| 1 | MrBayes |
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| 2 | |
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| 3 | DESCRIPTION |
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| 4 | MrBayes is a program for Bayesian inference and model choice across |
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| 5 | a wide range of phylogenetic and evolutionary models. MrBayes uses |
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| 6 | Markov chain Monte Carlo (MCMC) methods to estimate the posterior |
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| 7 | distribution of model parameters. |
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| 8 | |
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| 9 | The desciptions in this manual where copied from the official |
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| 10 | MrBayes manual at LINK{http://mrbayes.sourceforge.net/manual.php} |
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| 11 | |
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| 12 | |
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| 13 | PARAMETERS |
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| 14 | |
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| 15 | Number of substitution types |
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| 16 | |
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| 17 | Sets the number of substitution types: |
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| 18 | |
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| 19 | - "1" constrains all of the rates to be the same (e.g., a JC69 or F81 model); |
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| 20 | - "2" allows transitions and transversions to have potentially different rates |
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| 21 | (e.g., a K80 or HKY85 model); |
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| 22 | - "6" allows all rates to be different, subject to the constraint of |
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| 23 | time-reversibility (e.g., a GTR model). |
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| 24 | - Finally, 'nst' can be set to 'mixed', which results in the Markov |
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| 25 | chain sampling over the space of all possible reversible |
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| 26 | substitution models, including the GTR model and all models that |
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| 27 | can be derived from it model by grouping the six rates in various |
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| 28 | combinations. This includes all the named models above and a large |
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| 29 | number of others, with or without name. |
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| 30 | |
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| 31 | Model for among-site rate variation |
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| 32 | |
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| 33 | Sets the model for among-site rate variation. |
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| 34 | In general, the rate at a site is considered to be an unknown random |
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| 35 | variable. The valid options are: |
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| 36 | * No rate variation across sites. |
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| 37 | * Gamma-distributed rates across sites. The rate |
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| 38 | at a site is drawn from a gamma distribution. |
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| 39 | The gamma distribution has a single parameter that |
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| 40 | describes how much rates vary. |
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| 41 | * Autocorrelated rates across sites. The marginal rate distribution |
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| 42 | is gamma, but adjacent sites have correlated rates. |
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| 43 | * A proportion of the sites are invariable. |
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| 44 | * A proportion of the sites are invariable while the rate for |
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| 45 | the remaining sites are drawn from a gamma distribution. |
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| 46 | |
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| 47 | Number of rate categories for the gamma distribution |
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| 48 | |
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| 49 | Sets the number of rate categories for the gamma distribution. |
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| 50 | The gamma distribution is continuous. However, it is virtually |
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| 51 | impossible to calculate likelihoods under the continuous gamma |
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| 52 | distribution. Hence, an approximation to the continuous gamma is used; |
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| 53 | the gamma distribution is broken into ncat categories of equal |
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| 54 | weight (1/ncat). The mean rate for each category represents the |
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| 55 | rate for the entire cateogry. This option allows you to specify |
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| 56 | how many rate categories to use when approximating the gamma. |
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| 57 | The approximation is better as ncat is increased. In practice, |
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| 58 | "ncat=4" does a reasonable job of approximating the continuous gamma. |
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| 59 | |
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| 60 | Number of cycles for the MCMC algorithm |
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| 61 | |
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| 62 | This option sets the number of cycles for the MCMC algorithm. |
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| 63 | This should be a big number as you want the chain to first reach |
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| 64 | stationarity, and then remain there for enough time to take lots of samples. |
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| 65 | |
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| 66 | NOTE: the standalone version of MrBayes asks if you want to continue |
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| 67 | the calculation after the number of cycles has been reached. |
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| 68 | This does NOT happen when using the ARB version. If the number |
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| 69 | of cycles has been reached the algorithm will terminate! |
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| 70 | |
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| 71 | Number of chains |
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| 72 | |
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| 73 | How many chains are run for each analysis for the MCMCMC variant. |
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| 74 | The default is 4: 1 cold chain and 3 heated chains. |
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| 75 | If Nchains is set to 1, MrBayes will use regular MCMC sampling, without heating. |
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| 76 | |
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| 77 | Temperature parameter for heating the chains |
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| 78 | |
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| 79 | The temperature parameter for heating the chains. The higher the |
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| 80 | temperature, the more likely the heated chains are to move between |
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| 81 | isolated peaks in the posterior distribution. However, excessive |
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| 82 | heating may lead to very low acceptance rates for swaps between |
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| 83 | different chains. Before changing the default setting, however, |
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| 84 | note that the acceptance rates of swaps tend to fluctuate during |
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| 85 | the burn-in phase of the run. |
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| 86 | |
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| 87 | Markov chain sample frequency |
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| 88 | |
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| 89 | This specifies how often the Markov chain is sampled. You can |
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| 90 | sample the chain every cycle, but this results in very large |
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| 91 | output files. Thinning the chain is a way of making these files |
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| 92 | smaller and making the samples more independent. |
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| 93 | |
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| 94 | Fraction of samples that will be discarded |
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| 95 | |
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| 96 | Determines the fraction of samples that will be discarded when |
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| 97 | convergence diagnostics are calculated. The value of this option |
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| 98 | is only relevant when Relburnin is set to YES. Example: A value |
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| 99 | for this option of 0.25 means that 25% of the samples will be discarded. |
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| 100 | |
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| 101 | |
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| 102 | |
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| 103 | |
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| 104 | |
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| 105 | LICENSE |
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| 106 | GNU GENERAL PUBLIC LICENSE |
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| 107 | Version 2, June 1991 |
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