MrBayes

Sets the number of substitution types:

• "1" constrains all of the rates to be the same (e.g., a JC69 or F81 model);
• "2" allows transitions and transversions to have potentially different rates (e.g., a K80 or HKY85 model);
• "6" allows all rates to be different, subject to the constraint of time-reversibility (e.g., a GTR model).
• Finally, 'nst' can be set to 'mixed', which results in the Markov chain sampling over the space of all possible reversible substitution models, including the GTR model and all models that can be derived from it model by grouping the six rates in various combinations. This includes all the named models above and a large number of others, with or without name.

Sets the model for among-site rate variation. In general, the rate at a site is considered to be an unknown random variable. The valid options are:

• No rate variation across sites.
• Gamma-distributed rates across sites. The rate at a site is drawn from a gamma distribution. The gamma distribution has a single parameter that describes how much rates vary.
• Autocorrelated rates across sites. The marginal rate distribution is gamma, but adjacent sites have correlated rates.
• A proportion of the sites are invariable.
• Mixed invariable/gamma: A proportion of the sites are invariable while the rates for the remaining sites are drawn from a gamma distribution.

Sets the number of rate categories for the gamma distribution. The gamma distribution is continuous. However, it is virtually impossible to calculate likelihoods under the continuous gamma distribution. Hence, an approximation to the continuous gamma is used; the gamma distribution is broken into ncat categories of equal weight (1/ncat). The mean rate for each category represents the rate for the entire cateogry. This option allows you to specify how many rate categories to use when approximating the gamma. The approximation is better as ncat is increased. In practice, "ncat=4" does a reasonable job of approximating the continuous gamma.

This option sets the number of cycles for the MCMC algorithm. This should be a big number as you want the chain to first reach stationarity, and then remain there for enough time to take lots of samples.

NOTE: the standalone version of MrBayes asks if you want to continue
      the calculation after the number of cycles has been reached.
      This does NOT happen when using the ARB version. If the number
      of cycles has been reached the algorithm will terminate!

How many chains are run for each analysis for the MCMCMC variant. The default is 4: 1 cold chain and 3 heated chains. If Nchains is set to 1, MrBayes will use regular MCMC sampling, without heating.

The temperature parameter for heating the chains. The higher the temperature, the more likely the heated chains are to move between isolated peaks in the posterior distribution. However, excessive heating may lead to very low acceptance rates for swaps between different chains. Before changing the default setting, however, note that the acceptance rates of swaps tend to fluctuate during the burn-in phase of the run.

This specifies how often the Markov chain is sampled. You can sample the chain every cycle, but this results in very large output files. Thinning the chain is a way of making these files smaller and making the samples more independent.

Determines the fraction of samples that will be discarded when convergence diagnostics are calculated. The value of this option is only relevant when Relburnin is set to YES. Example: A value for this option of 0.25 means that 25% of the samples will be discarded.