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81_make_mrbayes_semistrict_tree_rresults.txt
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81_make_mrbayes_semistrict_tree_rresults.txt
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> ## this script takes semistric data and applies MrBayes utility to build Bayesian tree
> ## requires MrBayes installation and working "mb-mpi
>
> library(shipunov)
package 'shipunov', version 1.5-1
> library(ape)
> DATE <- format(Sys.time(), "%Y%m%d_%H%M%S") # timestamp
> treesp <- read.table("_kubricks_treesp.txt", sep="\t", h=TRUE, as.is=TRUE) # to understand outgroups and outliers
> outgroups <- treesp$SPECIES.NEW[treesp$TYPE == "outgroup" & treesp$USE == 1]
> outgroups <- gsub(" ", "_", outgroups)
> conc <- read.dna("40_concatenated/semistrict.fasta", format="fasta")
> LAB <- sub("__.*$", "", labels(conc))
> OUT <- labels(conc)[LAB %in% outgroups]
> outliers <- treesp$SPECIES.NEW[treesp$TYPE == "outlier" & treesp$USE == 1]
> if (length(outliers) > 0) {
+ outliers <- gsub(" ", "_", outliers)
+ EXC <- labels(conc)[LAB %in% outliers]
+ conc <- conc[!labels(conc) %in% EXC, ] # remove outliers, if any
+ }
> setwd("80_mrbayes_working") # go to MrBayes working directory
> tr <- MrBayes(conc, file="semistrict", exec="mb-mpi", # change MrBayes binary if needed, on Windows, you _need_ to change it
+ ngen=1e+04, # change MrBayes options if needed
+ run=TRUE) # default is not run, just make a NEXUS file for MrBayes
#NEXUS
[created by ips on Sat Feb 29 14:13:33 2020]
begin data;
dimensions ntax=12 nchar=1119;
format datatype=dna missing=N gap=-;
matrix
Kubrickus_heus__K-008 aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtagga-gaaaatccgttggctttatagaccgtgagaactggcctcaaatcaggtaggactacccgctgaacttaagcatatcaataagcggaggaaaagaaactaacaaggattcccctagtaacggcgagtgaagcgggaagagctcaaatttgaaatctggtggcctcaggtcatccgagttgtaatctatagaagtgttttccgtgctggct-catgtac--aagtcccttggaacagggcgtcatagagggtgagaatcccgtccttgacatgaactaccagtgctct-------gtgatacattttcaacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaattccatctaaagctaaatattggcgagagaccgatagcgccaaccca-cccc
Kubrickus_aus__K-001_efgh_- --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn
Kubrickus_beus__K-002_efgh_- --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn
Kubrickus_ceus__K-003_efgh_- aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn
Kubrickus_deus__K-004 aggataataacattgcatttgcaatgcagaaataatataatgattaccagccagtcatattcgattggggtagagatagagatggcgagagatggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattacctcagtaacggcgagtgaagcggtaacagctcaaatttgaaagctagctcttttagg--gttcgcattgtaatttgtagaagatgcttcgggtgtggcc-ccggtct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgtgactgg--------gtgctttcgctcatgtgaagctctttcgacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaatttcatctaaagctaaatattggccagagaccgatagcgacaaca-cccccc
Kubrickus_eus__K-005 aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatctaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattacctcagtaacggcgagtgaagcggtaacagctcaaatttgaaagctagctcttttagg--gttcgcattgtaatttgtagaagatgcttcgggtgtggcc-ccggtct--aggttccttggaacaggacgtcatagagggtgagaatcccgtatgtgactgg--------gtgctttcgctcatgtgaagctctttcgacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaatttcatctaaagctaaatattggccagagaccgatagcgacaaca-cccccc
Kubrickus_feus__K-006_efgh_XYZ124 --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattacctcagtaacggcgagtgaagcggtaacagctcaaatttgaaagctagctcttttagg--gttcgcattgtaatttgtagaagatccttcgggtgtggcc-ccggtctggaggttccttggaacaggacgtcatagagggtgagaatcccgt--gtgactgg--------gtgctttcgctcatgtgaagctctttcgacgagtcgagttgtttgggaatgcagctcaaaatgggtggtaaatttcatctaaagct--atattggccagagaccgatagcgacacaa-cccca-
Kubrickus_geus__K-007_efgh_- aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgacagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn
Kubrickus_ieus__K-009_efgh_- aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagatggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn
Kubrickus_jeus__K-010 --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagatggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgacagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattgctctagtaacggcgagtgaagcagcaatagctcaaatttgaaatctggcgtcttcgac--gtccgagttgtaatttgtagaggatgcttctga-gtggccaccgacct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgcggtcggaa----aggcgctct-----atacgtagctccttcgacgagtcgagtcgtttgggaatgcagctc-taatgtg-agtaaatttcttctaaagct-aatattggccagagaccgatagcgaacaccacaaaca
