Logging screen output to file "mrbayeslog.out" Successfully set likelihood model parameters Setting Nucmodel to 4by4 Set state frequency prior to default Successfully set likelihood model parameters Setting Nst to 1 Successfully set likelihood model parameters Setting Rates to Equal Successfully set likelihood model parameters Successfully set likelihood model parameters Setting Brlenspr to Unconstrained:Exponential(10.00) Successfully set prior model parameters Setting Tratiopr to Beta(1.00,1.00) Successfully set prior model parameters Setting Covswitchpr to Uniform(0.00,100.00) Successfully set prior model parameters Setting Statefreqpr to Dirichlet(0.25,0.25,0.25,0.25) Successfully set prior model parameters Setting Ratecorrpr to Uniform(-1.00,1.00) Successfully set prior model parameters Setting Ratepr to Fixed Successfully set prior model parameters Setting Pinvarpr to Uniform(0.00,1.00) Successfully set prior model parameters Setting Revmatpr to Dirichlet(1.00,1.00,1.00,1.00,1.00,1.00) Successfully set prior model parameters Setting reversible rate matrix (revmat) format to Dirichlet Not reporting site rates Setting number of generations to 1000 Setting chain seed to 1116355510 Setting swapseed to 1116355510 Setting number of runs to 2 Setting number of chains to 4 Setting heating parameter to 0.200000 Setting swap frequency to 1 Setting number of swaps per swapping cycle to 1 Setting sample frequency to 1000 Calculating MCMC diagnostics Setting minimum partition frequency to 0.10 Calculating MCMC diagnostics only for cold chain(s) Using relative burnin (a fraction of samples discarded). Setting chain burn-in to 0 Setting burnin fraction to 0.25 Using stopping rule. Setting starting tree to "Random" Setting burnin fraction to 0.25 Setting program to save branch length information Setting number of perturbations to start tree to 0 Setting ordertaxa to yes Setting chain output file names to "infile.nex..p/run.t>" Running Markov chain MCMC stamp = 2848481462 Seed = 1116355510 Swapseed = 1116355510 Model settings: Datatype = DNA Nucmodel = 4by4 Nst = 1 Covarion = No # States = 4 State frequencies have a Dirichlet prior (0.25,0.25,0.25,0.25) Rates = Equal Active parameters: Parameters ------------------ Statefreq 1 Topology 2 Brlens 3 ------------------ 1 -- Parameter = Statefreq Prior = Dirichlet 2 -- Parameter = Topology Prior = All topologies equally probable a priori 3 -- Parameter = Brlens Prior = Branch lengths are Unconstrained:Exponential(10.0) Number of taxa = 10 Number of characters = 759 Compressing data matrix for division 1 Division 1 has 288 unique site patterns The MCMC sampler will use the following moves: With prob. Chain will change 4.76 % param. 1 (state frequencies) with Dirichlet proposal 71.43 % param. 2 (topology and branch lengths) with extending TBR 23.81 % param. 2 (topology and branch lengths) with LOCAL Creating parsimony (bitset) matrix for division 1 Initializing conditional likelihoods for terminals Initializing conditional likelihoods for internal nodes Initial log likelihoods for run 1: Chain 1 -- -4790.665936 Chain 2 -- -4799.135886 Chain 3 -- -4770.542048 Chain 4 -- -4725.333974 Initial log likelihoods for run 2: Chain 1 -- -4742.855326 Chain 2 -- -4795.746089 Chain 3 -- -4803.929640 Chain 4 -- -4738.425756 Chain results: 1 -- [-4780.067] (-4799.136) (-4770.542) (-4725.334) * [-4742.855] (-4717.210) (-4803.930) (-4738.426) 100 -- (-4441.640) (-4413.733) (-4422.381) [-4366.313] * (-4442.191) (-4420.426) (-4404.473) [-4395.320] -- 0:00:00 200 -- (-4309.028) (-4323.935) (-4317.836) [-4279.397] * (-4332.404) (-4289.973) (-4307.985) [-4295.373] -- 0:00:00 300 -- [-4248.256] (-4286.016) (-4289.760) (-4255.176) * (-4280.278) [-4251.920] (-4300.010) (-4277.520) -- 0:00:00 400 -- (-4249.189) (-4285.116) (-4275.015) [-4253.844] * (-4260.130) [-4240.090] (-4279.935) (-4248.996) -- 0:00:00 500 -- [-4245.392] (-4257.756) (-4271.017) (-4252.471) * (-4257.952) (-4241.523) (-4258.164) [-4241.470] -- 0:00:00 600 -- [-4242.349] (-4253.919) (-4271.850) (-4248.079) * (-4259.529) (-4241.337) (-4245.642) [-4243.116] -- 