\description{
The lento plot represents support and conflict of splits/bipartitions.
}
\examples{
data(yeast)
yeast.ry <- acgt2ry(yeast)
splits.h <- h2st(yeast.ry)
lento(splits.h, trivial=TRUE)
}
\references{
Lento, G.M., Hickson, R.E., Chambers G.K., and Penny, D. (1995)
Use of spectral analysis to test hypotheses on the origin of pinninpeds.
\emph{Molecular Biology and Evolution}, \bold{12}, 28-52.
}
\seealso{
\code{\link{as.splits}, \link{hadamard}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
\keyword{plot}
phangorn/man/bootstrap.pml.Rd 0000644 0001762 0000144 00000006660 14013023424 015735 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bootstrap.R
\name{bootstrap.pml}
\alias{bootstrap.pml}
\alias{bootstrap.phyDat}
\title{Bootstrap}
\usage{
bootstrap.pml(x, bs = 100, trees = TRUE, multicore = FALSE,
mc.cores = NULL, ...)
bootstrap.phyDat(x, FUN, bs = 100, multicore = FALSE, mc.cores = NULL,
jumble = TRUE, ...)
}
\arguments{
\item{x}{an object of class \code{pml} or \code{phyDat}.}
\item{bs}{number of bootstrap samples.}
\item{trees}{return trees only (default) or whole \code{pml} objects.}
\item{multicore}{logical, whether models should estimated in parallel.}
\item{mc.cores}{The number of cores to use during bootstrap. Only supported
on UNIX-alike systems.}
\item{\dots}{further parameters used by \code{optim.pml} or
\code{plot.phylo}.}
\item{FUN}{the function to estimate the trees.}
\item{jumble}{logical, jumble the order of the sequences.}
}
\value{
\code{bootstrap.pml} returns an object of class \code{multi.phylo}
or a list where each element is an object of class \code{pml}. \code{plotBS}
returns silently a tree, i.e. an object of class \code{phylo} with the
bootstrap values as node labels. The argument \code{BStrees} is optional and
if not supplied the tree with labels supplied in the \code{node.label} slot.
}
\description{
\code{bootstrap.pml} performs (non-parametric) bootstrap analysis and
\code{bootstrap.phyDat} produces a list of bootstrapped data sets.
\code{plotBS} plots a phylogenetic tree with the bootstrap values assigned
to the (internal) edges.
}
\details{
It is possible that the bootstrap is performed in parallel, with help of the
multicore package. Unfortunately the multicore package does not work under
windows or with GUI interfaces ("aqua" on a mac). However it will speed up
nicely from the command line ("X11").
}
\examples{
\dontrun{
data(Laurasiatherian)
dm <- dist.hamming(Laurasiatherian)
tree <- NJ(dm)
# NJ
set.seed(123)
NJtrees <- bootstrap.phyDat(Laurasiatherian,
FUN=function(x)NJ(dist.hamming(x)), bs=100)
treeNJ <- plotBS(tree, NJtrees, "phylogram")
# Maximum likelihood
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit, rearrangement="NNI")
set.seed(123)
bs <- bootstrap.pml(fit, bs=100, optNni=TRUE)
treeBS <- plotBS(fit$tree,bs)
# Maximum parsimony
treeMP <- pratchet(Laurasiatherian)
treeMP <- acctran(treeMP, Laurasiatherian)
set.seed(123)
BStrees <- bootstrap.phyDat(Laurasiatherian, pratchet, bs = 100)
treeMP <- plotBS(treeMP, BStrees, "phylogram")
add.scale.bar()
# export tree with bootstrap values as node labels
# write.tree(treeBS)
}
}
\references{
Felsenstein J. (1985) Confidence limits on phylogenies. An
approach using the bootstrap. \emph{Evolution} \bold{39}, 783--791
Lemoine, F., Entfellner, J. B. D., Wilkinson, E., Correia, D., Felipe, M. D.,
De Oliveira, T., & Gascuel, O. (2018). Renewing Felsenstein’s phylogenetic
bootstrap in the era of big data. \emph{Nature}, \bold{556(7702)}, 452--456.
Penny D. and Hendy M.D. (1985) Testing methods evolutionary tree
construction. \emph{Cladistics} \bold{1}, 266--278
Penny D. and Hendy M.D. (1986) Estimating the reliability of evolutionary
trees. \emph{Molecular Biology and Evolution} \bold{3}, 403--417
}
\seealso{
\code{\link{optim.pml}}, \code{\link{pml}},
\code{\link{plot.phylo}}, \code{\link{maxCladeCred}}
\code{\link{nodelabels}},\code{\link{consensusNet}} and
\code{\link{SOWH.test}} for parametric bootstrap
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/phangorn-internal.Rd 0000644 0001762 0000144 00000001011 13707232051 016547 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/RcppExports.R, R/networx.R,
% R/phangorn-package.R, R/phyDat.R, R/pmlPen.R
\name{threshStateC}
\alias{threshStateC}
\alias{coords}
\alias{phangorn-internal}
\alias{map_duplicates}
\alias{pmlPen}
\title{Internal phangorn Functions}
\usage{
threshStateC(x, thresholds)
coords(obj, dim = "3D")
map_duplicates(x, dist = length(x) < 500, ...)
pmlPen(object, lambda, ...)
}
\description{
Internal \pkg{phangorn} functions.
}
\keyword{internal}
phangorn/man/midpoint.Rd 0000644 0001762 0000144 00000003437 14140465255 014770 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treeManipulation.R
\name{getRoot}
\alias{getRoot}
\alias{midpoint}
\alias{midpoint.phylo}
\alias{midpoint.multiPhylo}
\alias{pruneTree}
\title{Tree manipulation}
\usage{
getRoot(tree)
midpoint(tree, node.labels = "support", ...)
\method{midpoint}{phylo}(tree, node.labels = "support", ...)
\method{midpoint}{multiPhylo}(tree, node.labels = "support", ...)
pruneTree(tree, ..., FUN = ">=")
}
\arguments{
\item{tree}{an object of class \code{phylo}.}
\item{node.labels}{are node labels 'support' values (edges), 'label' or
should labels get 'deleted'?}
\item{\dots}{further arguments, passed to other methods.}
\item{FUN}{a function evaluated on the nodelabels, result must be logical.}
}
\value{
\code{pruneTree} and \code{midpoint} a tree. \code{getRoot} returns
the root node.
}
\description{
\code{midpoint} performs midpoint rooting of a tree. \code{pruneTree}
produces a consensus tree.
}
\details{
\code{pruneTree} prunes back a tree and produces a consensus tree, for trees
already containing nodelabels. It assumes that nodelabels are numerical or
character that allows conversion to numerical, it uses
as.numeric(as.character(tree$node.labels)) to convert them. \code{midpoint}
so far does not transform node.labels properly.
}
\examples{
tree <- rtree(10, rooted = FALSE)
tree$node.label <- c("", round(runif(tree$Nnode-1), 3))
tree2 <- midpoint(tree)
tree3 <- pruneTree(tree, .5)
old.par <- par(no.readonly = TRUE)
par(mfrow = c(3,1))
plot(tree, show.node.label=TRUE)
plot(tree2, show.node.label=TRUE)
plot(tree3, show.node.label=TRUE)
par(old.par)
}
\seealso{
\code{\link[ape]{consensus}}, \code{\link[ape]{root}},
\code{\link[ape]{multi2di}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/maxCladeCred.Rd 0000644 0001762 0000144 00000004442 14074005750 015452 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bootstrap.R
\name{maxCladeCred}
\alias{maxCladeCred}
\alias{mcc}
\alias{allCompat}
\title{Maximum clade credibility tree}
\usage{
maxCladeCred(x, tree = TRUE, part = NULL, rooted = TRUE)
mcc(x, tree = TRUE, part = NULL, rooted = TRUE)
allCompat(x)
}
\arguments{
\item{x}{\code{x} is an object of class \code{multiPhylo} or \code{phylo}}
\item{tree}{logical indicating whether return the tree with the clade
credibility (default) or the clade credibility score for all trees.}
\item{part}{a list of partitions as returned by \code{prop.part}}
\item{rooted}{logical, if FALSE the tree with highest maximum bipartition
credibility is returned.}
}
\value{
a tree (an object of class \code{phylo}) with the highest clade
credibility or a numeric vector of clade credibilities for each tree.
}
\description{
\code{maxCladeCred} computes the maximum clade credibility tree from a
sample of trees.
}
\details{
So far just the best tree is returned. No annotations or transformations of
edge length are performed.
If a list of partition is provided then the clade credibility is computed
for the trees in x.
\code{allCompat} returns a 50% majority rule consensus tree with added
compatible splits similar to the option allcompat in MrBayes.
}
\examples{
data(Laurasiatherian)
set.seed(42)
bs <- bootstrap.phyDat(Laurasiatherian,
FUN = function(x)upgma(dist.hamming(x)), bs=100)
strict_consensus <- consensus(bs)
majority_consensus <- consensus(bs, p=.5)
all_compat <- allCompat(bs)
max_clade_cred <- maxCladeCred(bs)
old.par <- par(no.readonly = TRUE)
par(mfrow = c(2,2), mar = c(1,4,1,1))
plot(strict_consensus, main="Strict consensus tree")
plot(majority_consensus, main="Majority consensus tree")
plot(all_compat, main="Majority consensus tree with compatible splits")
plot(max_clade_cred, main="Maximum clade credibility tree")
par(old.par)
# compute clade credibility for trees given a prop.part object
pp <- prop.part(bs)
tree <- rNNI(bs[[1]], 20)
maxCladeCred(c(tree, bs[[1]]), tree=FALSE, part = pp)
# first value likely be -Inf
}
\seealso{
\code{\link{consensus}}, \code{\link{consensusNet}},
\code{\link{prop.part}}, \code{\link{bootstrap.pml}}, \code{\link{plotBS}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/chloroplast.Rd 0000644 0001762 0000144 00000000503 13707232051 015460 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phangorn-package.R
\docType{data}
\name{chloroplast}
\alias{chloroplast}
\title{Chloroplast alignment}
\description{
Amino acid alignment of 15 genes of 19 different chloroplast.
}
\examples{
data(chloroplast)
chloroplast
}
\keyword{datasets}
phangorn/man/modelTest.Rd 0000644 0001762 0000144 00000006022 13707232051 015070 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/modelTest.R
\name{modelTest}
\alias{modelTest}
\alias{AICc}
\title{ModelTest}
\usage{
modelTest(object, tree = NULL, model = c("JC", "F81", "K80", "HKY", "SYM",
"GTR"), G = TRUE, I = TRUE, FREQ = FALSE, k = 4,
control = pml.control(epsilon = 1e-08, maxit = 10, trace = 1),
multicore = FALSE, mc.cores = NULL)
}
\arguments{
\item{object}{an object of class phyDat or pml}
\item{tree}{a phylogenetic tree.}
\item{model}{a vector containing the substitution models to compare with
each other or "all" to test all available models}
\item{G}{logical, TRUE (default) if (discrete) Gamma model should be tested}
\item{I}{logical, TRUE (default) if invariant sites should be tested}
\item{FREQ}{logical, FALSE (default) if TRUE amino acid frequencies will be
estimated.}
\item{k}{number of rate classes}
\item{control}{A list of parameters for controlling the fitting process.}
\item{multicore}{logical, whether models should estimated in parallel.}
\item{mc.cores}{The number of cores to use, i.e. at most how many child
processes will be run simultaneously. Must be at least one, and
parallelization requires at least two cores.}
}
\value{
A data.frame containing the log-likelihood, number of estimated
parameters, AIC, AICc and BIC all tested models. The data.frame has an
attributes "env" which is an environment which contains all the trees, the
data and the calls to allow get the estimated models, e.g. as a starting
point for further analysis (see example).
}
\description{
Comparison of different nucleotide or amino acid substitution models
}
\details{
\code{modelTest} estimates all the specified models for a given tree and
data. When the mclapply is available, the computations are done in
parallel. \code{modelTest} runs each model in one thread. This is may not
work within a GUI interface and will not work under Windows.
}
\examples{
\dontrun{
example(NJ)
(mT <- modelTest(Laurasiatherian, tree))
# some R magic
env <- attr(mT, "env")
ls(env=env)
(F81 <- get("F81+G", env)) # a call
eval(F81, env=env)
data(chloroplast)
(mTAA <- modelTest(chloroplast, model=c("JTT", "WAG", "LG")))
# test all available amino acid models
(mTAA_all <- modelTest(chloroplast, model="all", multicore=TRUE, mc.cores=2))
}
}
\references{
Burnham, K. P. and Anderson, D. R (2002) \emph{Model selection
and multimodel inference: a practical information-theoretic approach}. 2nd
ed. Springer, New York
Posada, D. and Crandall, K.A. (1998) MODELTEST: testing the model of DNA
substitution. \emph{Bioinformatics} \bold{14(9)}: 817-818
Posada, D. (2008) jModelTest: Phylogenetic Model Averaging. \emph{Molecular
Biology and Evolution} \bold{25}: 1253-1256
Darriba D., Taboada G.L., Doallo R and Posada D. (2011) ProtTest 3: fast
selection of best-fit models of protein evolution. . \emph{Bioinformatics}
\bold{27}: 1164-1165
}
\seealso{
\code{\link{pml}}, \code{\link{anova}}, \code{\link[stats]{AIC}},
\code{\link{codonTest}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/identify.networx.Rd 0000644 0001762 0000144 00000002013 13707232051 016444 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networx.R
\name{identify.networx}
\alias{identify.networx}
\title{Identify splits in a network}
\usage{
\method{identify}{networx}(x, quiet = FALSE, ...)
}
\arguments{
\item{x}{an object of class \code{networx}}
\item{quiet}{a logical controlling whether to print a message inviting the
user to click on the tree.}
\item{\dots}{further arguments to be passed to or from other methods.}
}
\value{
\code{identify.networx} returns a splits object.
}
\description{
\code{identify.networx} reads the position of the graphics pointer when the
mouse button is pressed. It then returns the split belonging to the edge
closest to the pointer. The network must be plotted beforehand.
}
\examples{
\dontrun{
data(yeast)
dm <- dist.ml(yeast)
nnet <- neighborNet(dm)
plot(nnet, "2D")
identify(nnet) # click close to an edge
}
}
\seealso{
\code{\link[phangorn]{plot.networx}}, \code{\link[graphics]{identify}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
phangorn/man/superTree.Rd 0000644 0001762 0000144 00000004446 13775657504 015141 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/superTree.R
\name{superTree}
\alias{superTree}
\title{Super Tree methods}
\usage{
superTree(tree, method = "MRP", rooted = FALSE, trace = 0,
start = NULL, multicore = FALSE, mc.cores = NULL, ...)
}
\arguments{
\item{tree}{an object of class \code{multiPhylo}}
\item{method}{An argument defining which algorithm is used to optimize the
tree. Possible are "MRP", "RF", and "SPR".}
\item{rooted}{should the resulting supertrees be rooted.}
\item{trace}{defines how much information is printed during optimization.}
\item{start}{a starting tree can be supplied.}
\item{multicore}{logical, whether models should estimated in parallel.}
\item{mc.cores}{The number of cores to use, i.e. at most how many child
processes will be run simultaneously.}
\item{\dots}{further arguments passed to or from other methods.}
}
\value{
The function returns an object of class \code{phylo}.
}
\description{
These function \code{superTree} allows the estimation of a supertree from a
set of trees using either Matrix representation parsimony, Robinson-Foulds
or SPR as criterion.
}
\details{
The function \code{superTree} extends the function mrp.supertree from Liam
Revells, with artificial adding an outgroup on the root of the trees. This
allows to root the supertree afterwards. The functions is internally used in
DensiTree. The implementation for the RF- and SPR-supertree are very basic
so far and assume that all trees share the same set of taxa.
}
\examples{
data(Laurasiatherian)
set.seed(1)
bs <- bootstrap.phyDat(Laurasiatherian,
FUN = function(x) upgma(dist.hamming(x)), bs=50)
mrp_st <- superTree(bs)
plot(mrp_st)
\dontrun{
rf_st <- superTree(bs, method = "RF")
spr_st <- superTree(bs, method = "SPR")
}
}
\references{
Baum, B. R., (1992) Combining trees as a way of combining data
sets for phylogenetic inference, and the desirability of combining gene
trees. \emph{Taxon}, \bold{41}, 3-10.
Ragan, M. A. (1992) Phylogenetic inference based on matrix representation of
trees. \emph{Molecular Phylogenetics and Evolution}, \bold{1}, 53-58.
}
\seealso{
\code{mrp.supertree}, \code{\link{densiTree}},
\code{\link{RF.dist}}, \code{\link{SPR.dist}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com} Liam Revell
}
\keyword{cluster}
phangorn/man/CI.Rd 0000644 0001762 0000144 00000002176 13707232051 013431 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/parsimony.R
\name{CI}
\alias{CI}
\alias{RI}
\title{Consistency Index and Retention Index}
\usage{
CI(tree, data, cost = NULL, sitewise = FALSE)
RI(tree, data, cost = NULL, sitewise = FALSE)
}
\arguments{
\item{tree}{tree to start the nni search from.}
\item{data}{A object of class phyDat containing sequences.}
\item{cost}{A cost matrix for the transitions between two states.}
\item{sitewise}{return CI/RI for alignment or sitewise}
}
\description{
\code{CI} and \code{RI} compute the Consistency Index (CI) and Retention
Index (RI).
}
\details{
The Consistency Index is defined as minimum number of changes
divided by the number of changes required on the tree (parsimony score). The
Consistency Index is equal to one if there is no homoplasy.
And the Retention Index is defined as
\deqn{RI = \frac{MaxChanges - ObsChanges}{MaxChanges - MinChanges}}{RI = (MaxChanges - ObsChanges) / (MaxChanges - MinChanges)}
}
\seealso{
\code{\link{parsimony}}, \code{\link{pratchet}}, \code{\link{fitch}},
\code{\link{sankoff}}, \code{\link{bab}}, \code{\link{ancestral.pars}}
}
phangorn/man/addConfidences.Rd 0000644 0001762 0000144 00000003526 13775174601 016042 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networx.R
\name{createLabel}
\alias{createLabel}
\alias{addConfidences}
\alias{addConfidences.phylo}
\alias{presenceAbsence}
\title{Compare splits and add support values to an object}
\usage{
createLabel(x, y, label_y, type = "edge", nomatch = NA)
addConfidences(x, y, ...)
\method{addConfidences}{phylo}(x, y, ...)
presenceAbsence(x, y)
}
\arguments{
\item{x}{an object of class \code{splits}, \code{phylo} or \code{networx}}
\item{y}{an object of class \code{splits}, \code{phylo}, \code{multiPhylo}
or \code{networx}}
\item{label_y}{label of y matched on x. Will be usually of
length(as.splits(x)).}
\item{type}{should labels returned for edges (in \code{networx}) or splits.}
\item{nomatch}{default value if no match between x and y is found.}
\item{...}{Further arguments passed to or from other methods.}
}
\value{
The object \code{x} with added bootstrap / MCMC support values.