Kubrickus_keus__K-011 --gataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagta-gggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctacgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgaaagattcgagttgaagtactgattttacattaagtaatccaattatgaatttctctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagacttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattgctctagtaacggcgagtgaagcagcaatagctcaaatttgaaatctggcgtcttcgac--gtccgagttgtaatttgtagaggatgcttctga-gtggccaccgacct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgcggtcggaa----aggcgctct-----atacgtagctccttcgacgagtcgagttgtttgggaatgcagctc-taatggg-agtaaatttcttctaaagct-aatattggccagagaccgatagcgaacaccacaaaca
Kubrickus_leus__K-012 aggataataacattgcatttgaaatgcagaaataatataatgattaccagccagtaatattcgattggggtagagatagagatggcgagagaaggggagtaggggcagaatctcccacccaatattgagcaaatatccaatgaataacactgatggatattagatcctatgattatgatctcgttctccgagaaggggatatggcggaattggtagacgctacggacttgatcgaattgagccttggtatggaaacctaccaagtgatagcttccaaatccagggaaccctgggatattttgaatgggtaatcctgagccaaatccggttcatggagacaatagtttcttcttttattctcctaagataggaaggggataggtgcagagactcaatggaagctattctaacgaatgaatctcatttggtccaatactgtatttatagaacgctctatttacacctaaaaagtgggaatgtgatataacatcagacaaaactcgcgatcagaacttgaatcgttccaagcatctattcgtaagatagatgccagattcgagttgaagtactgattttacattaagtaatccaattatgaatttatctactttagatagagaattgaatcagtttttggaataaatggttggacgagaataaagatagagtccaattctacgtgtcaatgtcaacaacaatgcaaattgcagtaggaggaaaatccgttggctttatagaccgtgagccttgacctcggatcaggtagggatacccgctgaacttaagcatatcaataagcggaggaaaagaaaccaacagggattgctctagtaacggcgagtgaagcagcaatagctcaaatttgaaatctggcgtcttcgac--gtccgagttgtaatttgtagaggatgcttctga-gtggccaccgacct--aagttccttggaacaggacgtcatagagggtgagaatcccgtatgcggtcggaa----aggcgctct-----atacgtagctccttcgacgagtcgagttgtttgggaatgcagctc-taatggg-agtaaattttttctaaagct-aatattggccagagaccgatagcgaacaccacaaaca
;
end;
MrBayes v3.2.6 x64
(Bayesian Analysis of Phylogeny)
(Parallel version)
(2 processors available)
Distributed under the GNU General Public License
Type "help" or "help <command>" for information
on the commands that are available.
Type "about" for authorship and general
information about the program.
Executing file "semistrict"
UNIX line termination
Longest line length = 1155
Parsing file
Expecting NEXUS formatted file
Reading data block
Allocated taxon set
Allocated matrix
Defining new matrix with 12 taxa and 1119 characters
Data is Dna
Missing data coded as N
Gaps coded as -
Taxon 1 -> Kubrickus_heus__K-008
Taxon 2 -> Kubrickus_aus__K-001_efgh_-
Taxon 3 -> Kubrickus_beus__K-002_efgh_-
Taxon 4 -> Kubrickus_ceus__K-003_efgh_-
Taxon 5 -> Kubrickus_deus__K-004
Taxon 6 -> Kubrickus_eus__K-005
Taxon 7 -> Kubrickus_feus__K-006_efgh_XYZ124
Taxon 8 -> Kubrickus_geus__K-007_efgh_-
Taxon 9 -> Kubrickus_ieus__K-009_efgh_-
Taxon 10 -> Kubrickus_jeus__K-010
Taxon 11 -> Kubrickus_keus__K-011
Taxon 12 -> Kubrickus_leus__K-012
Successfully read matrix
Setting default partition (does not divide up characters)
Setting model defaults
Seed (for generating default start values) = 1582953214
Setting output file names to "semistrict.run<i>.<p|t>"
Exiting data block
Reading mrbayes block
Setting Nst to 6
Setting Rates to Invgamma
Setting Ngammacat to 4
Successfully set likelihood model parameters
Setting number of runs to 2
Setting number of generations to 10000
Setting print frequency to 100
Setting sample frequency to 10
Setting number of chains to 4
Setting heating parameter to 0.200000
Setting chain output file names to "semistrict.run<i>.<p/t>"
Running Markov chain
MCMC stamp = 7524774487
Seed = 1005929022
Swapseed = 1582953214
Model settings:
Data not partitioned --
Datatype = DNA
Nucmodel = 4by4
Nst = 6
Substitution rates, expressed as proportions
of the rate sum, have a Dirichlet prior
(1.00,1.00,1.00,1.00,1.00,1.00)
Covarion = No
# States = 4
State frequencies have a Dirichlet prior
(1.00,1.00,1.00,1.00)
Rates = Invgamma
The distribution is approximated using 4 categories.
Likelihood summarized over all rate categories in each generation.
Shape parameter is exponentially
distributed with parameter (1.00).
Proportion of invariable sites is uniformly dist-
ributed on the interval (0.00,1.00).