0:00:00 700 -- [-4247.610] (-4243.062) (-4273.169) (-4248.496) * (-4246.952) (-4242.047) (-4243.750) [-4238.069] -- 0:00:00 800 -- (-4244.575) [-4240.880] (-4244.607) (-4235.920) * (-4243.888) [-4242.134] (-4244.224) (-4233.783) -- 0:00:00 900 -- (-4238.914) [-4244.131] (-4249.795) (-4233.601) * (-4243.913) (-4241.928) (-4254.256) [-4233.804] -- 0:00:00 1000 -- (-4235.971) (-4238.356) (-4244.632) [-4238.559] * (-4239.431) (-4239.850) (-4243.102) [-4232.398] -- 0:00:00 Average standard deviation of split frequencies: 0.235702 Analysis completed in 1 second Analysis used 1.13 seconds of CPU time Likelihood of best state for "cold" chain of run 1 was -4232.88 Likelihood of best state for "cold" chain of run 2 was -4232.88 Acceptance rates for the moves in the "cold" chain of run 1: With prob. Chain accepted changes to 21.43 % param. 1 (state frequencies) with Dirichlet proposal 11.90 % param. 2 (topology and branch lengths) with extending TBR 18.91 % param. 2 (topology and branch lengths) with LOCAL Acceptance rates for the moves in the "cold" chain of run 2: With prob. Chain accepted changes to 24.53 % param. 1 (state frequencies) with Dirichlet proposal 12.48 % param. 2 (topology and branch lengths) with extending TBR 18.14 % param. 2 (topology and branch lengths) with LOCAL Chain swap information for run 1: 1 2 3 4 -------------------------- 1 | 0.42 0.22 0.05 2 | 169 0.38 0.17 3 | 145 177 0.55 4 | 158 177 174 Chain swap information for run 2: 1 2 3 4 -------------------------- 1 | 0.41 0.18 0.05 2 | 156 0.59 0.28 3 | 171 184 0.54 4 | 170 149 170 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) ************************* WARNING!! ************************************ MrBayes suspects that your runs have not converged because the tree samples are very different (average standard deviation of split frequen- cies larger than 0.10 (0.24)). MrBayes suggests that you run the ana- lysis longer or try to improve the MCMC sampling efficiency by fine- tuning MCMC proposal or heating parameters. Setting sump burnin to 0 Setting sump nruns to 1 Setting sump to print output to file Setting sump output file name to "sumpoutput.out" Setting sump to print likelihood generation plot Setting sump to print marginal likelihood estimates Setting sump to print parameter table Summarizing parameters in file infile.nex.p Writing output to file sumpoutput.out Could not open file "infile.nex.p" Make sure that 'Nruns' is set correctly Error in command "Sump" Setting sumt burnin to 0 Setting sumt nruns to 2 Setting sumt ntrees to 1 Showing partitions with probability greater than or equal to 0.050000 Setting sumt contype to Halfcompat Summarizing trees in files "infile.nex.run1.t" and "infile.nex.run2.t" UNIX line termination Examining first file ... Found one tree block in file "infile.nex.run1.t" with 2 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 4 trees in 2 files (sampling 4 of them) (Each file contained 2 trees of which 2 were sampled) General explanation: A taxon bibartition 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 "*". The output includes the bipartition number (ID; sorted from highest to lowest probability), bipartition (e.g., ...**..), number of times the bipartition was observed (#obs), the posterior probabil- ity of the bipartition, and, if branch lengths were recorded on the trees in the file, the average (Mean(v)) and variance (Var(v)) of the lengths. Each "." or "*" in the bipartition represents a taxon that is to the left or right of the removed branch. A list of the taxa in the bipartition is given before the list of bipartitions. If you summarize several independent analy- ses, convergence diagnostics are presented for both the posterior probabil- ities of bipartitions (bipartition or split frequencies) and branch lengths (if recorded on the trees in the files). In the former case, the diagnostic is the standard deviation of the partition frequencies (Stdev(s)), in the second case it is the potential scale reduction factor (PSRF) of Gelman and Rubin (1992). Stdev(s) is expected to approach 0 and PSRF is expected to approach 1 as runs converge onto the posterior probability distribution. Note that these values 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 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 the phylogenetic context. List of taxa in bipartitions: 1 -- t0 2 -- t1 3 -- t2 4 -- t3 5 -- t4 6 -- t5 7 -- t6 8 -- t7 9 -- t8 10 -- t9 Summary statistics for taxon bipartitions: ID -- Partition #obs Probab. Stdev(s) Mean(v) Var(v) PSRF Nruns ---------------------------------------------------------------------------- 1 -- ........