}
\description{
Add support values to a \code{splits}, \code{phylo} or \code{networx}
object.
}
\examples{
data(woodmouse)
woodmouse <- phyDat(woodmouse)
tmpfile <- normalizePath(system.file("extdata/trees/RAxML_bootstrap.woodmouse", package="phangorn"))
boot_trees <- read.tree(tmpfile)
dm <- dist.ml(woodmouse)
tree <- upgma(dm)
nnet <- neighborNet(dm)
tree <- addConfidences(tree, boot_trees)
nnet <- addConfidences(nnet, boot_trees)
plot(tree, show.node.label=TRUE)
plot(nnet, "2D", show.edge.label=TRUE)
}
\references{
Schliep, K., Potts, A. J., Morrison, D. A. and Grimm, G. W. (2017),
Intertwining phylogenetic trees and networks. \emph{Methods Ecol Evol}.
\bold{8}, 1212--1220. doi:10.1111/2041-210X.12760
}
\seealso{
\code{\link{as.splits}}, \code{\link{as.networx}},
\code{\link{RF.dist}}, \code{\link{plot.phylo}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/plotBS.Rd 0000644 0001762 0000144 00000006427 14042563015 014344 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bootstrap.R
\name{plotBS}
\alias{plotBS}
\title{Plotting trees with bootstrap values}
\usage{
plotBS(tree, BStrees, type = "unrooted", method = "FBP",
bs.col = "black", bs.adj = NULL, digits = 3, p = 0, frame = "none",
...)
}
\arguments{
\item{tree}{The tree on which edges the bootstrap values are plotted.}
\item{BStrees}{a list of trees (object of class "multiPhylo").}
\item{type}{the type of tree to plot, one of "phylogram", "cladogram", "fan",
"unrooted", "radial" or "none". If type is "none" the tree is returned with
the bootstrap values assigned to the node labels.}
\item{method}{either "FBP" the classical bootstrap (default) or "TBE"
(transfer bootstrap)}
\item{bs.col}{color of bootstrap support labels.}
\item{bs.adj}{one or two numeric values specifying the horizontal and
vertical justification of the bootstrap labels.}
\item{digits}{integer indicating the number of decimal places.}
\item{p}{only plot support values higher than this percentage number
(default is 0).}
\item{frame}{a character string specifying the kind of frame to be printed
around the bootstrap values. This must be one of "none" (the default),
"rect" or "circle".}
\item{\dots}{further parameters used by \code{plot.phylo}.}
}
\value{
\code{plotBS} returns silently a tree, i.e. an object of class
\code{phylo} with the bootstrap values as node labels. The argument
\code{BStrees} is optional and if not supplied the labels supplied
in the \code{node.label} slot will be used.
}
\description{
\code{plotBS} plots a phylogenetic tree with the bootstrap values assigned
to the (internal) edges. It can also used to assign bootstrap values to a
phylogenetic tree.
}
\details{
\code{plotBS} can either assign the classical Felsenstein’s bootstrap
proportions (FBP) (Felsenstein (1985), Hendy & Penny (1985)) or the
transfer bootstrap expectation (TBE) of Lemoine et al. (2018). Using the
option \code{type=="n"} just assigns the bootstrap values and return the tree
without plotting it.
}
\examples{
fdir <- system.file("extdata/trees", package = "phangorn")
# RAxML best-known tree with bipartition support (from previous analysis)
raxml.tree <- read.tree(file.path(fdir,"RAxML_bipartitions.woodmouse"))
# RAxML bootstrap trees (from previous analysis)
raxml.bootstrap <- read.tree(file.path(fdir,"RAxML_bootstrap.woodmouse"))
par(mfrow=c(1,2))
plotBS(raxml.tree, raxml.bootstrap, "p")
plotBS(raxml.tree, raxml.bootstrap, "p", "TBE")
}
\references{
Felsenstein J. (1985) Confidence limits on phylogenies. An
approach using the bootstrap. \emph{Evolution} \bold{39}, 783--791
Lemoine, F., Entfellner, J. B. D., Wilkinson, E., Correia, D., Felipe, M. D.,
De Oliveira, T., & Gascuel, O. (2018). Renewing Felsenstein’s phylogenetic
bootstrap in the era of big data. \emph{Nature}, \bold{556(7702)}, 452--456.
Penny D. and Hendy M.D. (1985) Testing methods evolutionary tree
construction. \emph{Cladistics} \bold{1}, 266--278
Penny D. and Hendy M.D. (1986) Estimating the reliability of evolutionary
trees. \emph{Molecular Biology and Evolution} \bold{3}, 403--417
}
\seealso{
\code{\link{plot.phylo}}, \code{\link{maxCladeCred}}
\code{\link{nodelabels}}, \code{\link{consensus}}, \code{\link{consensusNet}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
phangorn/man/neighborNet.Rd 0000644 0001762 0000144 00000002274 13707232051 015401 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/neighborNet.R
\name{neighborNet}
\alias{neighborNet}
\title{Computes a neighborNet from a distance matrix}
\usage{
neighborNet(x, ord = NULL)
}
\arguments{
\item{x}{a distance matrix.}
\item{ord}{a circular ordering.}
}
\value{
\code{neighborNet} returns an object of class networx.
}
\description{
Computes a neighborNet, i.e. an object of class \code{networx} from a
distance matrix.
}
\details{
\code{neighborNet} is still experimental. The cyclic ordering sometimes
differ from the SplitsTree implementation, the \emph{ord} argument can be
used to enforce a certain circular ordering.
}
\examples{
data(yeast)
dm <- dist.ml(yeast)
nnet <- neighborNet(dm)
plot(nnet, "2D")
}
\references{
Bryant, D. & Moulton, V. (2004) Neighbor-Net: An Agglomerative
Method for the Construction of Phylogenetic Networks. \emph{Molecular
Biology and Evolution}, 2004, \bold{21}, 255-265
}
\seealso{
\code{\link{splitsNetwork}}, \code{\link{consensusNet}},
\code{\link{plot.networx}}, \code{\link{lento}},
\code{\link{cophenetic.networx}}, \code{\link{distanceHadamard}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{hplot}
phangorn/man/coalSpeciesTree.Rd 0000644 0001762 0000144 00000002225 13707232051 016203 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/Coalescent.R
\name{coalSpeciesTree}
\alias{coalSpeciesTree}
\title{Species Tree}
\usage{
coalSpeciesTree(tree, X = NULL, sTree = NULL)
}
\arguments{
\item{tree}{an object of class \code{multiPhylo}}
\item{X}{A \code{phyDat} object to define which individual belongs to which
species.}
\item{sTree}{A species tree which fixes the topology.}
}
\value{
The function returns an object of class \code{phylo}.
}
\description{
\code{coalSpeciesTree} estimates species trees and can handle multiple
individuals per species.
}
\details{
\code{coalSpeciesTree} estimates a single linkage tree as suggested by Liu
et al. (2010) from the element wise minima of the cophenetic matrices of the
gene trees. It extends \code{speciesTree} in ape as it allows that have
several individuals per gene tree.
}
\references{
Liu, L., Yu, L. and Pearl, D. K. (2010) Maximum tree: a
consistent estimator of the species tree. \emph{Journal of Mathematical
Biology}, \bold{60}, 95--106.
}
\seealso{
\code{\link{speciesTree}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com} Emmanuel Paradies
}
\keyword{cluster}
phangorn/man/getClans.Rd 0000644 0001762 0000144 00000014566 13707232051 014704 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/clanistic.R
\name{getClans}
\alias{getClans}
\alias{getSlices}
\alias{getClips}
\alias{getDiversity}
\alias{summary.clanistics}
\alias{diversity}
\title{Clans, slices and clips}
\usage{
getClans(tree)
getSlices(tree)
getClips(tree, all = TRUE)
getDiversity(tree, x, norm = TRUE, var.names = NULL, labels = "new")
\method{summary}{clanistics}(object, ...)
diversity(tree, X)
}
\arguments{
\item{tree}{An object of class phylo or multiPhylo (getDiversity).}
\item{all}{A logical, return all or just the largest clip.}
\item{x}{An object of class phyDat.}
\item{norm}{A logical, return Equitability Index (default) or Shannon
Diversity.}
\item{var.names}{A vector of variable names.}
\item{labels}{see details.}
\item{object}{an object for which a summary is desired.}
\item{...}{Further arguments passed to or from other methods.}
\item{X}{a data.frame}
}
\value{
getClans, getSlices and getClips return a matrix of partitions, a
matrix of ones and zeros where rows correspond to a clan, slice or clip and
columns to tips. A one indicates that a tip belongs to a certain partition.
\cr getDiversity returns a list with tree object, the first is a data.frame
of the equitability index or Shannon divergence and parsimony scores
(p-score) for all trees and variables. The data.frame has two attributes,
the first is a splits object to identify the taxa of each tree and the
second is a splits object containing all partitions that perfectly fit.
}
\description{
Functions for clanistics to compute clans, slices, clips for unrooted trees
and functions to quantify the fragmentation of trees.
}
\details{
Every split in an unrooted tree defines two complementary clans. Thus for an
unrooted binary tree with \eqn{n} leaves there are \eqn{2n - 3} edges, and
therefore \eqn{4n - 6} clans (including \eqn{n} trivial clans containing
only one leave).
Slices are defined by a pair of splits or tripartitions, which are not
clans. The number of distinguishable slices for a binary tree with \eqn{n}
tips is \eqn{2n^2 - 10n + 12}.
%A clip is a different type of partition as it is defined by evolutionary or
cophenetic distance and not by the topology. Namely clips are groups of
leaves for which the maximum pairwise distance is smaller than threshold.
%For a better separation we additionally demand that the maximum pairwise
distance within a clip is lower than the distance between any member of the
clip and any other tip.
A clip is a different type of partition, defining groups of leaves that are
related in terms of evolutionary distances and not only topology. Namely,
clips are groups of leaves for which all pairwise path-length distances are
smaller than a given threshold value (Lapointe et al. 2010). There exists
different numbers of clips for different thresholds, the largest (and
trivial) one being the whole tree. There is always a clip containing only
the two leaves with the smallest pairwise distance.
Clans, slices and clips can be used to characterize how well a vector of
categorial characters (natives/intruders) fit on a tree. We will follow the
definitions of Lapointe et al.(2010). A complete clan is a clan that
contains all leaves of a given state/color, but can also contain leaves of
another state/color. A clan is homogeneous if it only contains leaves of one
state/color.
\code{getDiversity} computes either the \cr Shannon Diversity: \eqn{H =
-\sum_{i=1}^{k}(N_i/N) log(N_i/N), N=\sum_{i=1}^{k} N_i}{H = -sum(N_i/N) *
log(N_i/N), N=sum(N_i)} \cr or the \cr Equitability Index: \eqn{E = H /
log(N)} \cr where \eqn{N_i} are the sizes of the \eqn{k} largest homogeneous
clans of intruders. If the categories of the data can be separated by an
edge of the tree then the E-value will be zero, and maximum equitability
(E=1) is reached if all intruders are in separate clans. getDiversity
computes these Intruder indices for the whole tree, complete clans and
complete slices. Additionally the parsimony scores (p-scores) are reported.
The p-score indicates if the leaves contain only one color (p-score=0), if
the the leaves can be separated by a single split (perfect clan, p-score=1)
or by a pair of splits (perfect slice, p-score=2).
So far only 2 states are supported (native, intruder), however it is also
possible to recode several states into the native or intruder state using
contrasts, for details see section 2 in vignette("phangorn-specials").
Furthermore unknown character states are coded as ambiguous character, which
can act either as native or intruder minimizing the number of clans or
changes (in parsimony analysis) needed to describe a tree for given data.
Set attribute labels to "old" for analysis as in Schliep et al. (2010) or to
"new" for names which are more intuitive.
\code{diversity} returns a data.frame with the parsimony score for each tree
and each levels of the variables in \code{X}. \code{X} has to be a
\code{data.frame} where each column is a factor and the rownames of \code{X}
correspond to the tips of the trees.
%TODO See also vignette("Clanistic").
}
\examples{
set.seed(111)
tree <- rtree(10)
getClans(tree)
getClips(tree, all=TRUE)
getSlices(tree)
set.seed(123)
trees <- rmtree(10, 20)
X <- matrix(sample(c("red", "blue", "violet"), 100, TRUE, c(.5,.4, .1)),
ncol=5, dimnames=list(paste('t',1:20, sep=""), paste('Var',1:5, sep="_")))
x <- phyDat(X, type = "USER", levels = c("red", "blue"), ambiguity="violet")
plot(trees[[1]], "u", tip.color = X[trees[[1]]$tip,1]) # intruders are blue
(divTab <- getDiversity(trees, x, var.names=colnames(X)))
summary(divTab)
}
\references{
Lapointe, F.-J., Lopez, P., Boucher, Y., Koenig, J., Bapteste,
E. (2010) Clanistics: a multi-level perspective for harvesting unrooted gene
trees. \emph{Trends in Microbiology} 18: 341-347
Wilkinson, M., McInerney, J.O., Hirt, R.P., Foster, P.G., Embley, T.M.
(2007) Of clades and clans: terms for phylogenetic relationships in unrooted
trees. \emph{Trends in Ecology and Evolution} 22: 114-115
Schliep, K., Lopez, P., Lapointe F.-J., Bapteste E. (2011) Harvesting
Evolutionary Signals in a Forest of Prokaryotic Gene Trees, \emph{Molecular
Biology and Evolution} 28(4): 1393-1405
}
\seealso{
\code{\link{parsimony}}, Consistency index \code{\link{CI}},
Retention index \code{\link{RI}}, \code{\link{phyDat}}
}
\author{
Klaus Schliep \email{klaus.schliep@snv.jussieu.fr}
Francois-Joseph Lapointe \email{francois-joseph.lapointe@umontreal.ca}
}
\keyword{cluster}
phangorn/man/delta.score.Rd 0000644 0001762 0000144 00000002164 13707232051 015336 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/delta.score.R
\name{delta.score}
\alias{delta.score}
\title{Computes the \eqn{\delta} score}
\usage{
delta.score(x, arg = "mean", ...)
}
\arguments{
\item{x}{an object of class \code{phyDat}}
\item{arg}{Specifies the return value, one of "all", "mean" or "sd"}
\item{...}{further arguments passed through \code{dist.hamming}}
}
\value{
A vector containing the \eqn{\delta} scores.
}
\description{
Computes the treelikeness
}
\examples{
data(yeast)
hist(delta.score(yeast, "all"))
}
\references{
BR Holland, KT Huber, A Dress, V Moulton (2002) \eqn{\delta}
Plots: a tool for analyzing phylogenetic distance data Russell D. Gray,
David Bryant, Simon J. Greenhill (2010) On the shape and fabric of human
history \emph{Molecular Biology and Evolution}, \bold{19(12)} 2051--2059
Russell D. Gray, David Bryant, Simon J. Greenhill (2010) On the shape and
fabric of human history \emph{Phil. Trans. R. Soc. B}, \bold{365}
3923--3933; DOI: 10.1098/rstb.2010.0162
}
\seealso{
\code{\link{dist.hamming}}
}
\author{
Alastair Potts and Klaus Schliep
}
\keyword{cluster}
phangorn/man/yeast.Rd 0000644 0001762 0000144 00000000770 13707232051 014261 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/phangorn-package.R
\docType{data}
\name{yeast}
\alias{yeast}
\title{Yeast alignment (Rokas et al.)}
\description{
Alignment of 106 genes of 8 different species of yeast.
}
\examples{
data(yeast)
str(yeast)
}
\references{
Rokas, A., Williams, B. L., King, N., and Carroll, S. B. (2003)
Genome-scale approaches to resolving incongruence in molecular phylogenies.
\emph{Nature}, \bold{425}(6960): 798--804
}
\keyword{datasets}
phangorn/man/bab.Rd 0000644 0001762 0000144 00000004026 14140462313 013654 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/bab.R
\name{bab}
\alias{bab}
\alias{BranchAndBound}
\title{Branch and bound for finding all most parsimonious trees}
\usage{
bab(data, tree = NULL, trace = 1, ...)
}
\arguments{
\item{data}{an object of class phyDat.}
\item{tree}{a phylogenetic tree an object of class phylo, otherwise a
pratchet search is performed.}
\item{trace}{defines how much information is printed during optimization.}
\item{\dots}{Further arguments passed to or from other methods}
}
\value{
\code{bab} returns all most parsimonious trees in an object of class
\code{multiPhylo}.
}
\description{
\code{bab} finds all most parsimonious trees.
}
\details{
This implementation is very slow and depending on the data may take very
long time. In the worst case all (2n-5)!! possible trees have to be
examined, where n is the number of species / tips. For 10 species there are
already 2027025 tip-labelled unrooted trees. It only uses some basic
strategies to find a lower and upper bounds similar to penny from phylip.
\code{bab} uses a very basic heuristic approach of MinMax Squeeze
(Holland et al. 2005) to improve the lower bound. On the positive side
\code{bab} is not like many other implementations restricted to binary or
nucleotide data.
}
\examples{
data(yeast)
dfactorial(11)
# choose only the first two genes
gene12 <- yeast[, 1:3158]
trees <- bab(gene12)
}
\references{
Hendy, M.D. and Penny D. (1982) Branch and bound algorithms to
determine minimal evolutionary trees. \emph{Math. Biosc.} \bold{59},
277-290
Holland, B.R., Huber, K.T. Penny, D. and Moulton, V. (2005) The MinMax
Squeeze: Guaranteeing a Minimal Tree for Population Data, \emph{Molecular
Biology and Evolution}, \bold{22}, 235--242
White, W.T. and Holland, B.R. (2011) Faster exact maximum parsimony search
with XMP. \emph{Bioinformatics}, \bold{27(10)},1359--1367
}
\seealso{
\code{\link{pratchet}}, \code{\link{dfactorial}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com} based on work on Liam
Revell
}
\keyword{cluster}
phangorn/man/add.tips.Rd 0000644 0001762 0000144 00000001605 14140465255 014646 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treeManipulation.R
\name{add.tips}
\alias{add.tips}
\title{Add tips to a tree}
\usage{
add.tips(tree, tips, where, edge.length = NULL)
}
\arguments{
\item{tree}{an object of class "phylo".}
\item{tips}{a character vector containing the names of the tips.}
\item{where}{an integer or character vector of the same length as tips giving
the number of the node or tip of the tree where to add the new tips.}
\item{edge.length}{optional numeric vector with edge length}
}
\value{
an object of class phylo
}
\description{
This function binds tips to nodes of a phylogenetic trees.
}
\examples{
tree <- rcoal(10)
plot(tree)
nodelabels()
tiplabels()
tree1 <- add.tips(tree, c("A", "B", "C"), c(1,2,15))
plot(tree1)
}
\seealso{
\code{\link{bind.tree}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/dist.p.Rd 0000644 0001762 0000144 00000006230 14140461515 014333 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/dist.p.R
\name{dist.p}
\alias{dist.p}
\title{Pairwise Polymorphism P-Distances from DNA Sequences}
\usage{
dist.p(x, cost = "polymorphism", ignore.indels = TRUE)
}
\arguments{
\item{x}{a matrix containing DNA sequences; this must be of class "phyDat"
(use as.phyDat to convert from DNAbin objects).}
\item{cost}{A cost matrix or "polymorphism" for a predefined one.}
\item{ignore.indels}{a logical indicating whether gaps are treated as fifth
state or not. Warning, each gap site is treated as a characters, so an an
indel that spans a number of base positions would be treated as multiple
character states.}
}
\value{
an object of class \code{dist}.
