Model selection of partition models

Comparing relative model fit with Bayes factors

Mike May and Sebastian Höhna

Last modified on August 6, 2018

Overview


You should read first the General Introduction to Model selection tutorial, which explains the theory and standard algorithms for estimating marginal likelihoods and Bayes factors. Additionally, you may want to work through the Model selection of common substitution models for one locus tutorial, which estimates marginal likelihoods for different substitution models for one locus, before attempting this tutorial.

Comparing Partitioned Models Using Bayes Factors


For this tutorial you should download the sequence data, ITS, matK, and rbcL. These new sequence data are for the genus Fagus, the beeches.

Data partitions allow us to apply different substitution models to different loci in order to accommodate process heterogeneity (variation in the substitution process among sequences). The substitution models may be of the same form (i.e., they may all be GTR models), or of entirely different forms (i.e, some may be HKY, while others are GTR). Just because two loci have the same form of substitution model does not necessarily mean they share the same substitution models; for example, we determined that the GTR model is preferred for each of the loci above, but it is possible that the stationary frequencies and relative rate parameters for these loci are different (i.e., they have different substitution models).

According to our previous analysis, we could partition our Fagus data so that each locus has the same or different substitution model parameters. Each of these choices imply different phylogenetic models, and thus we can choose among partitioned models using Bayes factors.

The Uniform Partitioned Model


If we assigned the same GTR+G to each locus, we would be assuming that the process of evolution is the same among loci (we often call this the “uniform model”). We can specify this uniform partition model by using the same $Q$ matrix and ASRV model for each alignment. Open the file marginal_likelihood_partition_1.Rev and examine how we specify this model. In particular, note that dnPhyloCTMC models all use the same Q matrix and site_rates:

seq_ITS ~ dnPhyloCTMC(tree=phylogeny, Q=Q, type="DNA", siteRates=site_rates)
seq_ITS.clamp(data_ITS) # attach the observed data

seq_matK ~ dnPhyloCTMC(tree=phylogeny, Q=Q, type="DNA", siteRates=site_rates)
seq_matK.clamp(data_matK) # attach the observed data

seq_rbcL ~ dnPhyloCTMC(tree=phylogeny, Q=Q, type="DNA", siteRates=site_rates)
seq_rbcL.clamp(data_rbcL) # attach the observed data

The Saturated Model


At the opposite end of the spectrum, the “saturated” model applies a different substitution model to each locus, and each locus receives its own subset-specific rate multiplier (with the contraint that the mean rate is 1!). Open the script marginal_likelihood_partition_5.Rev to see how this model is specified. Notice how the subset-specific rates are specified:

num_sites[1] = data_ITS.nchar()
num_sites[2] = data_matK.nchar()
num_sites[3] = data_rbcL.nchar()

relative_rates ~ dnDirichlet(v(1,1,1))
moves[mvi++] = mvBetaSimplex(relative_rates, weight=1.0)

subset_rates := relative_rates * sum(num_sites) / num_sites

(The last line forces the subset_rates to have a mean of 1.)

Notice also that each dnPhyloCTMC is receiving a different Q, subset_rates site_rates argument:

seq_ITS ~ dnPhyloCTMC(tree=phylogeny, branchRates=subset_rates[1],
                      Q=Q_ITS, type="DNA", siteRates=site_rates_ITS)
seq_ITS.clamp(data_ITS) # attach the observed data

seq_matK ~ dnPhyloCTMC(tree=phylogeny, branchRates=subset_rates[2],
                       Q=Q_matK, type="DNA", siteRates=site_rates_matK)
seq_matK.clamp(data_matK) # attach the observed data

seq_rbcL ~ dnPhyloCTMC(tree=phylogeny, branchRates=subset_rates[3],
                       Q=Q_rbcL, type="DNA", siteRates=site_rates_rbcL)
seq_rbcL.clamp(data_rbcL) # attach the observed data

In-Class Exercises


  1. Download the Rev script associated with your assigned partition model. Note how this script implements the particular partition model and the power posterior analysis.
  2. Execute your Rev script. This can take a long time; please be patient! Once your stepping-stone analysis is complete, record your result on the Google spreadsheet.