Active parameters:
Parameters
---------------------
Revmat 1
Statefreq 2
Shape 3
Pinvar 4
Ratemultiplier 5
Topology 6
Brlens 7
---------------------
1 -- Parameter = Revmat
Type = Rates of reversible rate matrix
Prior = Dirichlet(1.00,1.00,1.00,1.00,1.00,1.00)
2 -- Parameter = Pi
Type = Stationary state frequencies
Prior = Dirichlet
3 -- Parameter = Alpha
Type = Shape of scaled gamma distribution of site rates
Prior = Exponential(1.00)
4 -- Parameter = Pinvar
Type = Proportion of invariable sites
Prior = Uniform(0.00,1.00)
5 -- Parameter = Ratemultiplier
Type = Partition-specific rate multiplier
Prior = Fixed(1.0)
6 -- Parameter = Tau
Type = Topology
Prior = All topologies equally probable a priori
Subparam. = V
7 -- Parameter = V
Type = Branch lengths
Prior = Unconstrained:GammaDir(1.0,0.1000,1.0,1.0)
Number of chains per MPI processor = 4
The MCMC sampler will use the following moves:
With prob. Chain will use move
0.93 % Dirichlet(Revmat)
0.93 % Slider(Revmat)
0.93 % Dirichlet(Pi)
0.93 % Slider(Pi)
1.85 % Multiplier(Alpha)
1.85 % Slider(Pinvar)
9.26 % ExtSPR(Tau,V)
9.26 % ExtTBR(Tau,V)
9.26 % NNI(Tau,V)
9.26 % ParsSPR(Tau,V)
37.04 % Multiplier(V)
12.96 % Nodeslider(V)
5.56 % TLMultiplier(V)
Division 1 has 83 unique site patterns
Initializing conditional likelihoods
Using standard SSE likelihood calculator for division 1 (single-precision)
Initializing invariable-site conditional likelihoods
Initial log likelihoods and log prior probs for run 1:
Chain 1 -- -2771.124336 -- 42.620562
Chain 2 -- -2716.857739 -- 42.620562
Chain 3 -- -2881.576235 -- 42.620562
Chain 4 -- -2738.556069 -- 42.620562
There are 4 more chains on the other processor
Using a relative burnin of 25.0 % for diagnostics
Chain results (10000 generations requested):
0 -- [-2771.124] (-2716.858) (-2881.576) (-2738.556) [...4 remote chains...]
100 -- (-2383.057) (-2372.321) [-2358.153] (-2405.709) [...4 remote chains...] -- 0:00:00
200 -- (-2302.387) [-2272.942] (-2293.187) (-2303.654) [...4 remote chains...] -- 0:00:00
300 -- (-2273.977) (-2246.896) [-2247.789] (-2281.203) [...4 remote chains...] -- 0:00:00
400 -- (-2268.257) [-2235.761] (-2244.889) (-2272.900) [...4 remote chains...] -- 0:00:00
500 -- (-2258.833) (-2241.123) [-2221.521] (-2263.295) [...4 remote chains...] -- 0:00:00
600 -- (-2259.834) (-2232.494) [-2217.589] (-2253.882) [...4 remote chains...] -- 0:00:00
700 -- (-2265.463) (-2231.658) [-2219.727] (-2248.213) [...4 remote chains...] -- 0:00:00
800 -- (-2248.340) (-2229.645) [-2218.271] (-2241.454) [...4 remote chains...] -- 0:00:00
900 -- (-2255.552) (-2230.408) [-2214.836] (-2233.075) [...4 remote chains...] -- 0:00:00
1000 -- (-2255.369) [-2222.359] (-2216.838) (-2233.827) [...4 remote chains...] -- 0:00:00
1100 -- (-2242.912) [-2222.194] (-2225.357) (-2236.741) [...4 remote chains...] -- 0:00:00
1200 -- (-2245.289) (-2219.750) (-2221.226) [-2228.853] [...4 remote chains...] -- 0:00:00
1300 -- (-2240.249) [-2219.460] (-2222.640) (-2229.209) [...4 remote chains...] -- 0:00:00
1400 -- (-2239.562) [-2217.631] (-2220.214) (-2234.610) [...4 remote chains...] -- 0:00:00
1500 -- (-2231.749) [-2220.099] (-2224.680) (-2227.203) [...4 remote chains...] -- 0:00:00
1600 -- (-2227.318) (-2222.698) [-2227.342] (-2235.646) [...4 remote chains...] -- 0:00:00
1700 -- (-2221.688) (-2220.357) (-2221.398) [-2218.413] [...4 remote chains...] -- 0:00:00
1800 -- (-2222.162) (-2217.466) [-2211.275] (-2221.446) [...4 remote chains...] -- 0:00:00
1900 -- (-2227.325) (-2215.727) (-2215.232) [-2225.253] [...4 remote chains...] -- 0:00:00
2000 -- (-2229.489) (-2216.439) [-2212.487] (-2229.980) [...4 remote chains...] -- 0:00:00
2100 -- (-2221.504) (-2218.118) (-2216.634) [-2218.467] [...4 remote chains...] -- 0:00:00
2200 -- (-2223.721) [-2212.924] (-2215.173) (-2231.333) [...4 remote chains...] -- 0:00:00
2300 -- (-2223.005) [-2210.666] (-2206.907) (-2219.800) [...4 remote chains...] -- 0:00:00
2400 -- (-2226.506) [-2214.140] (-2207.102) (-2216.210) [...4 remote chains...] -- 0:00:00
2500 -- (-2224.416) [-2209.934] (-2205.697) (-2213.977) [...4 remote chains...] -- 0:00:00