}
\description{
This function computes a matrix of pairwise uncorrected polymorphism
p-distances. Polymorphism p-distances include intra-individual site
polymorphisms (2ISPs; e.g. "R") when calculating genetic distances.
}
\details{
The polymorphism p-distances (Potts et al. 2014) have been developed to
analyse intra-individual variant polymorphism. For example, the widely used
ribosomal internal transcribed spacer (ITS) region (e.g. Alvarez and Wendel,
2003) consists of 100's to 1000's of units within array across potentially
multiple nucleolus organizing regions (Bailey et al., 2003; Goeker and
Grimm, 2008). This can give rise to intra-individual site polymorphisms
(2ISPs) that can be detected from direct-PCR sequencing or cloning . Clone
consensus sequences (see Goeker and Grimm, 2008) can be analysed with this
function.
}
\examples{
data(Laurasiatherian)
laura <- as.DNAbin(Laurasiatherian)
dm <- dist.p(Laurasiatherian, "polymorphism")
########################################################
# Dealing with indel 2ISPs
# These can be coded using an "x" in the alignment. Note
# that as.character usage in the read.dna() function.
#########################################################
cat("3 5",
"No305 ATRA-",
"No304 ATAYX",
"No306 ATAGA",
file = "exdna.txt", sep = "\n")
(ex.dna <- read.dna("exdna.txt", format = "sequential", as.character=TRUE))
dat <- phyDat(ex.dna, "USER", levels=unique(as.vector(ex.dna)))
dist.p(dat)
unlink("exdna.txt")
}
\references{
Alvarez, I., and J. F. Wendel. (2003) Ribosomal ITS sequences
and plant phylogenetic inference. \emph{ Molecular Phylogenetics and
Evolution}, \bold{29}, 417--434.
Bailey, C. D., T. G. Carr, S. A. Harris, and C. E. Hughes. (2003)
Characterization of angiosperm nrDNA polymorphism, paralogy, and
pseudogenes. \emph{Molecular Phylogenetics and Evolution} \bold{29},
435--455.
Goeker, M., and G. Grimm. (2008) General functions to transform associate
data to host data, and their use in phylogenetic inference from sequences
with intra-individual variability. \emph{BMC Evolutionary Biology},
\bold{8}:86.
Potts, A.J., T.A. Hedderson, and G.W. Grimm. (2014) Constructing phylogenies
in the presence of intra-individual site polymorphisms (2ISPs) with a focus
on the nuclear ribosomal cistron. \emph{Systematic Biology}, \bold{63},
1--16
}
\seealso{
\code{\link[ape]{dist.dna}}, \code{\link[phangorn]{dist.hamming}}
}
\author{
Klaus Schliep and Alastair Potts
}
\keyword{cluster}
phangorn/man/codonTest.Rd 0000644 0001762 0000144 00000005721 14140464176 015106 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codonTest.R
\name{codonTest}
\alias{codonTest}
\title{codonTest}
\usage{
codonTest(tree, object, model = c("M0", "M1a", "M2a"),
frequencies = "F3x4", opt_freq = FALSE, codonstart = 1,
control = pml.control(maxit = 20), ...)
}
\arguments{
\item{tree}{a phylogenetic tree.}
\item{object}{an object of class phyDat.}
\item{model}{a vector containing the substitution models to compare with
each other or "all" to test all available models.}
\item{frequencies}{a character string or vector defining how to compute
the codon frequencies}
\item{opt_freq}{optimize frequencies (so far ignored)}
\item{codonstart}{an integer giving where to start the translation. This
should be 1, 2, or 3, but larger values are accepted and have for effect to
start the translation further within the sequence.}
\item{control}{a list of parameters for controlling the fitting process.}
\item{...}{further arguments passed to or from other methods.}
}
\value{
A list with an element called summary containing a data.frame with
the log-likelihood, number of estimated parameters, etc. of all tested
models. An object called posterior which contains the posterior probability
for the rate class for each sites and the estimates of the defined models.
}
\description{
Models for detecting positive selection
}
\details{
\code{codonTest} allows to test for positive selection similar to programs
like PAML (Yang ) or HyPhy (Kosakovsky Pond et al. 2005).
There are several options for deriving the codon frequencies.
Frequencies can be "equal" (1/61), derived from nucleotide frequencies "F1x4"
and "F3x4" or "empirical" codon frequencies. The frequencies taken using
the empirical frequencies or estimated via maximum likelihood.
So far the M0 model (Goldman and Yang 2002), M1a and M2a are
implemented. The M0 model is always computed the other are optional.
The convergence may be very slow and sometimes fails.
}
\examples{
\dontrun{
# load woodmouse data from ape
data(woodmouse)
dat_codon <- dna2codon(as.phyDat(woodmouse))
tree <- NJ(dist.ml(dat_codon))
# optimize the model the old way
fit <- pml(tree, dat_codon, bf="F3x4")
M0 <- optim.pml(fit, model="codon1")
# Now using the codonTest function
fit_codon <- codonTest(tree, dat_codon)
fit_codon
plot(fit_codon, "M1a")
}
}
\references{
Ziheng Yang (2014). \emph{Molecular Evolution: A Statistical
Approach}. Oxford University Press, Oxford
Sergei L. Kosakovsky Pond, Simon D. W. Frost, Spencer V. Muse (2005) HyPhy:
hypothesis testing using phylogenies, \emph{Bioinformatics}, \bold{21(5)}:
676--679, doi:10.1093/bioinformatics/bti079
Nielsen, R., and Z. Yang. (1998) Likelihood models for detecting positively
selected amino acid sites and applications to the HIV-1 envelope gene.
\emph{Genetics}, \bold{148}: 929--936
}
\seealso{
\code{\link{pml}}, \code{\link{pmlMix}}, \code{\link{modelTest}},
\code{\link[stats]{AIC}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/as.splits.Rd 0000644 0001762 0000144 00000005531 13707232051 015054 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networx.R, R/splits.R
\name{allSplits}
\alias{allSplits}
\alias{allCircularSplits}
\alias{as.splits}
\alias{splits}
\alias{as.Matrix}
\alias{distinct.splits}
\alias{as.phylo.splits}
\alias{addTrivialSplits}
\alias{removeTrivialSplits}
\alias{matchSplits}
\alias{as.matrix.splits}
\alias{as.Matrix.splits}
\alias{print.splits}
\alias{c.splits}
\alias{unique.splits}
\alias{as.splits.phylo}
\alias{as.splits.multiPhylo}
\alias{as.splits.networx}
\alias{as.prop.part.splits}
\alias{as.bitsplits.splits}
\alias{as.splits.bitsplits}
\alias{compatible}
\title{Splits representation of graphs and trees.}
\usage{
allSplits(k, labels = NULL)
allCircularSplits(k, labels = NULL)
as.splits(x, ...)
\method{as.matrix}{splits}(x, zero.print = 0L, one.print = 1L, ...)
\method{as.Matrix}{splits}(x, ...)
\method{print}{splits}(x, maxp = getOption("max.print"), zero.print = ".", one.print = "|", ...)
\method{c}{splits}(..., recursive = FALSE)
\method{unique}{splits}(x, incomparables = FALSE, unrooted = TRUE, ...)
\method{as.splits}{phylo}(x, ...)
\method{as.splits}{multiPhylo}(x, ...)
\method{as.splits}{networx}(x, ...)
\method{as.prop.part}{splits}(x, ...)
\method{as.bitsplits}{splits}(x)
\method{as.splits}{bitsplits}(x, ...)
compatible(obj)
}
\arguments{
\item{k}{number of taxa.}
\item{labels}{names of taxa.}
\item{x}{An object of class phylo or multiPhylo.}
\item{\dots}{Further arguments passed to or from other methods.}
\item{zero.print}{character which should be printed for zeros.}
\item{one.print}{character which should be printed for ones.}
\item{maxp}{integer, default from \code{options(max.print)}, influences how
many entries of large matrices are printed at all.}
\item{recursive}{logical. If recursive = TRUE, the function recursively
descends through lists (and pairlists) combining all their elements into a
vector.}
\item{incomparables}{only for compatibility so far.}
\item{unrooted}{todo.}
\item{obj}{an object of class splits.}
}
\value{
\code{as.splits} returns an object of class splits, which is mainly
a list of splits and some attributes. Often a \code{splits} object will
contain attributes \code{confidences} for bootstrap or Bayesian support
values and \code{weight} storing edge weights.
\code{compatible} return a lower triangular matrix where an 1 indicates that
two splits are incompatible.
}
\description{
\code{as.splits} produces a list of splits or bipartitions.
}
\note{
The internal representation is likely to change.
}
\examples{
(sp <- as.splits(rtree(5)))
write.nexus.splits(sp)
spl <- allCircularSplits(5)
plot(as.networx(spl), "2D")
}
\seealso{
\code{\link{prop.part}}, \code{\link{lento}},
\code{\link{as.networx}}, \code{\link{distanceHadamard}},
\code{\link{read.nexus.splits}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/SH.test.Rd 0000644 0001762 0000144 00000003367 14140462313 014427 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/SH.R
\name{SH.test}
\alias{SH.test}
\title{Shimodaira-Hasegawa Test}
\usage{
SH.test(..., B = 10000, data = NULL, weight = NULL)
}
\arguments{
\item{...}{either a series of objects of class \code{"pml"} separated by
commas, a list containing such objects or an object of class \code{"pmlPart"}
or a matrix containing the site-wise likelihoods in columns.}
\item{B}{the number of bootstrap replicates.}
\item{data}{an object of class \code{"phyDat"}.}
\item{weight}{if a matrix with site (log-)likelihoods is is supplied an
optional vector containing the number of occurrences of each site pattern.}
}
\value{
a numeric vector with the P-value associated with each tree given in
\code{...}.
}
\description{
This function computes the Shimodaira--Hasegawa test for a set of trees.
}
\examples{
data(Laurasiatherian)
dm <- dist.logDet(Laurasiatherian)
tree1 <- NJ(dm)
tree2 <- unroot(upgma(dm))
fit1 <- pml(tree1, Laurasiatherian)
fit2 <- pml(tree2, Laurasiatherian)
fit1 <- optim.pml(fit1) # optimize edge weights
fit2 <- optim.pml(fit2)
# with pml objects as input
SH.test(fit1, fit2, B=1000)
# in real analysis use larger B, e.g. 10000
# with matrix as input
X <- matrix(c(fit1$siteLik, fit2$siteLik), ncol=2)
SH.test(X, weight=attr(Laurasiatherian, "weight"), B=1000)
\dontrun{
example(pmlPart)
SH.test(sp, B=1000)
}
}
\references{
Shimodaira, H. and Hasegawa, M. (1999) Multiple comparisons of
log-likelihoods with applications to phylogenetic inference. \emph{Molecular
Biology and Evolution}, \bold{16}, 1114--1116.
}
\seealso{
\code{\link{pml}}, \code{\link{pmlPart}}, \code{\link{pmlCluster}},
\code{\link{SOWH.test}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{models}
phangorn/man/treedist.Rd 0000644 0001762 0000144 00000010610 14073022437 014753 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treedist.R
\name{treedist}
\alias{treedist}
\alias{sprdist}
\alias{SPR.dist}
\alias{RF.dist}
\alias{wRF.dist}
\alias{KF.dist}
\alias{path.dist}
\title{Distances between trees}
\usage{
treedist(tree1, tree2, check.labels = TRUE)
sprdist(tree1, tree2)
SPR.dist(tree1, tree2 = NULL)
RF.dist(tree1, tree2 = NULL, normalize = FALSE, check.labels = TRUE,
rooted = FALSE)
wRF.dist(tree1, tree2 = NULL, normalize = FALSE, check.labels = TRUE,
rooted = FALSE)
KF.dist(tree1, tree2 = NULL, check.labels = TRUE, rooted = FALSE)
path.dist(tree1, tree2 = NULL, check.labels = TRUE, use.weight = FALSE)
}
\arguments{
\item{tree1}{A phylogenetic tree (class \code{phylo}) or vector of trees (an
object of class \code{multiPhylo}). See details}
\item{tree2}{A phylogenetic tree.}
\item{check.labels}{compares labels of the trees.}
\item{normalize}{compute normalized RF-distance, see details.}
\item{rooted}{take bipartitions for rooted trees into account, default is
unrooting the trees.}
\item{use.weight}{use edge.length argument or just count number of edges on
the path (default)}
}
\value{
\code{treedist} returns a vector containing the following tree
distance methods \item{symmetric.difference}{symmetric.difference or
Robinson-Foulds distance}
\item{branch.score.difference}{branch.score.difference}
\item{path.difference}{path.difference}
\item{weighted.path.difference}{weighted.path.difference}
}
\description{
\code{treedist} computes different tree distance methods and \code{RF.dist}
the Robinson-Foulds or symmetric distance. The Robinson-Foulds distance only
depends on the topology of the trees. If edge weights should be considered
\code{wRF.dist} calculates the weighted RF distance (Robinson & Foulds
1981). and \code{KF.dist} calculates the branch score distance (Kuhner &
Felsenstein 1994). \code{path.dist} computes the path difference metric as
described in Steel and Penny 1993).
\code{sprdist} computes the approximate SPR distance (Oliveira Martins et
al. 2008, de Oliveira Martins 2016).
}
\details{
The Robinson-Foulds distance between two trees \eqn{T_1} and \eqn{T_2} with
\eqn{n} tips is defined as (following the notation Steel and Penny 1993):
\deqn{d(T_1, T_2) = i(T_1) + i(T_2) - 2v_s(T_1, T_2)} where \eqn{i(T_1)}
denotes the number of internal edges and \eqn{v_s(T_1, T_2)} denotes the
number of internal splits shared by the two trees. The normalized
Robinson-Foulds distance is derived by dividing \eqn{d(T_1, T_2)} by the
maximal possible distance \eqn{i(T_1) + i(T_2)}. If both trees are unrooted
and binary this value is \eqn{2n-6}.
Functions like \code{RF.dist} returns the Robinson-Foulds distance (Robinson
and Foulds 1981) between either 2 trees or computes a matrix of all pairwise
distances if a \code{multiPhylo} object is given.
For large number of trees the distance functions can use a lot of memory!
}
\examples{
tree1 <- rtree(100, rooted=FALSE)
tree2 <- rSPR(tree1, 3)
RF.dist(tree1, tree2)
treedist(tree1, tree2)
sprdist(tree1, tree2)
trees <- rSPR(tree1, 1:5)
SPR.dist(tree1, trees)
}
\references{
de Oliveira Martins L., Leal E., Kishino H. (2008)
\emph{Phylogenetic Detection of Recombination with a Bayesian Prior on the
Distance between Trees}. PLoS ONE \bold{3(7)}. e2651. doi:
10.1371/journal.pone.0002651
de Oliveira Martins L., Mallo D., Posada D. (2016) \emph{A Bayesian
Supertree Model for Genome-Wide Species Tree Reconstruction}. Syst. Biol.
\bold{65(3)}: 397-416, doi:10.1093/sysbio/syu082
Steel M. A. and Penny P. (1993) \emph{Distributions of tree comparison
metrics - some new results}, Syst. Biol., \bold{42(2)}, 126--141
Kuhner, M. K. and Felsenstein, J. (1994) \emph{A simulation comparison of
phylogeny algorithms under equal and unequal evolutionary rates}, Molecular
Biology and Evolution, \bold{11(3)}, 459--468
D.F. Robinson and L.R. Foulds (1981) \emph{Comparison of phylogenetic
trees}, Mathematical Biosciences, \bold{53(1)}, 131--147
D.F. Robinson and L.R. Foulds (1979) Comparison of weighted labelled trees.
In Horadam, A. F. and Wallis, W. D. (Eds.), \emph{Combinatorial Mathematics
VI: Proceedings of the Sixth Australian Conference on Combinatorial
Mathematics, Armidale, Australia}, 119--126
}
\seealso{
\code{\link[ape]{dist.topo}}, \code{\link{nni}},
\code{\link{superTree}}, \code{\link{mast}}
}
\author{
Klaus P. Schliep \email{klaus.schliep@gmail.com},
Leonardo de Oliveira Martins
}
\keyword{classif}
phangorn/man/splitsNetwork.Rd 0000644 0001762 0000144 00000003730 13707232051 016023 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/splitsNetwork.R
\name{splitsNetwork}
\alias{splitsNetwork}
\title{Phylogenetic Network}
\usage{
splitsNetwork(dm, splits = NULL, gamma = 0.1, lambda = 1e-06, weight = NULL)
}
\arguments{
\item{dm}{A distance matrix.}
\item{splits}{a splits object, containing all splits to consider, otherwise
all possible splits are used}
\item{gamma}{penalty value for the L1 constraint.}
\item{lambda}{penalty value for the L2 constraint.}
\item{weight}{a vector of weights.}
}
\value{
\code{splitsNetwork} returns a splits object with a matrix added.
The first column contains the indices of the splits, the second column an
unconstrained fit without penalty terms and the third column the constrained
fit.
}
\description{
\code{splitsNetwork} estimates weights for a splits graph from a distance
matrix.
}
\details{
\code{splitsNetwork} fits non-negative least-squares phylogenetic networks
using L1 (LASSO), L2(ridge regression) constraints. The function minimizes
the penalized least squares
\deqn{\beta = min \sum(dm - X\beta)^2 + \lambda \|\beta \|^2_2 }{ beta = sum(dm - X*beta)^2 + lambda |beta|^2_2 }
with respect to \deqn{\|\beta \|_1 <= \gamma, \beta >= 0}{ |beta|_1 = gamma, beta >= 0}
where \eqn{X} is a design matrix constructed with \code{designSplits}.
External edges are fitted without L1 or L2 constraints.
}
\examples{
data(yeast)
dm <- dist.ml(yeast)
fit <- splitsNetwork(dm)
net <- as.networx(fit)
plot(net, "2D")
write.nexus.splits(fit)
}
\references{
Efron, Hastie, Johnstone and Tibshirani (2004) Least Angle
Regression (with discussion) \emph{Annals of Statistics} \bold{32(2)}, 407--499
K. P. Schliep (2009). Some Applications of statistical phylogenetics (PhD
Thesis)
}
\seealso{
\code{\link[phangorn]{distanceHadamard}},
\code{\link[phangorn]{designTree}} \code{\link[phangorn]{consensusNet}},
\code{\link[phangorn]{plot.networx}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/read.phyDat.Rd 0000644 0001762 0000144 00000004504 14030677772 015313 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/read.phyDat.R
\name{read.phyDat}
\alias{read.phyDat}
\alias{write.phyDat}
\title{Import and export sequence alignments}
\usage{
read.phyDat(file, format = "phylip", type = "DNA", ...)
write.phyDat(x, file, format = "phylip", colsep = "", nbcol = -1, ...)