*. 4 1.000000 0.000000 0.068863 0.001304 0.712 2 2 -- .*........ 4 1.000000 0.000000 0.107209 0.000080 0.780 2 3 -- ......*... 4 1.000000 0.000000 0.070601 0.001242 0.746 2 4 -- .********* 4 1.000000 0.000000 0.124110 0.001066 0.859 2 5 -- ..*....... 4 1.000000 0.000000 0.092375 0.000080 0.722 2 6 -- .......*.. 4 1.000000 0.000000 0.067252 0.001433 0.708 2 7 -- .....*.... 4 1.000000 0.000000 0.076583 0.000731 0.707 2 8 -- ...*...... 4 1.000000 0.000000 0.096361 0.000043 1.061 2 9 -- ....*..... 4 1.000000 0.000000 0.108114 0.000511 0.789 2 10 -- .........* 4 1.000000 0.000000 0.148513 0.003575 0.773 2 11 -- ...*...**. 2 0.500000 0.000000 0.008119 0.000035 > 2.0 2 12 -- ...*....*. 2 0.500000 0.000000 0.017633 0.000038 > 2.0 2 13 -- .***.****. 2 0.500000 0.000000 0.030221 0.000069 > 2.0 2 14 -- .********. 2 0.500000 0.000000 0.037420 0.000020 > 2.0 2 15 -- ..*..*.... 2 0.500000 0.000000 0.021331 0.000018 > 2.0 2 16 -- .*.*...**. 2 0.500000 0.000000 0.017212 0.000010 > 2.0 2 17 -- .*.*..***. 2 0.500000 0.000000 0.013689 0.000014 > 2.0 2 18 -- ..*..*..*. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 19 -- ..*.....*. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 20 -- .*.....*.. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 21 -- ..*.**..*. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 22 -- .**.****** 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 23 -- .**.*****. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 24 -- ..*.***.*. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 25 -- .*......*. 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 26 -- .*.****.** 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 27 -- .*.******* 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 28 -- ...*.*.... 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * 29 -- ....*.*... 1 0.250000 0.353553 0.095818 0.000000 > 2.0 1 * 30 -- ...****..* 1 0.250000 0.353553 0.098034 0.000000 > 2.0 1 * 31 -- ...*.*...* 1 0.250000 0.353553 0.100000 0.000000 > 2.0 1 * ---------------------------------------------------------------------------- * The partition was not found in all runs so the values are unreliable Clade credibility values: /----------------------------------------------------------------------- t0 (1) | |----------------------------------------------------------------------- t9 (10) | | /------------------------------ t1 (2) | | | | /---------- t3 (4) | /----50----+ /----50---+ + | | | \---------- t8 (9) | | \----50---+ | /----50---+ \-------------------- t7 (8) | | | | | \----------------------------------------- t6 (7) | /----50---+ | | | /---------- t2 (3) | | \-------------------50-------------------+ \----50---+ \---------- t5 (6) | \------------------------------------------------------------- t4 (5) Phylogram: /---------------------------------------- t0 (1) | |------------------------------------------------ t9 (10) | | /----------------------------------- t1 (2) | | | | /------------------------------- t3 (4) | /----+ /-----+ + | | | \---------------------- t8 (9) | | \--+ | /----+ \---------------------- t7 (8) | | | | | \----------------------- t6 (7) | /---------+ | | | /------------------------------ t2 (3) | | \------+ \-----------+ \------------------------- t5 (6) | \----------------------------------- t4 (5) |----------------| 0.050 expected changes per site Calculating tree probabilities... Credible sets of trees (3 trees sampled): 90 % credible set contains 3 trees 95 % credible set contains 3 trees 99 % credible set contains 3 trees Deleting matrix