2600 -- (-2219.647) [-2212.594] (-2207.736) (-2216.012) [...4 remote chains...] -- 0:00:00
2700 -- (-2220.375) (-2209.339) [-2205.144] (-2216.678) [...4 remote chains...] -- 0:00:00
2800 -- (-2222.688) [-2213.957] (-2206.775) (-2211.540) [...4 remote chains...] -- 0:00:00
2900 -- (-2227.200) (-2219.084) (-2211.640) [-2215.036] [...4 remote chains...] -- 0:00:00
3000 -- (-2222.219) (-2215.579) [-2205.416] (-2215.810) [...4 remote chains...] -- 0:00:00
3100 -- (-2225.985) (-2224.688) [-2195.950] (-2224.362) [...4 remote chains...] -- 0:00:00
3200 -- (-2228.005) (-2213.211) [-2203.684] (-2219.681) [...4 remote chains...] -- 0:00:00
3300 -- (-2226.545) (-2221.826) [-2199.496] (-2219.793) [...4 remote chains...] -- 0:00:00
3400 -- (-2231.160) [-2210.326] (-2200.023) (-2217.070) [...4 remote chains...] -- 0:00:00
3500 -- (-2230.143) (-2215.871) (-2205.317) [-2209.671] [...4 remote chains...] -- 0:00:00
3600 -- (-2229.558) (-2218.752) [-2198.801] (-2215.178) [...4 remote chains...] -- 0:00:00
3700 -- (-2231.747) (-2212.128) [-2205.745] (-2213.719) [...4 remote chains...] -- 0:00:00
3800 -- (-2231.701) (-2214.989) [-2205.450] (-2215.544) [...4 remote chains...] -- 0:00:00
3900 -- (-2226.519) (-2214.661) (-2206.311) [-2207.515] [...4 remote chains...] -- 0:00:00
4000 -- (-2227.687) (-2212.305) [-2207.236] (-2211.254) [...4 remote chains...] -- 0:00:00
4100 -- (-2226.434) [-2217.730] (-2211.853) (-2211.619) [...4 remote chains...] -- 0:00:00
4200 -- (-2219.460) [-2211.290] (-2202.443) (-2212.377) [...4 remote chains...] -- 0:00:00
4300 -- (-2218.558) (-2207.241) [-2201.829] (-2215.528) [...4 remote chains...] -- 0:00:00
4400 -- (-2217.736) (-2214.035) [-2203.650] (-2216.661) [...4 remote chains...] -- 0:00:00
4500 -- (-2214.425) (-2213.642) [-2206.203] (-2207.944) [...4 remote chains...] -- 0:00:00
4600 -- (-2224.126) (-2218.506) (-2211.844) [-2212.875] [...4 remote chains...] -- 0:00:00
4700 -- (-2230.270) [-2209.951] (-2208.691) (-2215.092) [...4 remote chains...] -- 0:00:00
4800 -- (-2227.887) (-2210.280) [-2206.588] (-2211.816) [...4 remote chains...] -- 0:00:00
4900 -- (-2230.940) [-2204.757] (-2205.956) (-2212.312) [...4 remote chains...] -- 0:00:00
5000 -- (-2219.978) [-2204.439] (-2207.286) (-2217.236) [...4 remote chains...] -- 0:00:00
Average standard deviation of split frequencies: 0.061481
5100 -- (-2216.345) (-2204.033) [-2209.804] (-2209.585) [...4 remote chains...] -- 0:00:00
5200 -- (-2221.051) [-2205.580] (-2215.802) (-2204.511) [...4 remote chains...] -- 0:00:00
5300 -- (-2225.298) [-2195.218] (-2210.013) (-2208.972) [...4 remote chains...] -- 0:00:00
5400 -- (-2231.389) [-2197.442] (-2221.221) (-2205.241) [...4 remote chains...] -- 0:00:00
5500 -- (-2233.397) [-2199.226] (-2220.772) (-2205.409) [...4 remote chains...] -- 0:00:00
5600 -- (-2226.507) [-2199.614] (-2217.998) (-2207.983) [...4 remote chains...] -- 0:00:00
5700 -- (-2226.327) [-2197.571] (-2218.096) (-2210.174) [...4 remote chains...] -- 0:00:00
5800 -- (-2223.883) [-2199.572] (-2215.344) (-2209.479) [...4 remote chains...] -- 0:00:00
5900 -- (-2226.900) [-2199.864] (-2212.863) (-2216.056) [...4 remote chains...] -- 0:00:00
6000 -- (-2225.782) [-2200.524] (-2218.419) (-2217.728) [...4 remote chains...] -- 0:00:00
6100 -- (-2231.717) [-2200.286] (-2213.978) (-2214.404) [...4 remote chains...] -- 0:00:00
6200 -- (-2226.132) [-2200.896] (-2205.508) (-2219.192) [...4 remote chains...] -- 0:00:00
6300 -- (-2223.805) [-2204.844] (-2208.911) (-2217.060) [...4 remote chains...] -- 0:00:00
6400 -- (-2223.326) [-2202.726] (-2204.878) (-2216.653) [...4 remote chains...] -- 0:00:00
6500 -- (-2224.699) [-2205.479] (-2212.596) (-2214.634) [...4 remote chains...] -- 0:00:00
6600 -- (-2227.419) [-2199.915] (-2211.627) (-2219.467) [...4 remote chains...] -- 0:00:00
6700 -- (-2220.570) (-2208.481) [-2203.745] (-2228.153) [...4 remote chains...] -- 0:00:00
6800 -- (-2219.162) [-2200.033] (-2201.607) (-2210.306) [...4 remote chains...] -- 0:00:00
6900 -- (-2227.271) (-2200.452) [-2198.719] (-2216.419) [...4 remote chains...] -- 0:00:00