}
\arguments{
\item{file}{a file name specified by either a variable of mode character, or
a double-quoted string.}
\item{format}{File format of the sequence alignment (see details). Several
popular formats are supported: "phylip", "interleaved", "sequential",
"clustal", "fasta" or "nexus", or any unambiguous abbreviation of these.}
\item{type}{Type of sequences ("DNA", "AA", "CODON" or "USER").}
\item{...}{further arguments passed to or from other methods.}
\item{x}{An object of class \code{phyDat}.}
\item{colsep}{a character used to separate the columns (a single space by
default).}
\item{nbcol}{a numeric specifying the number of columns per row (-1 by
default); may be negative implying that the nucleotides are printed on a
single line.}
}
\value{
\code{read.phyDat} returns an object of class phyDat,
\code{write.phyDat} write an alignment to a file.
}
\description{
These functions read and write sequence alignments.
}
\details{
\code{write.phyDat} calls the function \code{\link[ape]{write.dna}} or
\code{\link[ape]{write.nexus.data}} and \code{read.phyDat} calls the function
\code{\link[ape]{read.dna}}, \code{read.aa} or \code{read.nexus.data}, so see
for more details over there.
You may import data directly with \code{\link[ape]{read.dna}} or
\code{\link[ape]{read.nexus.data}} and convert the data to class phyDat.
}
\examples{
fdir <- system.file("extdata/trees", package = "phangorn")
primates <- read.phyDat(file.path(fdir, "primates.dna"),
format = "interleaved")
}
\references{
% Anonymous. FASTA format description. %
\url{https://www.ncbi.nlm.nih.gov/blast/fasta.shtml} Felsenstein, J. (1993)
Phylip (Phylogeny Inference Package) version 3.5c. Department of Genetics,
University of Washington.
\url{https://evolution.genetics.washington.edu/phylip/phylip.html}
}
\seealso{
\code{\link[ape]{read.dna}}, \code{\link[ape]{read.GenBank}},
\code{\link[phangorn]{phyDat}}, \code{\link[seqinr]{read.alignment}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{IO}
phangorn/man/cladePar.Rd 0000644 0001762 0000144 00000002154 14065643321 014651 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cladePar.R
\name{cladePar}
\alias{cladePar}
\title{Utility function to plot.phylo}
\usage{
cladePar(tree, node, edge.color = "red", tip.color = edge.color,
edge.width = 1, edge.lty = "solid", x = NULL, plot = FALSE, ...)
}
\arguments{
\item{tree}{an object of class phylo.}
\item{node}{the node which is the common ancestor of the clade.}
\item{edge.color}{see plot.phylo.}
\item{tip.color}{see plot.phylo.}
\item{edge.width}{see plot.phylo.}
\item{edge.lty}{see plot.phylo.}
\item{x}{the result of a previous call to cladeInfo.}
\item{plot}{logical, if TRUE the tree is plotted.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
A list containing the information about the edges and tips.
}
\description{
cladePar can help you coloring (choosing edge width/type) of clades.
}
\examples{
tree <- rtree(10)
plot(tree)
nodelabels()
x <- cladePar(tree, 12)
cladePar(tree, 18, "blue", "blue", x=x, plot=TRUE)
}
\seealso{
\code{\link{plot.phylo}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{plot}
phangorn/man/cophenetic.networx.Rd 0000644 0001762 0000144 00000001447 13707232051 016764 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treedist.R
\name{cophenetic.networx}
\alias{cophenetic.networx}
\alias{cophenetic.splits}
\title{Pairwise Distances from a Phylogenetic Network}
\usage{
\method{cophenetic}{networx}(x)
}
\arguments{
\item{x}{an object of class \code{networx}.}
}
\value{
an object of class \code{dist}, names are set according to the tip
labels (as given by the element \code{tip.label} of the argument \code{x}).
}
\description{
\code{cophenetic.networx} computes the pairwise distances between the pairs
of tips from a phylogenetic network using its branch lengths.
}
\seealso{
\code{\link[stats]{cophenetic}} for the generic function,
\code{neighborNet} to construct a network from a distance matrix
}
\author{
Klaus Schliep
}
\keyword{manip}
phangorn/man/distanceHadamard.Rd 0000644 0001762 0000144 00000002046 13707232051 016346 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/hadamard.R
\name{distanceHadamard}
\alias{distanceHadamard}
\title{Distance Hadamard}
\usage{
distanceHadamard(dm, eps = 0.001)
}
\arguments{
\item{dm}{A distance matrix.}
\item{eps}{Threshold value for splits.}
}
\value{
\code{distanceHadamard} returns a matrix. The first column contains
the distance spectra, the second one the edge-spectra. If eps is positive an
object of with all splits greater eps is returned.
}
\description{
Distance Hadamard produces spectra of splits from a distance matrix.
}
\examples{
data(yeast)
dm <- dist.hamming(yeast)
dm <- as.matrix(dm)
fit <- distanceHadamard(dm)
lento(fit)
plot(as.networx(fit), "2D")
}
\references{
Hendy, M. D. and Penny, D. (1993). Spectral Analysis of
Phylogenetic Data. \emph{Journal of Classification}, \bold{10}, 5-24.
}
\seealso{
\code{\link{hadamard}}, \code{\link{lento}},
\code{\link{plot.networx}}, \code{\link{neighborNet}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}, Tim White
}
\keyword{cluster}
phangorn/man/dna2codon.Rd 0000644 0001762 0000144 00000004546 14140527773 015022 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/codon.R
\name{dna2codon}
\alias{dna2codon}
\alias{codon2dna}
\title{Translate nucleic acid sequences into codons}
\usage{
dna2codon(x, codonstart = 1, code = 1, ambiguity = "---", ...)
codon2dna(x)
}
\arguments{
\item{x}{An object containing sequences.}
\item{codonstart}{an integer giving where to start the translation. This
should be 1, 2, or 3, but larger values are accepted and have for effect to
start the translation further within the sequence.}
\item{code}{The ncbi genetic code number for translation (see details).
By default the standard genetic code is used.}
\item{ambiguity}{character for ambiguous character and no contrast is
provided.}
\item{...}{further arguments passed to or from other methods.}
}
\value{
The functions return an object of class \code{phyDat}.
}
\description{
The function transforms \code{dna2codon} DNA sequences to codon sequences,
\code{codon2dna} transform the other way.
}
\details{
The following genetic codes are described here. The number preceding each
corresponds to the code argument.
\tabular{rl}{
1 \tab standard \cr
2 \tab vertebrate.mitochondrial \cr
3 \tab yeast.mitochondrial \cr
4 \tab protozoan.mitochondrial+mycoplasma \cr
5 \tab invertebrate.mitochondrial \cr
6 \tab ciliate+dasycladaceal \cr
9 \tab echinoderm+flatworm.mitochondrial \cr
10 \tab euplotid \cr
11 \tab bacterial+plantplastid \cr
12 \tab alternativeyeast \cr
13 \tab ascidian.mitochondrial \cr
14 \tab alternativeflatworm.mitochondrial \cr
15 \tab blepharism \cr
16 \tab chlorophycean.mitochondrial \cr
21 \tab trematode.mitochondrial \cr
22 \tab scenedesmus.mitochondrial \cr
23 \tab thraustochytrium.mitochondria \cr
24 \tab Pterobranchia.mitochondrial \cr
25 \tab CandidateDivision.SR1+Gracilibacteria \cr
26 \tab Pachysolen.tannophilus
}
Alignment gaps and ambiguities are currently ignored and sites containing
these are deleted.
}
\examples{
data(Laurasiatherian)
class(Laurasiatherian)
Laurasiatherian
dna2codon(Laurasiatherian)
}
\references{
\url{https://www.ncbi.nlm.nih.gov/Taxonomy/taxonomyhome.html/index.cgi?chapter=cgencodes}
}
\seealso{
\code{\link{trans}}, \code{\link{phyDat}} and the chapter 4 in the
\code{vignette("phangorn-specials", package="phangorn")}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/man/Ancestors.Rd 0000644 0001762 0000144 00000004270 13707232051 015074 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/treeManipulation.R
\name{Ancestors}
\alias{Ancestors}
\alias{allDescendants}
\alias{Children}
\alias{Descendants}
\alias{Siblings}
\alias{mrca.phylo}
\title{tree utility function}
\usage{
Ancestors(x, node, type = c("all", "parent"))
allDescendants(x)
Children(x, node)
Descendants(x, node, type = c("tips", "children", "all"))
Siblings(x, node, include.self = FALSE)
mrca.phylo(x, node = NULL, full = FALSE)
}
\arguments{
\item{x}{a tree (a phylo object).}
\item{node}{an integer or a vector of integers corresponding to a node ID}
\item{type}{specify whether to return just direct children / parents or all}
\item{include.self}{whether to include self in list of siblings}
\item{full}{a logical indicating whether to return the MRCAs among all tips
and nodes (if TRUE); the default is to return only the MRCAs among tips.}
}
\value{
a vector or a list containing the indices of the nodes.
}
\description{
Functions for describing relationships among phylogenetic nodes.
}
\details{
These functions are inspired by \code{treewalk} in phylobase package, but
work on the S3 \code{phylo} objects. The nodes are the indices as given in
edge matrix of an phylo object. From taxon labels these indices can be
easily derived matching against the \code{tip.label} argument of an phylo
object, see example below. All the functions allow \code{node} to be either
a scalar or vector. \code{mrca} is a faster version of the mrca in ape, in
phangorn only because of dependencies.
If the argument node is missing the function is evaluated for all nodes.
}
\section{Functions}{
\itemize{
\item \code{allDescendants}: list all the descendant nodes of each node
}}
\examples{
tree <- rtree(10)
plot(tree, show.tip.label = FALSE)
nodelabels()
tiplabels()
Ancestors(tree, 1:3, "all")
Children(tree, 11)
Descendants(tree, 11, "tips")
Siblings(tree, 3)
# Siblings of all nodes
Siblings(tree)
mrca.phylo(tree, 1:3)
mrca.phylo(tree, match(c("t1", "t2", "t3"), tree$tip))
mrca.phylo(tree)
# same as mrca(tree), but faster for large trees
}
\seealso{
\code{treewalk}, \code{\link[ape]{as.phylo}},
\code{\link[ape]{nodelabels}}
}
\keyword{internal}
\keyword{misc}
phangorn/man/mast.Rd 0000644 0001762 0000144 00000002210 14041302175 014064 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/mast.R
\name{mast}
\alias{mast}
\title{Maximum agreement subtree}
\usage{
mast(x, y, tree = TRUE, rooted = TRUE)
}
\arguments{
\item{x}{a tree, i.e. an object of class \code{phylo}.}
\item{y}{a tree, i.e. an object of class \code{phylo}.}
\item{tree}{a logical, if TRUE returns a tree other wise the tip labels
of the the maximum agreement subtree.}
\item{rooted}{logical if TRUE treats trees as rooted otherwise unrooted.}
}
\value{
\code{mast} returns a vector of the tip labels in the MAST.
}
\description{
\code{mast} computes the maximum agreement subtree (MAST).
}
\details{
The code is derived from the code example in Valiente (2009).
The version for the unrooted trees is much slower.
}
\examples{
tree1 <- rtree(100)
tree2 <- rSPR(tree1, 5)
tips <- mast(tree1, tree2)
}
\references{
G. Valiente (2009). \emph{Combinatorial Pattern Matching Algorithms in Computational Biology using Perl and R}. Taylor & Francis/CRC Press
}
\seealso{
\code{\link{SPR.dist}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com} based on code of Gabriel Valiente
}
\keyword{cluster}
phangorn/man/as.networx.Rd 0000644 0001762 0000144 00000004010 14030677772 015250 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/networx.R
\name{as.networx}
\alias{as.networx}
\alias{networx}
\alias{as.networx.splits}
\alias{as.networx.phylo}
\title{Conversion among phylogenetic network objects}
\usage{
as.networx(x, ...)
\method{as.networx}{splits}(x, planar = FALSE, coord = c("none", "2D", "3D"), ...)
\method{as.networx}{phylo}(x, ...)
}
\arguments{
\item{x}{an object of class \code{"splits"} or \code{"phylo"}}
\item{\dots}{Further arguments passed to or from other methods.}
\item{planar}{logical whether to produce a planar graph from only cyclic
splits (may excludes splits).}
\item{coord}{add coordinates of the nodes, allows to reproduce the plot.}
}
\description{
\code{as.networx} convert \code{splits} objects into a \code{networx}
object. And most important there exists a generic \code{plot} function to
plot phylogenetic network or split graphs.
}
\details{
A \code{networx} object hold the information for a phylogenetic
network and extends the \code{phylo} object. Therefore some generic function
for \code{phylo} objects will also work for \code{networx} objects. The
argument \code{planar = TRUE} will create a planar split graph based on a
cyclic ordering. These objects can be nicely plotted in \code{"2D"}.
}
\note{
The internal representation is likely to change.
}
\examples{
set.seed(1)
tree1 <- rtree(20, rooted=FALSE)
sp <- as.splits(rNNI(tree1, n=10))
net <- as.networx(sp)
plot(net)
\dontrun{
# also see example in consensusNet
example(consensusNet)
}
}
\references{
Schliep, K., Potts, A. J., Morrison, D. A. and Grimm, G. W. (2017),
Intertwining phylogenetic trees and networks. \emph{Methods Ecol Evol}.
\bold{8}, 1212--1220. doi:10.1111/2041-210X.12760
}
\seealso{
\code{\link{consensusNet}}, \code{\link{neighborNet}},
\code{\link{splitsNetwork}}, \code{\link{hadamard}},
\code{\link{distanceHadamard}}, \code{\link{plot.networx}},
\code{\link[ape]{evonet}}, \code{\link[ape]{as.phylo}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{plot}
phangorn/man/pmlMix.Rd 0000644 0001762 0000144 00000005614 13707232051 014404 0 ustar ligges users % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/pmlMix.R
\name{pmlMix}
\alias{pmlMix}
\title{Phylogenetic mixture model}
\usage{
pmlMix(formula, fit, m = 2, omega = rep(1/m, m),
control = pml.control(epsilon = 1e-08, maxit = 20, trace = 1), ...)
}
\arguments{
\item{formula}{a formula object (see details).}
\item{fit}{an object of class \code{pml}.}
\item{m}{number of mixtures.}
\item{omega}{mixing weights.}
\item{control}{A list of parameters for controlling the fitting process.}
\item{\dots}{Further arguments passed to or from other methods.}
}
\value{
\code{pmlMix} returns a list with elements
\item{logLik}{log-likelihood of the fit} \item{omega}{mixing weights.}
\item{fits}{fits for the final mixtures.}
}
\description{
Phylogenetic mixture model.
}
\details{
The \code{formula} object allows to specify which parameter get optimized.
The formula is generally of the form \code{edge + bf + Q ~ rate + shape +
\dots{}}, on the left side are the parameters which get optimized over all
mixtures, on the right the parameter which are optimized specific to each
mixture. The parameters available are \code{"nni", "bf", "Q", "inv",
"shape", "edge", "rate"}. Each parameters can be used only once in the
formula. \code{"rate"} and \code{"nni"} are only available for the right
side of the formula. On the other hand parameters for invariable sites are
only allowed on the left-hand side. The convergence of the algorithm is
very slow and is likely that the algorithm can get stuck in local optima.
}
\examples{
\dontrun{
X <- allSitePattern(5)
tree <- read.tree(text = "((t1:0.3,t2:0.3):0.1,(t3:0.3,t4:0.3):0.1,t5:0.5);")
fit <- pml(tree,X, k=4)
weights <- 1000*exp(fit$siteLik)
attr(X, "weight") <- weights
fit1 <- update(fit, data=X, k=1)
fit2 <- update(fit, data=X)
(fitMixture <- pmlMix(edge~rate, fit1 , m=4))
(fit2 <- optim.pml(fit2, optGamma=TRUE))
data(Laurasiatherian)
dm <- dist.logDet(Laurasiatherian)
tree <- NJ(dm)
fit <- pml(tree, Laurasiatherian)
fit <- optim.pml(fit)
fit2 <- update(fit, k=4)
fit2 <- optim.pml(fit2, optGamma=TRUE)
fitMix <- pmlMix(edge ~ rate, fit, m=4)
fitMix
#
# simulation of mixture models
#
\dontrun{
X <- allSitePattern(5)
tree1 <- read.tree(text = "((t1:0.1,t2:0.5):0.1,(t3:0.1,t4:0.5):0.1,t5:0.5);")
tree2 <- read.tree(text = "((t1:0.5,t2:0.1):0.1,(t3:0.5,t4:0.1):0.1,t5:0.5);")
tree1 <- unroot(tree1)
tree2 <- unroot(tree2)
fit1 <- pml(tree1,X)
fit2 <- pml(tree2,X)
weights <- 2000*exp(fit1$siteLik) + 1000*exp(fit2$siteLik)
attr(X, "weight") <- weights
fit1 <- pml(tree1, X)
fit2 <- optim.pml(fit1)
logLik(fit2)
AIC(fit2, k=log(3000))
fitMixEdge <- pmlMix( ~ edge, fit1, m=2)
logLik(fitMixEdge)
AIC(fitMixEdge, k=log(3000))
fit.p <- pmlPen(fitMixEdge, .25)
logLik(fit.p)
AIC(fit.p, k=log(3000))
}
}
}
\seealso{
\code{\link{pml}},\code{\link{pmlPart}},\code{\link{pmlCluster}}
}
\author{
Klaus Schliep \email{klaus.schliep@gmail.com}
}
\keyword{cluster}
phangorn/TODO 0000644 0001762 0000144 00000000445 14137602432 012563 0 ustar ligges users as.splits.phylo trivial=FALSE bip bipart
as.splits.multiPhylo
Mkv
Ancestral
pml.fit4 add Mkv + weight for bootstrap
ratchet.par add minit als parameter
Inv or Mkv / Ascertainment bias
sankoff auf new umstellen:
index.nni
indexNNI
indexNNI3
indexNNI_fitch
combine optBf und optF3x4
phangorn/DESCRIPTION 0000644 0001762 0000144 00000006360 14156232262 013604 0 ustar ligges users Package: phangorn
Title: Phylogenetic Reconstruction and Analysis
Version: 2.8.1
Authors@R:
c(person(given = "Klaus",
family = "Schliep",
role = c("aut", "cre"),
email = "klaus.schliep@gmail.com",
comment = c(ORCID = "0000-0003-2941-0161")),
person(given = "Emmanuel",
family = "Paradis",
role = "aut",
comment = c(ORCID = "0000-0003-3092-2199")),
person(given = "Leonardo",
family = "de Oliveira Martins",
role = "aut",
comment = c(ORCID = "0000-0001-5247-1320")),
person(given = "Alastair",
family = "Potts",
role = "aut"),
person(given = "Tim W.",
family = "White",
role = "aut"),
person(given = "Cyrill",
family = "Stachniss",
role = "ctb"),
person(given = "Michelle",
family = "Kendall",
role = "ctb",
email = "m.kendall@imperial.ac.uk"),
person(given = "Keren",
family = "Halabi",
role = "ctb"),
person(given = "Richel",
family = "Bilderbeek",
role = "ctb"),
person(given = "Kristin",
family = "Winchell",
role = "ctb"),
person(given = "Liam",
family = "Revell",
role = "ctb"),
person(given = "Mike",
family = "Gilchrist",
role = "ctb"),
person(given = "Jeremy",
family = "Beaulieu",
role = "ctb"),
person(given = "Brian",
family = "O'Meara",
role = "ctb"),
person(given = "Long",
family = "Qu",
role = "ctb"))
Description: Allows for estimation of phylogenetic trees and networks
using Maximum Likelihood, Maximum Parsimony, distance methods and
Hadamard conjugation. Offers methods for tree comparison, model
selection and visualization of phylogenetic networks as described in
Schliep et al. (2017) .
