Description Usage Arguments Details Value References See Also Examples
Apply the NANUQ algorithm of \insertCiteABR19;textualMSCquartets to infer a hybridization network from a collection of gene trees, under the level1 network multispecies coalescent (NMSC) model.
1 2 3 4 5 6 7 8 9 10 
genedata 
gene tree data that may be supplied in any of 3 forms:

outfile 
a character string giving an output file name stub for
saving a 
omit 

epsilon 
minimum for branch lengths to be treated as nonzero; ignored if gene tree data given as quartet table 
alpha 
a value or vector of significance levels for judging pvalues testing a null hypothesis of no hybridization vs. an alternative of hybridization, for each quartet; a smaller value applies a less conservative test for a tree (more trees), hence a stricter requirement for desciding in favor of hybridization (fewer reticulations) 
beta 
a value or vector of significance levels for judging pvalues testing
a null hypothesis of a star tree (polytomy) for each quartet vs. an alternative of anything else; a smaller value applies a less conservative
test for a star tree (more polytomies), hence a stricter requirement for deciding in favor of a resolved tree or network;
if vectors, 
taxanames 
if 
plot 

This function
counts displayed quartets across gene trees to form quartet count concordance factors (qcCFs),
applies appropriate hypothesis tests to judge qcCFs as representing putative hybridization,
resolved trees, or unresolved (star) trees using alpha
and beta
as significance levels,
produces a simplex plot showing results of the hypothesis tests for all qcCFs
computes the appropriate NANUQ distance table, writing it to a file.
The distance table file
can then be opened in the external software SplitsTree \insertCiteSplitsTreeMSCquartets (recommended) or within R using the package phangorn
to
obtain a circular split system under the NeighborNet algorithm, which is then depicted as a splits graph.
The splits graph should be interpreted via
the theory of \insertCiteABR19;textualMSCquartets to infer the level1 species network, or to conclude the data does
not arise from the NMSC on such a network.
If alpha
and beta
are vectors, they must have the same length k. Then the ith entries are paired to
produce k plots and k output files. This is equivalent to k calls to NANUQ
with scalar values of alpha
and beta
.
A call of NANUQ
with genedata
given as a table previously output from NANUQ
is
equivalent to a call of NANUQdist
. If genedata
is a table previously output from quartetTableResolved
which lacks columns of pvalues for hypothesis tests, these will be appended to the table output by NANUQ
.
If plots are produced, each point represents an empirical quartet concordance factor, colorcoded to represent test results.
In general, alpha
should be chosen to be small and beta
to be large so that most quartets are interpreted as resolved trees.
Usually, an initial call to NANUQ
will not give a good analysis, as values
of alpha
and beta
are likely to need some adjustment based on inspecting the data. Saving the returned
table from NANUQ
will allow for the results of the timeconsuming computation of qcCFs to be
saved, along with pvalues,
for input to further calls of NANUQ
with new choices of alpha
and beta
.
See the documentation for quartetNetworkDist
for an explanation of a small, rarely noticeable,
stochastic element of the algorithm.
For data sets of many gene trees, user time may be reduced by using parallel code for
counting displayed quartets. See quartetTableParallel
, where example commands are given.
a table of quartets and pvalues for judging fit to the MSC on quartet
trees (returned invisibly);
this table can be used as input to NANUQ
or NANUQdist
with new choices of alpha and beta, without retallying quartets on
gene trees; a distance table to be used as input for SplitsTree is written to a nexus file
ABR19MSCquartets
\insertRefSplitsTreeMSCquartets
quartetTable
, quartetTableParallel
, quartetTableDominant
, quartetTreeTestInd
,
quartetStarTestInd
, NANUQdist
, quartetTestPlot
, pvalHist
,
quartetNetworkDist
1 2 3 4 5 6 7 8 9  pTable=NANUQ(system.file("extdata", "dataYeastRokas",package="MSCquartets"),
alpha=.0001, beta=.95, outfile = file.path(tempdir(), "NANUQdist"))
NANUQ(pTable, alpha=.05, beta=.95,outfile = file.path(tempdir(), "NANUQdist"))
# The distance table was written to an output file for opening in SplitsTree.
# Alternately, to use the experimental phangorn implementation of NeighborNet
# within R, enter the following additional lines:
dist=NANUQdist(pTable, alpha=.05, beta=.95,outfile = file.path(tempdir(), "NANUQdist"))
nn=neighborNet(dist)
plot(nn,"2D")

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