7000 -- (-2215.432) [-2203.140] (-2201.594) (-2213.939) [...4 remote chains...] -- 0:00:00
7100 -- (-2220.540) (-2206.670) [-2200.403] (-2217.383) [...4 remote chains...] -- 0:00:00
7200 -- (-2218.670) [-2202.027] (-2207.599) (-2214.649) [...4 remote chains...] -- 0:00:00
7300 -- (-2218.408) [-2212.488] (-2206.458) (-2217.891) [...4 remote chains...] -- 0:00:00
7400 -- (-2213.730) [-2207.775] (-2210.282) (-2217.004) [...4 remote chains...] -- 0:00:00
7500 -- (-2213.103) [-2206.540] (-2209.469) (-2220.920) [...4 remote chains...] -- 0:00:00
7600 -- (-2217.260) [-2202.697] (-2206.090) (-2220.164) [...4 remote chains...] -- 0:00:00
7700 -- (-2213.114) [-2204.830] (-2207.898) (-2216.292) [...4 remote chains...] -- 0:00:00
7800 -- (-2222.592) [-2208.285] (-2206.016) (-2222.545) [...4 remote chains...] -- 0:00:00
7900 -- (-2219.878) [-2205.508] (-2221.416) (-2221.526) [...4 remote chains...] -- 0:00:00
8000 -- (-2209.222) (-2218.874) [-2212.746] (-2215.200) [...4 remote chains...] -- 0:00:00
8100 -- (-2217.085) (-2209.575) [-2206.261] (-2210.196) [...4 remote chains...] -- 0:00:00
8200 -- (-2208.824) (-2217.770) (-2205.465) [-2205.456] [...4 remote chains...] -- 0:00:00
8300 -- [-2208.022] (-2213.092) (-2208.136) (-2213.600) [...4 remote chains...] -- 0:00:00
8400 -- (-2210.061) (-2214.718) (-2203.606) [-2213.503] [...4 remote chains...] -- 0:00:00
8500 -- (-2208.436) (-2215.118) [-2198.055] (-2213.414) [...4 remote chains...] -- 0:00:00
8600 -- [-2201.826] (-2209.755) (-2204.270) (-2217.296) [...4 remote chains...] -- 0:00:00
8700 -- (-2210.144) (-2218.115) [-2206.291] (-2213.281) [...4 remote chains...] -- 0:00:00
8800 -- (-2212.395) (-2215.419) [-2205.856] (-2210.960) [...4 remote chains...] -- 0:00:00
8900 -- [-2202.247] (-2209.077) (-2206.486) (-2215.793) [...4 remote chains...] -- 0:00:00
9000 -- (-2206.786) (-2212.253) [-2200.503] (-2213.866) [...4 remote chains...] -- 0:00:00
9100 -- (-2209.686) (-2210.854) [-2206.431] (-2213.892) [...4 remote chains...] -- 0:00:00
9200 -- [-2211.126] (-2208.937) (-2215.171) (-2209.550) [...4 remote chains...] -- 0:00:00
9300 -- (-2205.449) (-2212.711) (-2209.399) [-2209.335] [...4 remote chains...] -- 0:00:00
9400 -- [-2201.742] (-2215.367) (-2213.201) (-2216.808) [...4 remote chains...] -- 0:00:00
9500 -- [-2198.975] (-2212.760) (-2212.839) (-2221.615) [...4 remote chains...] -- 0:00:00
9600 -- [-2198.164] (-2213.166) (-2210.907) (-2218.351) [...4 remote chains...] -- 0:00:00
9700 -- (-2200.033) [-2207.892] (-2206.640) (-2212.555) [...4 remote chains...] -- 0:00:00
9800 -- (-2200.310) (-2210.881) [-2210.337] (-2212.054) [...4 remote chains...] -- 0:00:00
9900 -- (-2201.485) (-2216.307) [-2205.735] (-2208.511) [...4 remote chains...] -- 0:00:00
10000 -- (-2200.246) (-2213.404) [-2205.623] (-2213.224) [...4 remote chains...] -- 0:00:00
Average standard deviation of split frequencies: 0.050891
Analysis completed in 1 second
Analysis used 1.78 seconds of CPU time on processor 0
Likelihood of best state for "cold" chain of run 1 was -2194.89
Likelihood of best state for "cold" chain of run 2 was -2197.80
Acceptance rates for the moves in the "cold" chain of run 1:
With prob. (last 100) chain accepted proposals by move
NA NA Dirichlet(Revmat)
NA NA Slider(Revmat)
NA NA Dirichlet(Pi)
NA NA Slider(Pi)
82.7 % ( 81 %) Multiplier(Alpha)
96.2 % ( 95 %) Slider(Pinvar)
16.4 % ( 19 %) ExtSPR(Tau,V)
15.5 % ( 18 %) ExtTBR(Tau,V)
18.8 % ( 27 %) NNI(Tau,V)
7.1 % ( 10 %) ParsSPR(Tau,V)
73.3 % ( 72 %) Multiplier(V)
50.2 % ( 48 %) Nodeslider(V)
27.3 % ( 25 %) TLMultiplier(V)
Acceptance rates for the moves in the "cold" chain of run 2:
With prob. (last 100) chain accepted proposals by move
NA NA Dirichlet(Revmat)
NA NA Slider(Revmat)
NA NA Dirichlet(Pi)
NA NA Slider(Pi)
86.2 % ( 85 %) Multiplier(Alpha)
90.4 % ( 92 %) Slider(Pinvar)
14.2 % ( 22 %) ExtSPR(Tau,V)
13.1 % ( 15 %) ExtTBR(Tau,V)
19.1 % ( 21 %) NNI(Tau,V)
7.1 % ( 7 %) ParsSPR(Tau,V)
72.5 % ( 66 %) Multiplier(V)
53.0 % ( 55 %) Nodeslider(V)
23.8 % ( 23 %) TLMultiplier(V)