License: GPL (>= 2)
URL: https://github.com/KlausVigo/phangorn
BugReports: https://github.com/KlausVigo/phangorn/issues
Depends: ape (>= 5.5), R (>= 4.1.0)
Imports: fastmatch, graphics, grDevices, igraph (>= 1.0), Matrix,
methods, parallel, quadprog, Rcpp, stats, utils
Suggests: Biostrings, knitr, magick, prettydoc, rgl, rmarkdown, seqinr,
seqLogo, tinytest, xtable
LinkingTo: Rcpp
VignetteBuilder: knitr, utils
biocViews: Software, Technology, QualityControl
Encoding: UTF-8
Repository: CRAN
RoxygenNote: 7.1.2
Language: en-US
NeedsCompilation: yes
Packaged: 2021-12-13 14:03:14 UTC; klaus
Author: Klaus Schliep [aut, cre] (),
Emmanuel Paradis [aut] (),
Leonardo de Oliveira Martins [aut]
(),
Alastair Potts [aut],
Tim W. White [aut],
Cyrill Stachniss [ctb],
Michelle Kendall [ctb],
Keren Halabi [ctb],
Richel Bilderbeek [ctb],
Kristin Winchell [ctb],
Liam Revell [ctb],
Mike Gilchrist [ctb],
Jeremy Beaulieu [ctb],
Brian O'Meara [ctb],
Long Qu [ctb]
Maintainer: Klaus Schliep
Date/Publication: 2021-12-15 00:20:02 UTC
phangorn/build/ 0000755 0001762 0000144 00000000000 14155651242 013172 5 ustar ligges users phangorn/build/vignette.rds 0000644 0001762 0000144 00000000603 14155651242 015530 0 ustar ligges users SQO0.c}5ل c_ƭm" [;F>7cs+sviӨ15hoBy@RCHP{1JT$}?~)o>ɱU jdK3>%E2R&̊NjuAJ(ʰc>\0O<-c4'q 1O6Mf rz*qAPH]Ͼ[\B{\Jo_bj(b{'l>yH,# 6}7Ӵ/Gۢ phangorn/tests/ 0000755 0001762 0000144 00000000000 14140752512 013231 5 ustar ligges users phangorn/tests/tinytest.R 0000644 0001762 0000144 00000000424 13707232051 015236 0 ustar ligges users if (require("tinytest", quietly=TRUE)){
set.seed(42)
home <- length(unclass(packageVersion("phangorn"))[[1]]) == 4 # 0.0.0.9000
if(home) {
# Only run locally, let CRAN test examples and the vignette
test_package("phangorn", at_home = home)
}
}
phangorn/src/ 0000755 0001762 0000144 00000000000 14155651242 012662 5 ustar ligges users phangorn/src/lessAndEqual.h 0000644 0001762 0000144 00000005270 13707232051 015413 0 ustar ligges users #ifndef R_R_H
#include
#endif
#ifndef R_INTERNALS_H_
#include
#endif
#include
/*
NOTE: R_NaString is a different SEXP than mkChar("NA"), but holding the same string "NA".
We will treat R_NaString to be smaller than every usual string, including mkChar("NA").
Real NaN becomes mkChar("NaN") by as.character();
Real -Inf becomes mkChar("-Inf") by as.character();
Real Inf becomes mkChar("Inf") by as.character();
*/
class CharSEXP{
public:
SEXP sexp;
inline bool valid() {return( TYPEOF(sexp) == CHARSXP );}
CharSEXP(SEXP x)
{
if (TYPEOF(x) == CHARSXP) sexp = x;
else error("CharSEXP should be initialized with a CHARSXP type object");
}
CharSEXP()
{
sexp = R_NaString;
}
friend inline bool operator< (const CharSEXP& lhs, const CharSEXP& rhs)
{
if (lhs.sexp == R_NaString) return( rhs.sexp != R_NaString );
if (rhs.sexp == R_NaString) return(false);
return(
strcmp(const_cast(CHAR(lhs.sexp)), const_cast(CHAR(rhs.sexp)) )<0
);
}
friend inline bool operator== (const CharSEXP& lhs, const CharSEXP& rhs)
{
return (lhs.sexp == rhs.sexp); // R CHARSXP objects are cached (only one copy per string)
}
};
/* for general T where operator< and operator== have been implemented;
* this is helpful for
* (1) integers where NA's is just a special integer value;
* (2) unsigned char, where NA's is converted to 00;
* (3) CharSEXP, where NA's are properly handled by operator<.
*/
template
bool lessThan (const T& lhs, const T& rhs) { return lhs < rhs;}
template
bool equalTo (const T& lhs, const T& rhs) { return lhs == rhs;}
/* double Assumptions: NaN < NA_real_ < -Inf < Finite numbers < Inf */
template <>
inline bool lessThan(const double& lhs, const double& rhs)
{
if (R_FINITE(lhs) && R_FINITE(rhs)) return lhs< rhs; // probably the most common case (both finite)
bool rhsTest = R_IsNaN(rhs); // rhs = NaN
if (R_IsNaN(lhs)) return !rhsTest; // lhs = NaN
rhsTest = rhsTest || ISNA(rhs); // rhs <= NA_real_
if (ISNA(lhs)) return !rhsTest; // lhs = NA
rhsTest = rhsTest || (rhs == R_NegInf); // rhs <= -Inf
if (lhs == R_NegInf) return !rhsTest; // lhs = -Inf
if(rhsTest) return false; // lhs is finite or +Inf but rhs <= -Inf
return R_FINITE(lhs); // lhs is finite or +Inf but rhs is +Inf
}
template <>
inline bool equalTo (const double& lhs, const double& rhs)
{return(
(lhs == rhs) ||
(ISNA(lhs) && ISNA(rhs)) ||
(R_IsNaN(lhs) && R_IsNaN(rhs))
);}
phangorn/src/dupAtomMat.cpp 0000644 0001762 0000144 00000003457 13707232051 015445 0 ustar ligges users #include "rcSet.h"
vecMap intVecMap;
vecMap doubleVecMap;
vecMap charsexpVecMap;
vecMap rawVecMap; // Rbyte is an alias of unsigned char
extern "C" {
SEXP grpDupAtomMat(SEXP x, SEXP MARGIN, SEXP fromLast)
{/* returns an integer vector of duplicated rows of numeric matrix x */
SEXP out;
int* dim;
int nGrps;
dim=INTEGER(getAttrib(x, R_DimSymbol));
out = PROTECT(allocVector(INTSXP, dim[*INTEGER(MARGIN)-1]));
switch (TYPEOF(x)) {
case REALSXP:
nGrps = doubleVecMap.grpDuplicatedMat (REAL(x), dim, dim+1, INTEGER(out), *INTEGER(MARGIN)==1, (bool)(*(LOGICAL(fromLast))) );
break;
case INTSXP: // factor type is also covered here
// if(!inherits(x, "factor"))
nGrps = intVecMap.grpDuplicatedMat (INTEGER(x), dim, dim+1, INTEGER(out), *INTEGER(MARGIN)==1, (bool)(*(LOGICAL(fromLast))) );
// else {;}
break;
case LGLSXP:
nGrps = intVecMap.grpDuplicatedMat (LOGICAL(x), dim, dim+1, INTEGER(out), *INTEGER(MARGIN)==1, (bool)(*(LOGICAL(fromLast))) );
break;
case STRSXP: {
CharSEXP* charSexpPtr = new CharSEXP [ dim[0]*dim[1] ];
for(int i=dim[0]*dim[1]-1; i>=0; --i)
charSexpPtr[i].sexp = STRING_ELT(x, i);
nGrps = charsexpVecMap.grpDuplicatedMat (charSexpPtr, dim, dim+1, INTEGER(out), *INTEGER(MARGIN)==1, (bool)(*(LOGICAL(fromLast))) );
delete[] charSexpPtr;
break;
}
case RAWSXP:
nGrps = rawVecMap.grpDuplicatedMat (RAW(x), dim, dim+1, INTEGER(out), *INTEGER(MARGIN)==1, (bool)(*(LOGICAL(fromLast))) );
break;
default:
error("C function 'grpDupAtomMat' only accepts REALSXP, LGLSXP, INTSXP and STRSXP");
}
SEXP nLevels;
nLevels = PROTECT(allocVector(INTSXP, 1));
INTEGER(nLevels)[0] = nGrps;
setAttrib(out, install("nlevels"), nLevels);
UNPROTECT(2);
return out;
}
}
phangorn/src/phangorn_utils.h 0000644 0001762 0000144 00000001545 13743613604 016076 0 ustar ligges users #ifndef PHANGORNUTILS_H
#define PHANGORNUTILS_H
#include
using namespace Rcpp;
List allDescCPP(IntegerMatrix orig, int nTips);
List bipartCPP(IntegerMatrix orig, int nTips);
std::vector< std::vector > bipCPP(IntegerMatrix orig, int nTips);
List allChildrenCPP(const IntegerMatrix orig);
List allSiblingsCPP(const IntegerMatrix & edge);
IntegerVector p2dna(NumericMatrix xx, double eps=0.999);
NumericVector node_height_cpp(IntegerVector edge1, IntegerVector edge2,
NumericVector edge_length);
NumericVector cophenetic_cpp(IntegerMatrix edge, NumericVector edge_length,
int nTips, int nNode);
IntegerVector threshStateC(NumericVector x, NumericVector thresholds);
int countCycles_cpp(IntegerMatrix M);
std::vector getIndex(IntegerVector left, IntegerVector right, int n);
#endif
phangorn/src/ml.c 0000644 0001762 0000144 00000074677 14137217607 013466 0 ustar ligges users /*
* ml.c
*
* (c) 2008-2021 Klaus Schliep (klaus.schliep@gmail.com)
*
*
* This code may be distributed under the GNU GPL
*
*/
#define USE_FC_LEN_T
#include
#include
#include
#include
#ifndef FCONE
# define FCONE
#endif
#define LINDEX(i, k) (i - ntips - 1L) * (nr * nc) + k * ntips * (nr * nc)
// index for LL
#define LINDEX2(i, k) (i - *ntips - 1L) * (*nr* *nc) + k * *ntips * (*nr * *nc)
// index for scaling matrix SCM
#define LINDEX3(i, j) (i - *ntips - 1L) * *nr + j * *ntips * *nr //nr statt *nr
double one = 1.0, zero = 0.0;
int ONE = 1L;
const double ScaleEPS = 1.0/4294967296.0;
const double ScaleMAX = 4294967296.0;
const double LOG_SCALE_EPS = -22.18070977791824915926;
static double *LL; //, *WEIGHTS;
static int *SCM; //, *XXX;
SEXP ll_free2(){
free(LL);
free(SCM);
return R_NilValue;
}
SEXP ll_init2(SEXP nr, SEXP nTips, SEXP nc, SEXP k)
{
int nrx = INTEGER(nr)[0], nTipsx = INTEGER(nTips)[0], ncx = INTEGER(nc)[0], kx = INTEGER(k)[0];
int i;
LL = (double *) calloc(nrx * ncx * kx * nTipsx, sizeof(double));
SCM = (int *) calloc(nrx * kx * nTipsx, sizeof(int)); // * 2L
for(i =0; i < (nrx * kx * nTipsx); i++) SCM[i] = 0L;
return R_NilValue;
}
/*
LL likelihood for internal edges
SCM scaling coefficients
*/
void matm(int *x, double *contrast, int *nr, int *nc, int *nco, double *result){
int i, j;
for(i = 0; i < (*nr); i++){
for(j = 0; j < (*nc); j++) result[i + j*(*nr)] *= contrast[x[i] - 1L + j*(*nco)];
}
}
SEXP invSites(SEXP dlist, SEXP nr, SEXP nc, SEXP contrast, SEXP nco){
R_len_t n = length(dlist);
int nrx=INTEGER(nr)[0], ncx=INTEGER(nc)[0], i, j;
SEXP result;
PROTECT(result = allocMatrix(REALSXP, nrx, ncx));
double *res;
res = REAL(result);
for(j=0; j < (nrx * ncx); j++) res[j] = 1.0;
for(i=0; i < n; i++) matm(INTEGER(VECTOR_ELT(dlist, i)), REAL(contrast), INTEGER(nr), INTEGER(nc), INTEGER(nco), res);
UNPROTECT(1); // result
return(result);
}
void scaleMatrix(double *X, int *nr, int *nc, int *result){
int i, j;
double tmp;
for(i = 0; i < *nr; i++) {
tmp = 0.0;
for(j = 0; j < *nc; j++) tmp += X[i + j* *nr];
while(tmp < ScaleEPS){
for(j = 0; j < *nc; j++) X[i + j* *nr] *=ScaleMAX;
result[i] +=1L;
tmp *= ScaleMAX;
}
}
}
// contrast to full dense matrix
void matp(int *x, double *contrast, double *P, int *nr, int *nc, int *nrs, double *result){
int i, j;
double *tmp;
tmp = (double *) R_alloc((*nc) *(*nrs), sizeof(double));
F77_CALL(dgemm)("N", "N", nrs, nc, nc, &one, contrast, nrs, P, nc, &zero, tmp, nrs FCONE FCONE);
for(i = 0; i < (*nr); i++){
for(j = 0; j < (*nc); j++) result[i + j*(*nr)] = tmp[x[i] - 1L + j*(*nrs)];
}
}
void rowMinScale(int *dat, int n, int k, int *res){
int i, h;
int tmp;
for(i = 0; i < n; i++){
tmp = dat[i];
for(h = 1; h< k; h++) {if(dat[i + h*n] < tmp) tmp=dat[i + h*n];}
if(tmp>0L){for(h = 0; h< k; h++) dat[i + h*n] -= tmp;}
res[i] = tmp;
}
}
void getP(double *eva, double *ev, double *evi, int m, double el, double w, double *result){
int i, j, h;
double res;
double *tmp;
tmp = (double *) R_alloc(m, sizeof(double));
for(i = 0; i < m; i++) tmp[i] = exp(eva[i] * w * el);
for(i = 0; i < m; i++){
for(j = 0; j < m; j++){
res = 0.0;
for(h = 0; h < m; h++) res += ev[i + h*m] * tmp[h] * evi[h + j*m];
result[i+j*m] = res;
}
}
}
SEXP getPM(SEXP eig, SEXP nc, SEXP el, SEXP w){
R_len_t i, j, nel, nw, k;
int m=INTEGER(nc)[0], l=0;
double *ws=REAL(w);
double *edgelen=REAL(el);
double *eva, *eve, *evei;
SEXP P, RESULT;
nel = length(el);
nw = length(w);
if(!isNewList(eig)) error("'eig' must be a list");
eva = REAL(VECTOR_ELT(eig, 0));
eve = REAL(VECTOR_ELT(eig, 1));
evei = REAL(VECTOR_ELT(eig, 2));
PROTECT(RESULT = allocVector(VECSXP, nel*nw));
for(j=0; j0)scaleMatrix(&ans[ni * rc], nr, nc, scaleTmp); // (ni-nTips)
ni = node[i];
if(ei < nTips)
matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, &ans[ni * rc]);
else
F77_CALL(dgemm)("N", "N", nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, &ans[ni * rc], nr FCONE FCONE);
}
else {
if(ei < nTips)
matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, rtmp);
else
F77_CALL(dgemm)("N", "N", nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, rtmp, nr FCONE FCONE);
for(j=0; j < rc; j++) ans[ni * rc + j] *= rtmp[j];
}
}
scaleMatrix(&ans[ni * rc], nr, nc, scaleTmp);
F77_CALL(dgemv)("N", nr, nc, &one, &ans[ni * rc], nr, bf, &ONE, &zero, TMP, &ONE FCONE);
}
// this seems to work perfectly
void lll3(SEXP dlist, double *eva, double *eve, double *evei, double *el, double g, int *nr, int *nc, int *node, int *edge,
int nTips, double *contrast, int nco, int n, int *scaleTmp, double *bf, double *TMP, double *ans, int *SC){
int ni, ei, j, i, rc; // R_len_t i, n = length(node);
double *rtmp, *P;
ni = -1L;
rc = *nr * *nc;
rtmp = (double *) R_alloc(*nr * *nc, sizeof(double));
P = (double *) R_alloc(*nc * *nc, sizeof(double));
for(j=0; j < *nr; j++) scaleTmp[j] = 0L;
for(i = 0; i < n; i++) {
getP(eva, eve, evei, *nc, el[i], g, P);
ei = edge[i];
if(ni != node[i]){
// test for node[i+1]
if(ni>0)scaleMatrix(&ans[ni * rc], nr, nc, &SC[ni * *nr]); // (ni-nTips)
ni = node[i];
for(j=0; j < *nr; j++) SC[j + ni * *nr] = 0L;
if(ei < nTips)
matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, &ans[ni * rc]);
else{
F77_CALL(dgemm)("N", "N", nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, &ans[ni * rc], nr FCONE FCONE);
for(j=0; j < *nr; j++) SC[ni * *nr + j] = SC[(ei-nTips) * *nr + j];
}
}
else {
if(ei < nTips)
matp(INTEGER(VECTOR_ELT(dlist, ei)), contrast, P, nr, nc, &nco, rtmp);
else{
F77_CALL(dgemm)("N", "N", nr, nc, nc, &one, &ans[(ei-nTips) * rc], nr, P, nc, &zero, rtmp, nr FCONE FCONE);
for(j=0; j < *nr; j++) SC[ni * *nr + j] += SC[(ei-nTips) * *nr + j];
}
for(j=0; j < rc; j++) ans[ni * rc + j] *= rtmp[j];
}
}
scaleMatrix(&ans[ni * rc], nr, nc, &SC[ni * *nr]);
for(j=0; j < *nr; j++) scaleTmp[j] = SC[ni * *nr + j];
F77_CALL(dgemv)("N", nr, nc, &one, &ans[ni * rc], nr, bf, &ONE, &zero, TMP, &ONE FCONE);
}
SEXP PML0(SEXP dlist, SEXP EL, SEXP G, SEXP NR, SEXP NC, SEXP K, SEXP eig, SEXP bf, SEXP node, SEXP edge, SEXP NTips, SEXP nco, SEXP contrast, SEXP N){
int nr=INTEGER(NR)[0], nc=INTEGER(NC)[0], k=INTEGER(K)[0], i, indLL;
int nTips = INTEGER(NTips)[0], *SC;
double *g=REAL(G), *tmp, logScaleEPS;
SEXP TMP;
double *eva, *eve, *evei;
eva = REAL(VECTOR_ELT(eig, 0));
eve = REAL(VECTOR_ELT(eig, 1));
evei = REAL(VECTOR_ELT(eig, 2));
SC = (int *) R_alloc(nr * k, sizeof(int));
PROTECT(TMP = allocMatrix(REALSXP, nr, k)); // changed
tmp=REAL(TMP);
for(i=0; i<(k*nr); i++)tmp[i]=0.0;
indLL = nr * nc * nTips;
for(i=0; intips){
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, el, g[i], P);
goDown(&LL[LINDEX(ch, i)], &LL[LINDEX(pa, i)], P, nr, nc, tmp);
}
}
else{
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, el, g[i], P);
goUp(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp);
}
}
}
// double *w,
void updateLL2(SEXP dlist, int pa, int ch, double *eva, double *eve, double*evei,
double el, double *g, int nr,
int nc, int ntips, double *contrast, int nco, int k,
double *tmp, double *P){
int i;
if(ch>ntips){
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, el, g[i], P);
goDown(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp);
}
}
else{
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, el, g[i], P);
goUp(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp);
}
}
}
void ExtractScale(int ch, int k, int *nr, int *ntips, double *res){
int i;
int j, blub, tmp;
for(i = 0; i < k; i++){
blub = LINDEX3(ch, i);
for(j=0; j< *nr; j++) res[j +i * *nr] = SCM[blub+j];
}
for(i = 0; i< *nr; i++){
tmp = res[i];
for(j = 1; j 1e-05) && (k < 10) ) {
if(scalep>0.6){
NR_df(eva, nc-1L, edle, w, g, X, ld, nr, f, tmp);
ll=0.0;
lll=0.0;
for(i=0; i 10.0) newedle = 10.0;
if (newedle < tau) newedle = tau; // 1e-8 phyML
for(i=0; i0L)SET_VECTOR_ELT(RESULT, 1, getM3(child, dad, P, nr, nc));
if(INTEGER(retB)[0]>0L)SET_VECTOR_ELT(RESULT, 2, getM3(dad, child, P, nr, nc));
// add variance ??
SET_VECTOR_ELT(RESULT, 3, ScalarReal(tmp[2]));
UNPROTECT(3);
return (RESULT);
}
// in dist.ml
SEXP FS5(SEXP eig, SEXP nc, SEXP el, SEXP w, SEXP g, SEXP X, SEXP ld, SEXP nr, SEXP weight, SEXP f0, SEXP tau)
{
SEXP RESULT; // EL, P;
double *wgt=REAL(weight), *eva=REAL(VECTOR_ELT(eig,0));
int ncx=INTEGER(nc)[0], nrx=INTEGER(nr)[0];
PROTECT(RESULT = allocVector(REALSXP, 3));
double edle = REAL(el)[0];
fs3(eva, ncx, edle, REAL(w), REAL(g), REAL(X), INTEGER(ld)[0], nrx, wgt, REAL(f0), REAL(tau)[0], REAL(RESULT));
UNPROTECT(1);
return (RESULT);
}
SEXP optE(SEXP PARENT, SEXP CHILD, SEXP ANC, SEXP eig, SEXP EVI, SEXP EL,
SEXP W, SEXP G, SEXP NR, SEXP NC, SEXP NTIPS, SEXP CONTRAST,
SEXP CONTRAST2, SEXP NCO,
SEXP dlist, SEXP WEIGHT, SEXP F0, SEXP TAU){
int i, k=length(W), h, j, n=length(PARENT), m, lEL=length(EL);
int nc=INTEGER(NC)[0], nr=INTEGER(NR)[0], ntips=INTEGER(NTIPS)[0];
int *parent=INTEGER(PARENT), *child=INTEGER(CHILD), *anc=INTEGER(ANC);
int loli, nco =INTEGER(NCO)[0];
double *weight=REAL(WEIGHT), *f0=REAL(F0), *w=REAL(W), tau=REAL(TAU)[0];
double *g=REAL(G), *evi=REAL(EVI), *contrast=REAL(CONTRAST), *contrast2=REAL(CONTRAST2);
double *el; //=REAL(EL);
double *eva, *eve, *evei, *tmp, *P;
double *X; // define it *blub=REAL(BLUB),
double *blub = (double *) R_alloc(nr * k, sizeof(double));
double oldel; //=el[ch-1L]
int ancloli, pa, ch; //=anc[loli]
double *res = (double *) R_alloc(3L, sizeof(double));
tmp = (double *) R_alloc(nr * nc, sizeof(double));
P = (double *) R_alloc(nc * nc, sizeof(double));
X = (double *) R_alloc(k * nr * nc, sizeof(double));
ExtractScale(parent[0], k, &nr, &ntips, blub);
SEXP RESULT;
PROTECT(RESULT = allocVector(REALSXP, lEL));
el=REAL(RESULT);
for(i = 0; i < lEL; i++) el[i] = REAL(EL)[i];
eva = REAL(VECTOR_ELT(eig, 0));
eve = REAL(VECTOR_ELT(eig, 1));
evei = REAL(VECTOR_ELT(eig, 2));
loli = parent[0];
for(m = 0; m < n; m++){
pa = parent[m];
ch = child[m];
oldel=el[ch-1L];
while(loli != pa){
ancloli=anc[loli];
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, el[loli-1L], g[i], P);
moveLL5(&LL[LINDEX(loli, i)], &LL[LINDEX(ancloli, i)], P, &nr, &nc, tmp);
}
loli = ancloli;
}
// moveDad
if(ch>ntips){
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, oldel, g[i], P);
helpDADI(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp);
helpPrep(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], eve, evi, nr, nc, tmp, &X[i*nr*nc]);
for(h = 0; h < nc; h++){
for(j = 0; j < nr; j++){
X[j+h*nr + i*nr*nc] *= blub[j+i*nr];
}
}
}
}
else{
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, oldel, g[i], P);
helpDAD5(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp);
helpPrep2(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast2, evi, nr, nc, nco, &X[i*nr*nc]); //;
for(h = 0; h < nc; h++){
for(j = 0; j < nr; j++){
X[j+h*nr + i*nr*nc] *= blub[j+i*nr];
}
}
}
}
fs3(eva, nc, oldel, w, g, X, k, nr, weight, f0, tau, res);
updateLL2(dlist, pa, ch, eva, eve, evei, res[0], g, nr,
nc, ntips, contrast, nco, k, tmp, P);
el[ch-1L] = res[0];
if (ch > ntips) loli = ch;
else loli = pa;
}
UNPROTECT(1); //RESULT
return(RESULT);
}
SEXP optQrtt(SEXP PARENT, SEXP CHILD, SEXP eig, SEXP EVI, SEXP EL,
SEXP W, SEXP G, SEXP NR, SEXP NC, SEXP NTIPS, SEXP CONTRAST,
SEXP CONTRAST2, SEXP NCO,
SEXP dlist, SEXP WEIGHT, SEXP F0, SEXP TAU){
int i, k=length(W), h, j, m, lEL=length(EL);
int nc=INTEGER(NC)[0], nr=INTEGER(NR)[0], ntips=INTEGER(NTIPS)[0];
int *parent=INTEGER(PARENT), *child=INTEGER(CHILD), pa, ch;
int nco =INTEGER(NCO)[0]; // loli,
double *weight=REAL(WEIGHT), *f0=REAL(F0), *w=REAL(W), tau=REAL(TAU)[0];
double *g=REAL(G), *evi=REAL(EVI), *contrast=REAL(CONTRAST), *contrast2=REAL(CONTRAST2);
double *el, *X;
double *eva, *eve, *evei, *tmp, *P;
double *blub = (double *) R_alloc(nr * k, sizeof(double));
double oldel;
double *res = (double *) R_alloc(3L, sizeof(double));
tmp = (double *) R_alloc(nr * nc, sizeof(double));
P = (double *) R_alloc(nc * nc, sizeof(double));
X = (double *) R_alloc(k * nr * nc, sizeof(double));
ExtractScale(parent[0], k, &nr, &ntips, blub);
SEXP RESULT;
PROTECT(RESULT = allocVector(REALSXP, lEL));
el=REAL(RESULT);
for(i = 0; i < lEL; i++) el[i] = REAL(EL)[i];
eva = REAL(VECTOR_ELT(eig, 0));
eve = REAL(VECTOR_ELT(eig, 1));
evei = REAL(VECTOR_ELT(eig, 2));
for(m = 4L; m > -1L; m--){
pa = parent[m];
ch = child[m];
oldel=el[m];
// moveDad
if(ch>ntips){
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, oldel, g[i], P);
helpDADI(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], P, nr, nc, tmp);
helpPrep(&LL[LINDEX(pa, i)], &LL[LINDEX(ch, i)], eve, evi, nr, nc, tmp, &X[i*nr*nc]);
for(h = 0; h < nc; h++){
for(j = 0; j < nr; j++){
X[j+h*nr + i*nr*nc] *= blub[j+i*nr];
}
}
}
}
else{
for(i = 0; i < k; i++){
getP(eva, eve, evei, nc, oldel, g[i], P);
helpDAD5(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast, P, nr, nc, nco, tmp);
helpPrep2(&LL[LINDEX(pa, i)], INTEGER(VECTOR_ELT(dlist, ch-1L)), contrast2, evi, nr, nc, nco, &X[i*nr*nc]); //;
for(h = 0; h < nc; h++){
for(j = 0; j < nr; j++){
X[j+h*nr + i*nr*nc] *= blub[j+i*nr];
}
}
}
}
fs3(eva, nc, oldel, w, g, X, k, nr, weight, f0, tau, res);
// go up
// if i=2 go down
if(m==2)updateLLQ(dlist, ch, pa, eva, eve, evei, res[0], g, nr,
nc, ntips, contrast, nco, k, tmp, P);
else updateLLQ(dlist, pa, ch, eva, eve, evei, res[0], g, nr,
nc, ntips, contrast, nco, k, tmp, P);
el[m] = res[0];
}
UNPROTECT(1); //RESULT
return(RESULT);
}
phangorn/src/sprdist.c 0000644 0001762 0000144 00000117050 14127044006 014513 0 ustar ligges users /*
* sprdist.c
*
* (c) 2016-2019 Leonardo de Oliveira Martins (leomrtns@gmail.com)
*
*
* This code may be distributed under the GNU GPL
*
*/
#include
#include
#include
#include
#include /* standard integer types (int32_t typedef etc.) [C99]*/
#define true 1U /*!< Boolean TRUE */
#define false 0U /*!< Boolean FALSE */
typedef unsigned char bool;
typedef struct splitset_struct* splitset;
typedef struct hungarian_struct* hungarian;
typedef struct bipartition_struct* bipartition;
typedef struct bipsize_struct* bipsize;
struct splitset_struct
{
int size, spsize, spr, spr_extra, rf, hdist; /*! \brief spr, extra prunes for spr, rf distances and hdist=assignment cost */
int n_g, n_s, n_agree, n_disagree;
bipartition *g_split, *s_split, *agree, *disagree;
bipartition prune;
hungarian h; /* hungarian method for solving the assignment between edges */
bool match; /*! \brief do we want to calculate the minimum cost assignment */
};
struct hungarian_struct
{
int **cost, *col_mate; /*! \brief cost matrix, and col_mate[row] with column match for row */
int size, /*! \brief assignment size. Cost is a square matrix, so size should be an overestimate where "missing" nodes are added w/ cost zero */
initial_cost, /*! \brief sum of lowest input cost values for each column. The hungarian method rescales them so that minimum per column is zero */
final_cost; /*! \brief our final cost is on rescaled cost matrix, therefore to restore the "classical" optimal cost one should sum it with initial_cost */
int *unchosen_row, *row_dec, *slack_row, *row_mate, *parent_row, *col_inc, *slack; /* aux vectors */
};
/*! \brief Bit-string representation of splits. */
struct bipartition_struct
{
unsigned long long *bs; /*! \brief Representation of a bipartition by a vector of integers (bitstrings). */
int n_ones; /*! \brief Counter (number of "one"s) */
bipsize n; /*! \brief number of bits (leaves), vector size and mask */
int ref_counter; /*! \brief How many times this struct is being referenced */
};
struct bipsize_struct
{
unsigned long long mask;/*! \brief mask to make sure we consider only active positions (of last bitstring) */
int ints, bits, original_size; /*! \brief Vector size and total number of elements (n_ints = n_bits/(8*sizeof(long long)) +1). */
int ref_counter; /*! \brief How many times this struct is being referenced */
};
/*! \brief Allocate space for splitset structure (two vectors of bipartitions), for simple comparisons */
splitset new_splitset (int nleaves, int nsplits);
/*! \brief free memory allocated for splitset structure */
void del_splitset (splitset split);
/*! \brief low level function that does the actual SPR and hdist calculation based on a filled splitset struct */
int dSPR_topology_lowlevel (splitset split);
/*! \brief function used by qsort for a vector of bipartitions (from smaller to larger) */
int compare_splitset_bipartition_increasing (const void *a1, const void *a2);
/* BELOW: low level functions that work with bipartitions */
void split_create_agreement_list (splitset split);
void split_remove_agree_edges (splitset split, bipartition *b, int *nb);
void split_remove_duplicates (bipartition *b, int *nb);
void split_compress_agreement (splitset split);
void split_create_disagreement_list (splitset split);
void split_disagreement_assign_match (splitset split);
void split_find_small_disagreement (splitset split);
void split_remove_small_disagreement (splitset split);
void split_minimize_subtrees (splitset split);
void split_remove_redundant_bit (splitset split, int id);
void split_replace_bit (splitset split, int to, int from);
void split_new_size (splitset split, int size, bool update_bipartitions);
void split_swap_position (bipartition *b, int i1, int i2);
/* BELOW: Hungarian method for bipartite matching (assignment) */
hungarian new_hungarian (int size);
void hungarian_reset (hungarian p);
void hungarian_update_cost (hungarian p, int row, int col, int cost);
void del_hungarian (hungarian p);
void hungarian_solve (hungarian p, int this_size);
/* BELOW: Memory-efficient, fast bipartition comparisions based on 64bit representation of splits */
bipartition new_bipartition (int size);
bipsize new_bipsize (int size);
bipartition new_bipartition_copy_from (const bipartition from);
bipartition new_bipartition_from_bipsize (bipsize n);
void del_bipartition (bipartition bip);
void del_bipsize (bipsize n);
void bipsize_resize (bipsize n, int nbits);
void bipartition_initialize (bipartition bip, int position);
void bipartition_zero (bipartition bip);
void bipartition_set (bipartition bip, int position);
void bipartition_set_lowlevel (bipartition bip, int i, int j);
void bipartition_unset (bipartition bip, int position);
void bipartition_unset_lowlevel (bipartition bip, int i, int j);
void bipartition_copy (bipartition to, const bipartition from);
void bipartition_OR (bipartition result, const bipartition b1, const bipartition b2, bool update_count);
void bipartition_AND (bipartition result, const bipartition b1, const bipartition b2, bool update_count);
void bipartition_ANDNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count);
void bipartition_XOR (bipartition result, const bipartition b1, const bipartition b2, bool update_count);
void bipartition_XORNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count);
void bipartition_NOT (bipartition result, const bipartition bip);
void bipartition_count_n_ones (const bipartition bip);
void bipartition_to_int_vector (const bipartition b, int *id, int vecsize);
bool bipartition_is_equal (const bipartition b1, const bipartition b2);
bool bipartition_is_equal_bothsides (const bipartition b1, const bipartition b2);
bool bipartition_is_larger (const bipartition b1, const bipartition b2);
void bipartition_flip_to_smaller_set (bipartition bip);
bool bipartition_is_bit_set (const bipartition bip, int position);
bool bipartition_contains_bits (const bipartition b1, const bipartition b2);
// void bipartition_print_to_stdout (const bipartition b1);
void bipartition_replace_bit_in_vector (bipartition *bvec, int n_b, int to, int from, bool reduce);
void bipartition_resize_vector (bipartition *bvec, int n_b);
/*! \brief Main SPR calculation function, to be used within R */
SEXP C_sprdist (SEXP bp1, SEXP bp2, SEXP lt) {
int i, j, n_leaves = INTEGER(lt)[0];
SEXP result;
double *res;
splitset split;
PROTECT(result = allocVector(REALSXP, 4));
res = REAL(result);
// if (length(bp1) != length(bp2)) error ("number of bipartitions given to C_sprdist are not the same");
split = new_splitset (n_leaves, length(bp1));
for (i=0; i < split->size; i++) {
// for (j=0; j < length(VECTOR_ELT (bp1, i)); j++) printf (";;%d ", INTEGER (VECTOR_ELT (bp1, i))[j]);
for (j=0; j < length(VECTOR_ELT (bp1, i)); j++) bipartition_set (split->g_split[i], INTEGER (VECTOR_ELT (bp1, i))[j] - 1);
for (j=0; j < length(VECTOR_ELT (bp2, i)); j++) bipartition_set (split->s_split[i], INTEGER (VECTOR_ELT (bp2, i))[j] - 1);
}
dSPR_topology_lowlevel (split);
res[0] = split->spr;
res[1] = split->spr_extra;
res[2] = split->rf;
res[3] = split->hdist;
del_splitset (split);
UNPROTECT(1); // result
return(result);
}
/* functions below should not be called outside this scope */
splitset
new_splitset (int nleaves, int nsplits)
{
splitset split;
int i;
split = (splitset) malloc (sizeof (struct splitset_struct));
split->n_g = split->n_s = split->size = nsplits;
split->n_agree = split->n_disagree = 0;
split->prune = NULL;
split->match = true; /* do we want to calculate the assignment matching cost (using hungarian() )? */
split->spr = split->spr_extra = split->rf = split->hdist = 0;
split->g_split = (bipartition*) malloc (split->size * sizeof (bipartition));
split->s_split = (bipartition*) malloc (split->size * sizeof (bipartition));
split->g_split[0] = new_bipartition (nleaves);
split->s_split[0] = new_bipartition (nleaves);
for (i = 1; i < split->size; i++) {
split->g_split[i] = new_bipartition_from_bipsize (split->g_split[0]->n); /* use same bipsize */
split->s_split[i] = new_bipartition_from_bipsize (split->s_split[0]->n);
}
split->agree = (bipartition*) malloc (split->size * sizeof (bipartition));
split->disagree = (bipartition*) malloc (split->size * split->size * sizeof (bipartition));
split->agree[0] = new_bipartition (nleaves); // this bipsize will be recycled below
split->disagree[0] = new_bipartition (nleaves);
for (i = 1; i < split->size; i++) split->agree[i] = new_bipartition_from_bipsize (split->agree[0]->n);
for (i = 1; i < split->size * split->size; i++) split->disagree[i] = new_bipartition_from_bipsize (split->disagree[0]->n);
split->prune = new_bipartition_from_bipsize (split->disagree[0]->n);
split->h = new_hungarian (split->size);
return split;
}
void
del_splitset (splitset split)
{
int i;
if (!split) return;
del_bipartition (split->prune);
if (split->disagree) {
for (i = split->size * split->size - 1; i >= 0; i--) del_bipartition (split->disagree[i]);
free (split->disagree);
}
if (split->agree) {
for (i = split->size - 1; i >= 0; i--) del_bipartition (split->agree[i]);
free (split->agree);
}
if (split->g_split) {
for (i = split->size - 1; i >= 0; i--) del_bipartition (split->g_split[i]);
free (split->g_split);
}
if (split->s_split) {
for (i = split->size - 1; i >= 0; i--) del_bipartition (split->s_split[i]);
free (split->s_split);
}
del_hungarian (split->h);
free (split);
}
int
compare_splitset_bipartition_increasing (const void *a1, const void *a2)
{ /* similar to bipartition_is_larger() */
bipartition *b1 = (bipartition *) a1;
bipartition *b2 = (bipartition *) a2;
int i;
if ((*b1)->n_ones > (*b2)->n_ones) return 1;
if ((*b1)->n_ones < (*b2)->n_ones) return -1;
for (i = (*b1)->n->ints - 1; (i >= 0) && ((*b1)->bs[i] == (*b2)->bs[i]); i--); /* find position of distinct bipartition elem*/
if (i < 0) return 0; /* identical bipartitions */
if ((*b1)->bs[i] > (*b2)->bs[i]) return 1;
else return -1;
}
int
dSPR_topology_lowlevel (splitset split)
{
int i = 0, mismatch = -1;
for (i=0; i < split->size; i++) {
bipartition_flip_to_smaller_set (split->g_split[i]);
bipartition_flip_to_smaller_set (split->s_split[i]);
}
qsort (split->g_split, split->size, sizeof (bipartition), compare_splitset_bipartition_increasing);