Chain swap information for run 1:
1 2 3 4
--------------------------
1 | 0.43 0.17 0.06
2 | 1654 0.49 0.22
3 | 1695 1635 0.59
4 | 1692 1663 1661
Chain swap information for run 2:
1 2 3 4
--------------------------
1 | 0.49 0.22 0.07
2 | 1725 0.55 0.26
3 | 1693 1645 0.57
4 | 1654 1630 1653
Upper diagonal: Proportion of successful state exchanges between chains
Lower diagonal: Number of attempted state exchanges between chains
Chain information:
ID -- Heat
-----------
1 -- 1.00 (cold chain)
2 -- 0.83
3 -- 0.71
4 -- 0.62
Heat = 1 / (1 + T * (ID - 1))
(where T = 0.20 is the temperature and ID is the chain number)
Setting sumt filename and outputname to semistrict
Setting urn-in to 10
Setting sumt contype to Allcompat
Setting sumt conformat to Simple
Summarizing trees in files "semistrict.run1.t" and "semistrict.run2.t"
Using relative burnin ('relburnin=yes'), discarding the first 25 % of sampled trees
Writing statistics to files semistrict.<parts|tstat|vstat|trprobs|con>
Examining first file ...
Found one tree block in file "semistrict.run1.t" with 1001 trees in last block
Expecting the same number of trees in the last tree block of all files
Tree reading status:
0 10 20 30 40 50 60 70 80 90 100
v-------v-------v-------v-------v-------v-------v-------v-------v-------v-------v
*********************************************************************************
Read a total of 2002 trees in 2 files (sampling 1502 of them)
(Each file contained 1001 trees of which 751 were sampled)
General explanation:
In an unrooted tree, a taxon bipartition (split) is specified by removing a
branch, thereby dividing the species into those to the left and those to the
right of the branch. Here, taxa to one side of the removed branch are denoted
'.' and those to the other side are denoted '*'. Specifically, the '.' symbol
is used for the taxa on the same side as the outgroup.
In a rooted or clock tree, the tree is rooted using the model and not by
reference to an outgroup. Each bipartition therefore corresponds to a clade,
that is, a group that includes all the descendants of a particular branch in
the tree. Taxa that are included in each clade are denoted using '*', and
taxa that are not included are denoted using the '.' symbol.
The output first includes a key to all the bipartitions with frequency larger
or equual to (Minpartfreq) in at least one run. Minpartfreq is a parameter to
sumt command and currently it is set to 0.10. This is followed by a table
with statistics for the informative bipartitions (those including at least
two taxa), sorted from highest to lowest probability. For each bipartition,
the table gives the number of times the partition or split was observed in all
runs (#obs) and the posterior probability of the bipartition (Probab.), which
is the same as the split frequency. If several runs are summarized, this is
followed by the minimum split frequency (Min(s)), the maximum frequency
(Max(s)), and the standard deviation of frequencies (Stddev(s)) across runs.
The latter value should approach 0 for all bipartitions as MCMC runs converge.
This is followed by a table summarizing branch lengths, node heights (if a
clock model was used) and relaxed clock parameters (if a relaxed clock model
was used). The mean, variance, and 95 % credible interval are given for each
of these parameters. If several runs are summarized, the potential scale
reduction factor (PSRF) is also given; it should approach 1 as runs converge.
Node heights will take calibration points into account, if such points were
used in the analysis.
Note that Stddev may be unreliable if the partition is not present in all
runs (the last column indicates the number of runs that sampled the partition
if more than one run is summarized). The PSRF is not calculated at all if
the partition is not present in all runs.The PSRF is also sensitive to small
sample sizes and it should only be considered a rough guide to convergence
since some of the assumptions allowing one to interpret it as a true potential
scale reduction factor are violated in MrBayes.