qsort (split->s_split, split->size, sizeof (bipartition), compare_splitset_bipartition_increasing);
//for (i = 0; i < split->n_g; i++) bipartition_print_to_stdout (split->g_split[i]); printf ("G ::DEBUG 0 ::\n");
//for (i = 0; i < split->n_s; i++) bipartition_print_to_stdout (split->s_split[i]); printf ("S\n");
i++; /* to trick -Werror, since we don't use it unless for debug */
while (mismatch) {
split_create_agreement_list (split); // vector of identical bipartitions
split_compress_agreement (split); // iterative replacement of cherry by new leaf
// for (i = 0; i < split->n_g; i++) bipartition_print_to_stdout (split->g_split[i]); printf ("G ::DEBUG::\n");
// for (i = 0; i < split->n_s; i++) bipartition_print_to_stdout (split->s_split[i]); printf ("S\n");
// for (i = 0; i < split->n_agree; i++) bipartition_print_to_stdout (split->agree[i]); printf ("A\n");
if (mismatch == -1) split->rf = split->n_g + split->n_s;
mismatch = (split->n_g > 0) && (split->n_s > 0); // all edges were in agreement
if (!mismatch) return split->spr;
split_create_disagreement_list (split); // vector of smallest disagreements
split_disagreement_assign_match (split); /* assignment matching between edges using hungarian method (split->hdist after first time) */
split_remove_duplicates (split->disagree, &(split->n_disagree)); // some elements are equal; this function also qsorts
split_find_small_disagreement (split); // could also be one leaf only
//for (i = 0; i < split->n_disagree; i++) { bipartition_print_to_stdout (split->disagree[i]); printf ("\n"); }
//printf ("{%d} prune: ", split->n_disagree); bipartition_print_to_stdout (split->prune); printf ("\n");
split->spr++;
split_remove_small_disagreement (split);
split_minimize_subtrees (split);
mismatch = (split->n_g > 0) && (split->n_s > 0); // all edges were in agreement
}
return split->spr;
}
void
split_create_agreement_list (splitset split)
{
int s, g;
for (g = 0; g < split->n_g; g++) for (s = 0; s < split->n_s; s++)
if (bipartition_is_equal (split->g_split[g], split->s_split[s])) {
bipartition_copy (split->agree[split->n_agree++], split->g_split[g]);
split->n_g--; split_swap_position (split->g_split, g, split->n_g); /* if we don't swap them, we lose ref to "old" value on g_split[] */
split->n_s--; split_swap_position (split->s_split, s, split->n_s);
g--; s = split->n_s; /* pretend loop finished, examine again with new values */
}
split_remove_agree_edges (split, split->g_split, &(split->n_g));
split_remove_agree_edges (split, split->s_split, &(split->n_s));
}
void
split_remove_agree_edges (splitset split, bipartition *b, int *nb)
{
int i, a;
for (i = 0; i < (*nb); i++) for (a = 0; a < split->n_agree; a++)
if (bipartition_is_equal (b[i], split->agree[a])) {
(*nb)--;
split_swap_position (b, i, (*nb));
i--;
a = split->n_agree; /* loop again over new value */
}
}
void
split_remove_duplicates (bipartition *b, int *nb)
{
int i, j;
bipartition pivot;
if ((*nb) < 2) return; /* only if we have a vector with > 1 element */
qsort (b, (*nb), sizeof (bipartition), compare_splitset_bipartition_increasing);
for (i = (*nb) - 1; i >= 1; i--)
if (bipartition_is_equal (b[i], b[i-1])) {
pivot = b[i]; /* do not lose a pointer to this element */
for (j = i; j < (*nb)-1; j++) b[j] = b[j+1];
b[j] = pivot; /* j = (*nb) - 1, which will become obsolete through next line --> (*nb)-- */
(*nb)--;
}
}
void
split_compress_agreement (splitset split)
{
int i, j, pair[2];
for (i = 0; i < split->n_agree; i++) if (split->agree[i]->n_ones == 2) { /* cherry in common, can be represented by just one leaf */
bipartition_to_int_vector (split->agree[i], pair, 2);
split_remove_redundant_bit (split, pair[1]);
split_new_size (split,split->agree[0]->n->bits - 1, false); /* false = do not recalculate every bipartition's last elem */
bipartition_resize_vector (split->agree, split->n_agree);
for (j = 0; j < split->n_agree; j++) { /* minimize subtree size and remove single leaves */
bipartition_flip_to_smaller_set (split->agree[j]); /* agree only */
if (split->agree[j]->n_ones < 2) split_swap_position (split->agree, j--, --split->n_agree);
}
i = -1; /* redo all iterations, with new info (agree[] will be smaller) */
}
bipartition_resize_vector (split->g_split, split->n_g);
bipartition_resize_vector (split->s_split, split->n_s);
}
void
split_create_disagreement_list (splitset split)
{
int g, s;
for (g = 0; g < split->n_g; g++) for (s = 0; s < split->n_s; s++) {
bipartition_XOR (split->disagree[g * split->n_s + s], split->g_split[g], split->s_split[s], true); /* true means to calculate n_ones */
bipartition_flip_to_smaller_set (split->disagree[g * split->n_s + s]);
}
split->n_disagree = split->n_g * split->n_s;
}
void
split_disagreement_assign_match (splitset split)
{ /* also calculates split->hdist */
int g, s, max_n, sum = 0;
if (split->n_g > split->n_s) max_n = split->n_g;
else max_n = split->n_s;
if (max_n < 2) return;
hungarian_reset (split->h);
for (g = 0; g < split->n_g; g++) for (s = 0; s < split->n_s; s++)
hungarian_update_cost (split->h, g, s, split->disagree[g * split->n_s + s]->n_ones);
hungarian_solve (split->h, max_n);
/* now split->h->col_mate will have the pairs */
/* if we do the matching below it becomes much faster, but we may miss the best prune subtrees in a few cases (do not compromise the algo) */
split->n_disagree = 0;
for (g = 0; g < max_n; g++) if ((g < split->n_g) && ( split->h->col_mate[g] < split->n_s)) { /* some matchings might be to dummy edges */
bipartition_XOR (split->disagree[split->n_disagree], split->g_split[g], split->s_split[split->h->col_mate[g]], true); /* true means to calculate n_ones */
bipartition_flip_to_smaller_set (split->disagree[split->n_disagree++]);
sum += split->disagree[split->n_disagree-1]->n_ones;
}
if (split->match) { split->hdist = split->h->final_cost+split->h->initial_cost; split->match = false; }
}
void
split_find_small_disagreement (splitset split)
{
bipartition dis;
int a, d;
bipartition_copy (split->prune, split->disagree[0]); /* smallest, in case we don't find a better one in loop below */
if (split->prune->n_ones < 2) return;
dis = new_bipartition_from_bipsize (split->disagree[0]->n);
for (d = 0; d < split->n_disagree; d++) for (a = 0; a < split->n_agree; a++) {
if ((split->disagree[d]->n_ones == split->agree[a]->n_ones) ||
(split->disagree[d]->n_ones == (split->agree[a]->n->bits - split->agree[a]->n_ones))) {
bipartition_XOR (dis, split->disagree[d], split->agree[a], true);
if (!dis->n_ones) { bipartition_copy (split->prune, split->disagree[d]); d = split->n_disagree; a = split->n_agree; }
else if (dis->n_ones == dis->n->bits) { bipartition_NOT (split->prune, split->disagree[d]); d = split->n_disagree; a = split->n_agree; }
}
}
/* check if prune nodes are all on same side of a tree or if they are actually two SPRs (one from each tree) */
for (d = 0; d < split->n_g; d++) {
if (!bipartition_contains_bits (split->g_split[d], split->prune)) {
bipartition_NOT (dis, split->g_split[d]);
if (!bipartition_contains_bits (dis, split->prune)) { split->spr_extra++; d = split->n_g; }
}
}
del_bipartition (dis);
}
void
split_remove_small_disagreement (splitset split)
{
int *index, i, j = split->prune->n_ones - 1, k = 0, size = split->agree[0]->n->bits;
index = (int*) malloc (split->prune->n_ones * sizeof (int));
bipartition_to_int_vector (split->prune, index, split->prune->n_ones);
for (i = size - 1; i >= (size - split->prune->n_ones); i--) {
if (index[k] >= (size - split->prune->n_ones)) i = -1;
else {
if (i == index[j]) j--;
else split_replace_bit (split, index[k++], i);
}
}
split_new_size (split,size - split->prune->n_ones, true);
if (index) free (index);
}
void
split_minimize_subtrees (splitset split)
{
int i;
for (i = 0; i < split->n_s; i++) {
bipartition_flip_to_smaller_set (split->s_split[i]);
if (split->s_split[i]->n_ones < 2) { split->n_s--; split_swap_position (split->s_split, i, split->n_s); i--; }
}
for (i = 0; i < split->n_g; i++) {
bipartition_flip_to_smaller_set (split->g_split[i]);
if (split->g_split[i]->n_ones < 2) { split->n_g--; split_swap_position (split->g_split, i, split->n_g); i--; }
}
for (i = 0; i < split->n_agree; i++) {
bipartition_flip_to_smaller_set (split->agree[i]);
if (split->agree[i]->n_ones < 2) { split->n_agree--; split_swap_position (split->agree, i, split->n_agree); i--; }
}
}
void
split_remove_redundant_bit (splitset split, int id)
{
int last = split->agree[0]->n->bits-1;
if (id < last) split_replace_bit (split, id, last);
}
void
split_replace_bit (splitset split, int to, int from)
{
if (from <= to) return;
/*not needed for disagree[] */
bipartition_replace_bit_in_vector (split->agree, split->n_agree, to, from, true);
bipartition_replace_bit_in_vector (split->g_split, split->n_g, to, from, true);
bipartition_replace_bit_in_vector (split->s_split, split->n_s, to, from, true);
}
void
split_new_size (splitset split, int size, bool update_bipartitions)
{
bipsize_resize (split->g_split[0]->n, size);
bipsize_resize (split->s_split[0]->n, size);
bipsize_resize (split->agree[0]->n, size);
bipsize_resize (split->disagree[0]->n, size);
if (update_bipartitions) {
bipartition_resize_vector (split->g_split, split->n_g);
bipartition_resize_vector (split->s_split, split->n_s);
bipartition_resize_vector (split->agree, split->n_agree);
}
}
void
split_swap_position (bipartition *b, int i1, int i2)
{
bipartition pivot = b[i1];
b[i1] = b[i2];
b[i2] = pivot;
}
/* The hungarian method below is copied from http://www.informatik.uni-freiburg.de/~stachnis/misc.html
* The (edited) original message follows:
*
** libhungarian by Cyrill Stachniss, 2004 Solving the Minimum Assignment Problem using the
** Hungarian Method. ** This file may be freely copied and distributed! **
**
** Parts of the used code was originally provided by the "Stanford GraphGase", but I made changes to this code.
** As asked by the copyright node of the "Stanford GraphGase", I hereby proclaim that this file are *NOT* part of the
** "Stanford GraphGase" distrubition! */
void
hungarian_reset (hungarian p)
{
int i, j;
for (i = 0; i < p->size; i++) {
p->col_mate[i] = p->unchosen_row[i] = p->row_dec[i] = p->slack_row[i] = p->row_mate[i] = p->parent_row[i] = p->col_inc[i] = p->slack[i] = 0;
for (j = 0; j < p->size; j++) p->cost[i][j] = 0;
}
p->final_cost = 0;
}
hungarian
new_hungarian (int size)
{
int i;
hungarian p;
p = (hungarian) malloc (sizeof (struct hungarian_struct));
p->size = size; /* n_rows = n_columns; if it's not, fill with zeroes (no cost) */
p->cost = (int**) malloc (size * sizeof (int*));
for (i = 0; i < p->size; i++)
p->cost[i] = (int*) malloc (size * sizeof (int));
/* edges would be assignment_matrix[ i * ncols + col_mate[i] ] = true; and other elems "false" (but we don't use the matrix notation) */
p->col_mate = (int*) malloc (size * sizeof (int)); /* for a given row node, col_mate[row] is the assigned col node */
p->unchosen_row = (int*) malloc (size * sizeof (int));
p->row_dec = (int*) malloc (size * sizeof (int));
p->slack_row = (int*) malloc (size * sizeof (int));
p->row_mate = (int*) malloc (size * sizeof (int));
p->parent_row = (int*) malloc (size * sizeof (int));
p->col_inc = (int*) malloc (size * sizeof (int));
p->slack = (int*) malloc (size * sizeof (int));
hungarian_reset (p);
return p;
}
void
hungarian_update_cost (hungarian p, int row, int col, int cost)
{
if (row >= p->size) return;
if (col >= p->size) return;
p->cost[row][col] = cost;
}
void
del_hungarian (hungarian p)
{
int i;
if (!p) return;
if (p->cost) {
for (i = p->size - 1; i >= 0; i--) if (p->cost[i]) free (p->cost[i]);
free (p->cost);
}
free (p->col_mate); /* this is the important one, with i assigned to col_mate[i] */
free (p->slack);
free (p->col_inc);
free (p->parent_row);
free (p->row_mate);
free (p->slack_row);
free (p->row_dec);
free (p->unchosen_row);
free (p);
}
void
hungarian_solve (hungarian p, int this_size)
{
int i, j, nrows = this_size, ncols = this_size, k, l, s, t, q, unmatched;
p->final_cost = p->initial_cost = 0;
if (this_size > p->size) { p->final_cost = -1; return; } /* we don't call biomcmc_error(), but it *is* an error! */
for (l = 0; l < ncols; l++) { // Begin subtract column minima in order to start with lots of zeroes 12
s = p->cost[0][l];
for (k = 1; k < nrows; k++) if (p->cost[k][l] < s) s = p->cost[k][l];
p->initial_cost += s; /* this should be added to final_cost to have classical assignment cost; here we distinguish them */
if (s!=0) for (k = 0; k < nrows; k++) p->cost[k][l] -= s;
} // End subtract column minima in order to start with lots of zeroes 12
// Begin initial state 16
t=0;
for (l = 0; l < ncols; l++) { // n => num_cols
p->row_mate[l]= -1;
p->parent_row[l]= -1;
p->col_inc[l]=0;
p->slack[l]= 0x7FFFFFFF;
}
for (k = 0; k < nrows; k++) { // m => num_rows
s = p->cost[k][0];
for (l = 1; l < ncols; l++) if (p->cost[k][l] < s) s = p->cost[k][l];
p->row_dec[k]=s;
for (l = 0; l < ncols; l++) if ((s==p->cost[k][l]) && (p->row_mate[l] < 0)) {
p->col_mate[k] = l;
p->row_mate[l] = k; // fprintf(stderr, "matching col %d==row %d\n",l,k);
goto row_done;
}
p->col_mate[k] = -1; // fprintf(stderr, "node %d: unmatched row %d\n",t,k);
p->unchosen_row[t++] = k;
row_done:
;
}
// End initial state 16
// Begin Hungarian algorithm 18
if (t==0) goto done;
unmatched=t;
while (1) {
q=0; // fprintf(stderr, "Matched %d rows.\n",m-t);
while (1) {
while (qunchosen_row[q];
s=p->row_dec[k];
for (l=0;lslack[l]) {
int del;
del = p->cost[k][l] - s + p->col_inc[l];
if (del < p->slack[l]) {
if (del==0) {
if (p->row_mate[l]<0) goto breakthru;
p->slack[l]=0;
p->parent_row[l]=k; // fprintf(stderr, "node %d: row %d==col %d--row %d\n", t,row_mate[l],l,k);
p->unchosen_row[t++]=p->row_mate[l];
}
else { p->slack[l]=del; p->slack_row[l]=k; }
}
}
} // End explore node q of the forest 19
q++;
}
// Begin introduce a new zero into the matrix 21
s = 0x7FFFFFFF;
for (l = 0;l < ncols; l++) if (p->slack[l] && p->slack[l] < s) s = p->slack[l];
for (q = 0; q < t; q++) p->row_dec[ p->unchosen_row[q] ] += s;
for (l = 0; l < ncols; l++) if (p->slack[l]) {
p->slack[l]-=s;
if (p->slack[l]==0) { // Begin look at a new zero 22
k = p->slack_row[l]; // fprintf(stderr, "Decreasing uncovered elements by %d produces zero at [%d,%d]\n", s,k,l);
if (p->row_mate[l]<0) {
for (j=l+1;jslack[j]==0) p->col_inc[j]+=s;
goto breakthru;
}
else {
p->parent_row[l]=k; // fprintf(stderr, "node %d: row %d==col %d--row %d\n",t,row_mate[l],l,k);
p->unchosen_row[t++]=p->row_mate[l];
}
} // End look at a new zero 22
}
else p->col_inc[l]+=s;
// End introduce a new zero into the matrix 21
}
breakthru:
// fprintf(stderr, "Breakthrough at node %d of %d!\n",q,t);
while (1) { // Begin update the matching 20
j=p->col_mate[k];
p->col_mate[k]=l;
p->row_mate[l]=k; // fprintf(stderr, "rematching col %d==row %d\n",l,k);
if (j<0) break;
k=p->parent_row[j];
l=j;
} // End update the matching 20
if (--unmatched==0) goto done;
// Begin get ready for another stage 17
t=0;
for (l=0;lparent_row[l]= -1;
p->slack[l]=0x7FFFFFFF;
}
for (k=0;kcol_mate[k]<0) p->unchosen_row[t++]=k; // fprintf(stderr, "node %d: unmatched row %d\n",t,k);
// End get ready for another stage 17
}
done:
// Begin doublecheck the solution 23
for (k = 0; k < nrows; k++) for (l=0;lcost[k][l] < p->row_dec[k] - p->col_inc[l]) { p->final_cost = -1; return;} //printf ("\n**\n");
for (k = 0; k < nrows; k++) {
l=p->col_mate[k];
if ((l < 0) || (p->cost[k][l] != p->row_dec[k] - p->col_inc[l])) { p->final_cost = -1; return; }
}
k=0;
for (l=0;lcol_inc[l]) k++;
if (k>nrows) { p->final_cost = -1; return; }
// End doublecheck the solution 23
// End Hungarian algorithm 18
for (k = 0; k < nrows; ++k) for (l = 0; l < ncols; ++l) p->cost[k][l] = p->cost[k][l] - p->row_dec[k] + p->col_inc[l];
for (i = 0; i < nrows; i++) p->final_cost += p->row_dec[i];
for (i = 0; i < ncols; i++) p->final_cost -= p->col_inc[i]; // fprintf(stderr, "Cost is %d\n",cost);