List of taxa in bipartitions:
1 -- Kubrickus_heus__K-008
2 -- Kubrickus_aus__K-001_efgh_-
3 -- Kubrickus_beus__K-002_efgh_-
4 -- Kubrickus_ceus__K-003_efgh_-
5 -- Kubrickus_deus__K-004
6 -- Kubrickus_eus__K-005
7 -- Kubrickus_feus__K-006_efgh_XYZ124
8 -- Kubrickus_geus__K-007_efgh_-
9 -- Kubrickus_ieus__K-009_efgh_-
10 -- Kubrickus_jeus__K-010
11 -- Kubrickus_keus__K-011
12 -- Kubrickus_leus__K-012
Key to taxon bipartitions (saved to file "semistrict.parts"):
ID -- Partition
------------------
1 -- .***********
2 -- .*..........
3 -- ..*.........
4 -- ...*........
5 -- ....*.......
6 -- .....*......
7 -- ......*.....
8 -- .......*....
9 -- ........*...
10 -- .........*..
11 -- ..........*.
12 -- ...........*
13 -- ....*...*...
14 -- .........**.
15 -- .**.********
16 -- .......*.***
17 -- .......*...*
18 -- ....***.*...
19 -- ..*....*.***
20 -- ....*.*.*...
21 -- ..*...*.....
22 -- .*..***.*...
23 -- .**.***.*...
24 -- ..*.***.*...
25 -- ..*..**.....
26 -- .......*.**.
27 -- ..*.********
28 -- .....**.....
29 -- .........***
30 -- .**....*.***
31 -- .*.....*.***
32 -- .*...*......
------------------
Summary statistics for informative taxon bipartitions
(saved to file "semistrict.tstat"):
ID #obs Probab. Sd(s)+ Min(s) Max(s) Nruns
----------------------------------------------------------------
13 1420 0.945406 0.005649 0.941411 0.949401 2
14 1292 0.860186 0.035779 0.834887 0.885486 2
15 1260 0.838881 0.086623 0.777630 0.900133 2
16 1258 0.837550 0.030130 0.816245 0.858855 2
17 906 0.603196 0.020714 0.588549 0.617843 2
18 726 0.483356 0.126168 0.394141 0.572570 2
19 659 0.438748 0.072500 0.387483 0.490013 2
20 584 0.388815 0.077207 0.334221 0.443409 2
21 538 0.358189 0.043311 0.327563 0.388815 2
22 487 0.324234 0.046136 0.291611 0.356858 2
23 483 0.321571 0.106396 0.246338 0.396804 2
24 447 0.297603 0.080032 0.241012 0.354194 2
25 342 0.227696 0.015065 0.217044 0.238349 2
26 335 0.223036 0.017890 0.210386 0.235686 2
27 239 0.159121 0.072500 0.107856 0.210386 2
28 223 0.148469 0.006591 0.143808 0.153129 2
29 188 0.125166 0.037662 0.098535 0.151798 2
30 147 0.097870 0.032954 0.074567 0.121172 2
31 109 0.072570 0.055552 0.033289 0.111851 2
32 104 0.069241 0.048961 0.034621 0.103862 2
----------------------------------------------------------------
+ Convergence diagnostic (standard deviation of split frequencies)
should approach 0.0 as runs converge.
Summary statistics for branch and node parameters
(saved to file "semistrict.vstat"):
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median PSRF+ Nruns
--------------------------------------------------------------------------------------
length[1] 0.026670 0.000238 0.000458 0.053421 0.026134 1.001 2
length[2] 0.001473 0.000002 0.000000 0.004044 0.001048 1.073 2
length[3] 0.001574 0.000002 0.000016 0.004380 0.001172 1.051 2
length[4] 0.001473 0.000002 0.000006 0.005024 0.000864 1.021 2
length[5] 0.002986 0.000003 0.000399 0.006841 0.002621 1.008 2
length[6] 0.001801 0.000002 0.000018 0.004483 0.001480 1.003 2
length[7] 0.004259 0.000005 0.000346 0.008024 0.003937 1.001 2
length[8] 0.001927 0.000005 0.000050 0.006060 0.001351 1.008 2
length[9] 0.001455 0.000002 0.000006 0.003801 0.001110 1.031 2
length[10] 0.003401 0.000003 0.000698 0.006717 0.003201 1.000 2
length[11] 0.001710 0.000002 0.000023 0.004312 0.001405 1.001 2
length[12] 0.001779 0.000002 0.000007 0.004219 0.001525 1.005 2
length[13] 0.002195 0.000002 0.000134 0.005377 0.001915 1.001 2
length[14] 0.002714 0.000004 0.000063 0.007091 0.002302 1.021 2
length[15] 0.022319 0.000175 0.000179 0.046181 0.021275 1.000 2
length[16] 0.022023 0.000101 0.002060 0.038118 0.023971 1.018 2
length[17] 0.002418 0.000002 0.000212 0.005462 0.002149 1.011 2
length[18] 0.010195 0.000037 0.000257 0.020812 0.009751 1.017 2
length[19] 0.016605 0.000103 0.000626 0.037008 0.015569 0.999 2
length[20] 0.001661 0.000001 0.000177 0.003886 0.001410 0.999 2
length[21] 0.003275 0.000004 0.000651 0.007466 0.002934 0.998 2
length[22] 0.009687 0.000030 0.000160 0.018485 0.009842 1.020 2
length[23] 0.010064 0.000028 0.001403 0.018402 0.010176 1.225 2
length[24] 0.008511 0.000032 0.000057 0.019797 0.007958 1.083 2
length[25] 0.001487 0.000002 0.000048 0.003516 0.001074 1.011 2
length[26] 0.001524 0.000001 0.000111 0.003312 0.001316 1.001 2
length[27] 0.008354 0.000060 0.000248 0.024078 0.006263 1.173 2
length[28] 0.001346 0.000001 0.000055 0.003334 0.001091 0.998 2
length[29] 0.007620 0.000075 0.000058 0.027692 0.004605 1.008 2
length[30] 0.009314 0.000097 0.000259 0.029522 0.005824 1.219 2
length[31] 0.008069 0.000024 0.000831 0.016315 0.006107 1.029 2
length[32] 0.000930 0.000001 0.000047 0.002464 0.000711 0.994 2
--------------------------------------------------------------------------------------
+ Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman
and Rubin, 1992) should approach 1.0 as runs converge. NA is reported when
deviation of parameter values within all runs is 0 or when a parameter
value (a branch length, for instance) is not sampled in all runs.