}
/* BELOW are the bipartition functions (memory-efficient storage of bipartitions on 64 bits */
int BitStringSize = 8 * sizeof (unsigned long long);
bipartition
new_bipartition (int size)
{
bipartition bip;
int i;
bip = (bipartition) malloc (sizeof (struct bipartition_struct));
bip->n = new_bipsize (size);
bip->n_ones = 0;
bip->ref_counter = 1;
bip->bs = (unsigned long long*) malloc (bip->n->ints * sizeof (unsigned long long));
for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0ULL;
return bip;
}
bipsize
new_bipsize (int size)
{
bipsize n;
int i;
n = (bipsize) malloc (sizeof (struct bipsize_struct));
n->bits = n->original_size = size;
n->ref_counter = 1;
n->ints = size/BitStringSize + 1;
n->mask = 0ULL;
for (i=0; i < n->bits%BitStringSize; i++) n->mask |= (1ULL << i); /* disregard other bits */
return n;
}
bipartition
new_bipartition_copy_from (const bipartition from)
{
bipartition bip;
int i;
bip = (bipartition) malloc (sizeof (struct bipartition_struct));
bip->n = new_bipsize (from->n->bits);
bip->n_ones = from->n_ones;
bip->ref_counter = 1;
bip->bs = (unsigned long long*) malloc (bip->n->ints * sizeof (unsigned long long));
for (i=0; i < bip->n->ints; i++) bip->bs[i] = from->bs[i];
return bip;
}
bipartition
new_bipartition_from_bipsize (bipsize n)
{
bipartition bip;
int i;
bip = (bipartition) malloc (sizeof (struct bipartition_struct));
bip->n = n;
bip->n->ref_counter++;
bip->n_ones = 0;
bip->ref_counter = 1;
bip->bs = (unsigned long long*) malloc (bip->n->ints * sizeof (unsigned long long));
for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0ULL;
return bip;
}
void
del_bipartition (bipartition bip)
{
if (bip) {
if (--bip->ref_counter) return;
if (bip->bs) free (bip->bs);
del_bipsize (bip->n);
free (bip);
}
}
void
del_bipsize (bipsize n)
{
if (n) {
if (--n->ref_counter) return;
free (n);
}
}
void
bipsize_resize (bipsize n, int nbits)
{
int i;
n->bits = nbits;
n->ints = nbits/BitStringSize + 1; // might be smaller than original bs size
n->mask = 0ULL;
for (i=0; i < nbits%BitStringSize; i++) n->mask |= (1ULL << i); /* disregard other bits */
}
void
bipartition_initialize (bipartition bip, int position)
{
int i, j;
for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0ULL;
j = position%BitStringSize;
i = position/BitStringSize;
bip->bs[i] = (1ULL << j);
bip->n_ones = 1;
}
void
bipartition_zero (bipartition bip)
{
int i;
for (i=0; i < bip->n->ints; i++) bip->bs[i] = 0ULL;
bip->n_ones = 0;
}
void
bipartition_set (bipartition bip, int position)
{
bipartition_set_lowlevel (bip, position/BitStringSize, position%BitStringSize);
}
void
bipartition_set_lowlevel (bipartition bip, int i, int j)
{
if (bip->bs[i] & (1ULL << j)) return; // bit already set
bip->bs[i] |= (1ULL << j);
bip->n_ones++; /* doesn't work if we reduce space later (check replace_int_in_vector() ) */
}
void
bipartition_unset (bipartition bip, int position)
{
bipartition_unset_lowlevel (bip, position/BitStringSize, position%BitStringSize);
}
void
bipartition_unset_lowlevel (bipartition bip, int i, int j)
{
if (!(bip->bs[i] & (1ULL << j))) return; // bit already unset
bip->bs[i] &= ~(1ULL << j);
bip->n_ones--;
}
void
bipartition_copy (bipartition to, const bipartition from)
{
int i;
for (i=0; i < to->n->ints; i++) to->bs[i] = from->bs[i];
to->n_ones = from->n_ones;
}
void
bipartition_OR (bipartition result, const bipartition b1, const bipartition b2, bool update_count)
{
int i;
for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] | b2->bs[i];
result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */
if (update_count) bipartition_count_n_ones (result);
else result->n_ones = b1->n_ones + b2->n_ones; // works on topologies where b1 and b2 are disjoint
}
void
bipartition_AND (bipartition result, const bipartition b1, const bipartition b2, bool update_count)
{
int i;
for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] & b2->bs[i];
result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */
if (update_count) bipartition_count_n_ones (result);
else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var)
}
void
bipartition_ANDNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count)
{
int i;
for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] & (~b2->bs[i]);
result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */
if (update_count) bipartition_count_n_ones (result);
else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var)
}
void
bipartition_XOR (bipartition result, const bipartition b1, const bipartition b2, bool update_count)
{
int i;
for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] ^ b2->bs[i];
result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */
if (update_count) bipartition_count_n_ones (result);
else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var)
}
void
bipartition_XORNOT (bipartition result, const bipartition b1, const bipartition b2, bool update_count)
{ /* equivalent to XOR followed by NOT */
int i;
for (i=0; i < result->n->ints; i++) result->bs[i] = b1->bs[i] ^ (~b2->bs[i]);
result->bs[i-1] &= b1->n->mask; /* do not change last bits (do not belong to bipartition) */
if (update_count) bipartition_count_n_ones (result);
else result->n_ones = 0;// update_count = false should be used only when you don't care about this value (temp var)
}
void
bipartition_NOT (bipartition result, const bipartition bip)
{
int i;
for (i=0; i < result->n->ints; i++) result->bs[i] = ~bip->bs[i];
result->bs[i-1] &= bip->n->mask; /* do not invert last bits (do not belong to bipartition) */
result->n_ones = bip->n->bits - bip->n_ones;
}
void
bipartition_count_n_ones (const bipartition bip)
{
int i;
unsigned long long j;
bip->n_ones = 0;
/* // Naive approach
for (i=0; i < bip->n_ints - 1; i++) for (j=0; j < BitStringSize; j++) bip->n_ones += ((bip->bs[i] >> j) & 1ULL);
for (j=0; j < bip->n_bits%BitStringSize; j++) bip->n_ones += ((bip->bs[i] >> j) & 1ULL);
*/
// clear the least significant bit set per iteration (Peter Wegner in CACM 3 (1960), 322, mentioned in K&R)
for (i=0; i < bip->n->ints; i++) for (j = bip->bs[i]; j; bip->n_ones++) j &= j - 1ULL;
}
bool
bipartition_is_equal (const bipartition b1, const bipartition b2)
{
int i;
if (b1->n_ones != b2->n_ones) return false;
if (b1->n->ints != b2->n->ints) return false;
for (i=0; i < b1->n->ints - 1; i++) if (b1->bs[i] != b2->bs[i]) return false;
b1->bs[i] &= b1->n->mask; b2->bs[i] &= b2->n->mask; /* apply mask before comparing last elems */
if (b1->bs[i] != b2->bs[i]) return false;
return true;
}
bool
bipartition_is_equal_bothsides (const bipartition b1, const bipartition b2)
{
int i;
bool equal = true;
for (i=0; (i < b1->n->ints - 1) && (equal); i++) if (b1->bs[i] != b2->bs[i]) equal = false;
if ((equal) && ((b1->bs[i] & b1->n->mask) != (b2->bs[i] & b2->n->mask))) equal = false;
if (equal) return true; /* the biparitions are already the same, without flipping the bits */
/* now we compare one bipartition with the complement of the other */
for (i=0; (i < b1->n->ints - 1); i++) if (b1->bs[i] != ~b2->bs[i]) return false;
if ((b1->bs[i] & b1->n->mask) != ((~b2->bs[i]) & b2->n->mask)) return false;
return true; /* they are the exact complement of one another */
}
bool
bipartition_is_larger (const bipartition b1, const bipartition b2)
{
int i;
if (b1->n_ones > b2->n_ones) return true;
if (b1->n_ones < b2->n_ones) return false;
for (i = b1->n->ints - 1; (i >= 0) && (b1->bs[i] == b2->bs[i]); i--); /* find position of distinct bipartition elem*/
if (i < 0) return false; /* identical bipartitions */
if (b1->bs[i] > b2->bs[i]) return true;
else return false;
}
void
bipartition_flip_to_smaller_set (bipartition bip)
{
int i = bip->n->ints - 1; /* most significant position -- consistent with is_larger() above, using OLD algo below */
if ((2 * bip->n_ones) < bip->n->bits) return; /* it is already the smaller set */
/* OLD always x is different from ~x, so we just look at last element ("largest digits of number") */
// if (((2 * bip->n_ones) == bip->n->bits) && (bip->bs[i] < (bip->n->mask & ~bip->bs[i]))) return;
/* NEW: resolve ties by always showing the same "side" of bipartition, that is, the one having an arbitrary leaf (first one, in our case) */
if (((2 * bip->n_ones) == bip->n->bits) && (bip->bs[0] & 1ULL)) return;
for (i=0; i < bip->n->ints; i++) bip->bs[i] = ~bip->bs[i]; /* like bipartition_NOT() */
bip->bs[i-1] &= bip->n->mask; /* do not invert last bits (do not belong to bipartition) */
bip->n_ones = bip->n->bits - bip->n_ones;
return;
}
bool
bipartition_is_bit_set (const bipartition bip, int position)
{
if (bip->bs[(int)(position/BitStringSize)] & (1ULL << (int)(position%BitStringSize))) return true;
return false;
}
bool
bipartition_contains_bits (const bipartition b1, const bipartition b2)
{ /* generalization of bipartition_is_bit_set(); b1 contains or not b2 */
int i;
if (b1->n_ones < b2->n_ones) return false;
for (i=0; i < b1->n->ints; i++) if ((b2->bs[i]) && (b2->bs[i] != (b1->bs[i] & b2->bs[i]))) return false;
return true;
}
void
bipartition_to_int_vector (const bipartition b, int *id, int vecsize)
{
int i, j, k = 0;
for (i=0; i < b->n->ints; i++) for (j=0; (j < BitStringSize) && (k < vecsize); j++) if ( ((b->bs[i] >> j) & 1ULL) ) id[k++] = i * BitStringSize + j;
}
/*
void
bipartition_print_to_stdout (const bipartition b1)
{
int i, j;
for (i = 0; i < b1->n->ints - 1; i++) {
for (j = 0; j < BitStringSize; j++) printf ("%d", (int)((b1->bs[i] >> j) & 1ULL));
printf (".");
}
for (j = 0; j < b1->n->bits%BitStringSize; j++) printf ("%d", (int)((b1->bs[i] >> j) & 1ULL));
printf ("[%d] ", b1->n_ones);
}
*/
void
bipartition_replace_bit_in_vector (bipartition *bvec, int n_b, int to, int from, bool reduce)
{ /* copy info from position "from" to position "to" */
int k, j = from%BitStringSize, i = from/BitStringSize, j2 = to%BitStringSize, i2 = to/BitStringSize;
/* boolean "reduce" means that bitstring space will be reduced (last bits will be removed), therefore the update of
* n_ones is different from default bipartition_set() behaviour: it's not an extra "1" (that is, one that did not
* contribute to n_ones), but an existing "1" that change places. Schematically:
* from -> to | normal n_ones count | when bitstring is reduced afterwards
* 0 -> 0 | 0 | 0
* 0 -> 1 | -1 | -1
* 1 -> 0 | +1 | 0 (since it's a leaf that belonged to position "from" and now is on position "to")
* 1 -> 1 | 0 | -1 (in fact one of the two "1"s dissapeared after reducing the bitstring
* (the above description is outdated since I rewrote by hand the bit functions -- observe how we must erase 1 values from "from") */
if (reduce) for (k = 0; k < n_b; k++) { // copy 0 or 1 values, erasing "from" values to avoid problems after reducing space (hanging 1s out of range)
if ( ((bvec[k]->bs[i] >> j) & 1ULL) && ((bvec[k]->bs[i2] >> j2) & 1ULL) ) { bvec[k]->n_ones--; bvec[k]->bs[i] &= ~(1ULL << j); }
else if ( ((bvec[k]->bs[i] >> j) & 1ULL) && !((bvec[k]->bs[i2] >> j2) & 1ULL) ) { bvec[k]->bs[i2] |= (1ULL << j2); bvec[k]->bs[i] &= ~(1ULL << j); }
else if ( !((bvec[k]->bs[i] >> j) & 1ULL) && ((bvec[k]->bs[i2] >> j2) & 1ULL) ) { bvec[k]->bs[i2] &= ~(1ULL << j2); bvec[k]->n_ones--; }
/* else do nothing (from zero to zero) */
}
else for (k = 0; k < n_b; k++) { // copy 0 or 1 values
if ( ((bvec[k]->bs[i] >> j) & 1ULL) ) bipartition_set_lowlevel (bvec[k], i2, j2); // will check if n_ones change or not
else bipartition_unset_lowlevel (bvec[k], i2, j2);
}
}
void
bipartition_resize_vector (bipartition *bvec, int n_b)
{
int k, i = bvec[0]->n->ints - 1;
for (k = 0; k < n_b; k++) { bvec[k]->bs[i] &= bvec[0]->n->mask; bipartition_count_n_ones (bvec[k]); }
}
phangorn/src/Fitch.h 0000644 0001762 0000144 00000004244 14140457727 014102 0 ustar ligges users #include
#ifndef _FITCH_H_
#define _FITCH_H_
#ifndef __has_builtin
#define __has_builtin(x) 0
#endif
#ifdef __GNUC__
#define GNUC_PREREQ(x, y) \
(__GNUC__ > x || (__GNUC__ == x && __GNUC_MINOR__ >= y))
#else
#define GNUC_PREREQ(x, y) 0
#endif
#if GNUC_PREREQ(4, 2) || \
__has_builtin(__builtin_popcount)
#define HAVE_BUILTIN_POPCOUNT
#endif
using namespace Rcpp;
// Counting bits set, Brian Kernighan's way
// from Bit Twiddling Hacks by Sean Eron Anderson seander@cs.stanford.edu
// count the number of bits set in v
// fast if few bits set, what we assume
static inline int bitCount64(uint64_t v){
int c;
for (c = 0; v; c++){
v &= v - 1; // clear the least significant bit set
}
return c;
}
#if defined(HAVE_BUILTIN_POPCOUNT)
static inline int popcnt64(uint64_t x)
{
return __builtin_popcountll(x);
}
#else
static inline int popcnt64(uint64_t x)
{
return bitCount64(x);
}
#endif
#define BIT_SIZE 64
std::vector< std::vector > readFitch(const List &xlist,
IntegerMatrix contr, int nSeq, int nChar, int nStates,
int nBits, int m);
// IntegerMatrix preorder(const IntegerMatrix & edge, int nTips);
class Fitch {
public:
// Fitch(Robject)
Fitch (RObject obj, int w1, int m) {
weight = obj.attr("weight");
nChar = (int) obj.attr("nr");
if( (nChar % BIT_SIZE) ){
for(int i= (nChar % BIT_SIZE); i > X;
IntegerVector pscore_nodes;
NumericVector weight; // Integer??
int nChar;
int nSeq;
int nStates;
int nBits;
int wBits;
int m;
int p0;
};
#endif // _FITCH_H_
phangorn/src/fitch64.cpp 0000644 0001762 0000144 00000066425 14140460000 014632 0 ustar ligges users #include
#include "Fitch.h"
#include "phangorn_utils.h"
//#include
// parallel for
// simd
//using namespace Rcpp;
//Enable C++11, as we may want to have unsigned long long (uint64_t)
// [[Rcpp::plugins(cpp11)]]
/*
// Examples from Dirk to find minimum dist
double vecmin(NumericVector x) {
// Rcpp supports STL-style iterators
NumericVector::iterator it = std::min_element(x.begin(), x.end());
// we want the value so dereference
return *it;
}
int vecminInd(NumericVector x) {
// Rcpp supports STL-style iterators
NumericVector::iterator it = std::min_element(x.begin(), x.end());
// we want the position (+1 in R)
return it - x.begin();
}
*/
std::vector< std::vector > readFitch(const List &xlist, IntegerMatrix contr,
int nSeq, int nChar, int nStates, int nBits, int m){
int current_bit=0;
std::vector< std::vector > out(m * nSeq);
std::vector tmp; // tmp(nStates);
for (int k = 0; k < nStates; ++k) tmp.push_back(0ull);
for(int i=0; i
phangorn is a package for phylogenetic reconstruction and analysis in the R language. phangorn offers the possibility of reconstructing phylogenies with distance based methods, maximum parsimony or maximum likelihood (ML) and performing Hadamard conjugation. Extending the general ML framework, this package provides the possibility of estimating mixture and partition models. Furthermore, phangorn offers several functions for comparing trees, phylogenetic models or splits, simulating character data and performing congruence analyses.
You can install
- the latest released version `install.packages("phangorn")`
- the latest development version `remotes::install_github("KlausVigo/phangorn")`
To install the development version you may need to install the Biostrings and seqLogo package from bioconductor first:
```
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("Biostrings", "seqLogo"))
```
Also the development version usually depends on the latest ape development
version and information to download can be found [here](http://ape-package.ird.fr/ape_installation.html).
Additionally you may need to install on windows [Rtools](https://cran.r-project.org/bin/windows/Rtools/) and on mac [XCode](https://developer.apple.com/xcode/)
and [GFortran](https://gcc.gnu.org/wiki/GFortranBinaries).
If you use phangorn please cite:
Schliep K.P. 2011. phangorn: phylogenetic analysis in R. Bioinformatics, 27(4) 592-593
License
-------
phangorn is licensed under the GPLv2.
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