Summary statistics for partitions with frequency >= 0.10 in at least one run:
Average standard deviation of split frequencies = 0.050891
Maximum standard deviation of split frequencies = 0.126168
Average PSRF for parameter values (excluding NA and >10.0) = 1.032
Maximum PSRF for parameter values = 1.225
Clade credibility values:
/---------------------------------------------------------- Kubrickus_heus_~ (1)
|
|---------------------------------------------------------- Kubrickus_ceus_~ (4)
|
| /--------------------------------------- Kubrickus_aus__~ (2)
| |
| | /---------- Kubrickus_deus_~ (5)
+ | /---95---+
| /---32---+ | \---------- Kubrickus_ieus_~ (9)
| | | /----39---+
| | | | \------------------- Kubrickus_feus_~ (7)
| | \----48---+
| | \----------------------------- Kubrickus_eus__~ (6)
\----84---+
| /----------------------------- Kubrickus_beus_~ (3)
| |
| | /---------- Kubrickus_geus_~ (8)
\--------44--------+ /---60---+
| | \---------- Kubrickus_leus~ (12)
\----84---+
| /---------- Kubrickus_jeus~ (10)
\---86---+
\---------- Kubrickus_keus~ (11)
Phylogram (based on average branch lengths):
/----------------------- Kubrickus_heus_~ (1)
|
|- Kubrickus_ceus_~ (4)
|
| /- Kubrickus_aus__~ (2)
| |
| | /-- Kubrickus_deus_~ (5)
+ | /-+
| /-------+ | \- Kubrickus_ieus_~ (9)
| | | /+
| | | |\--- Kubrickus_feus_~ (7)
| | \--------+
| | \- Kubrickus_eus__~ (6)
\------------------+
| /- Kubrickus_beus_~ (3)
| |
| | /- Kubrickus_geus_~ (8)
\------------+ /-+
| | \- Kubrickus_leus~ (12)
\--------------------+
| /--- Kubrickus_jeus~ (10)
\-+
\- Kubrickus_keus~ (11)
|-------| 0.010 expected changes per site
Calculating tree probabilities...
Credible sets of trees (402 trees sampled):
50 % credible set contains 41 trees
90 % credible set contains 252 trees
95 % credible set contains 327 trees
99 % credible set contains 387 trees
Exiting mrbayes block
Reached end of file
Tasks completed, exiting program because mode is noninteractive
To return control to the command line after completion of file processing,
set mode to interactive with 'mb -i <filename>' (i is for interactive)
or use 'set mode=interactive'
> setwd("..")
> tr <- tr[[1]] # we need the first tree
> tr <- root(tr, outgroup=OUT, resolve.root=TRUE)
> tr$node.label <- suppressWarnings(round(as.numeric(tr$node.label)*100)) # warning is OK so it is suppressed
> ## plot tree into PDF
> pdf(paste0("99_trees/", DATE, "_semistrict_mb_kubricks.pdf"), height=8, width=12) # change PDF size if needed
> oldpar <- par(mar=rep(0, 4))
> plot(tr)
> nodelabels(tr$node.label, frame="none", bg="transparent", adj=-0.1)
> mtext("semistrict MB, all compatible to 50% majority rule", font=2, line=-1)
> tmp <- legend("bottom", plot=FALSE, legend="") # this is how to get rid of overlapped scale bar
> add.scale.bar(x=tmp$text$x, y=tmp$text$y) # it is now centered
> dev.off()
null device
1
> ## also save it into Newick
> tr$node.label[tr$node.label == "NA"] <- "" # useful for some Newick reading software
> write.tree(tr, file=paste0("99_trees/", DATE, "_semistrict_mb_kubricks.tre"))