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BayesTraits Wrapper (btw)


btw is an R package for running the BayesTraits phylogenetic comparative methods software from R. The functions in btw run BayesTraits on your system, import and parse the output files in R, and delete the output files from your system.

There are two major advantages to using btw rather than running BayesTraits from the command line. First, btw makes it easy to incorporate BayesTraits into R scripts. Second, btw automatically parses BayesTraits output into ready-to-use formats for R analyses, greatly simplifying your workflow if you analyze your BayesTraits output in R.

BayesTraits was developed by Mark Pagel and Andrew Meade, and is available from their website as an executable file that can be run from a command line program such as Terminal (MacOS) or Command Prompt (Windows).

This is version 2 of btw, which I added in May 2018. There are three significant changes from version 1:

  1. Windows compatibility (formerly btw only worked on MacOS)
  2. Simplified structure for specifying BayesTraits commands
  3. It works with BayesTraitsV3

I recommend working with this version of btw and BayesTraitsV3 (which is the most recent version as of May 2018). If you want to work with version 1 of btw, visit the version 1 project page for a tutorial that demonstrations how to install and use version 1.


If you have devtools installed, get the most recent version of btw from GitHub like this:


Before using btw, navigate to the directory containing BayesTraitsV3 using the setwd command. On my Mac, BayesTraitsV3 is in a directory on my desktop, so I navigate there like this:


Double-check that you are in the right directory by typing list.files() in your R console. If you are in the right place, then BayesTraitsV3 will be one of the file names that prints to your R console.

All of the output files produced by btw and BayesTraits will reside in this directory. This is an important point, because any files existing in this directory can be overwritten if they have the same name as the files being produced by btw or BayesTraits.

btw comes with an example phylogeny and several data sets, which you can load into your R session after btw is loaded.


Type ls() into your R console to see the data files that were loaded. Some important formatting points about the data:

Now let’s run some analyses.

Fitting models

In this updated version of btw, you must list the desired BayesTraits commands in a character vector, in the correct order. The only exception is that you do not need to include the final “run” command. So, for example, to fit the Multistate model in maximum likelihood mode without any additional parameters, your vector of commands would simply be:

# commands to run multistate model ("1") in Maximum Likelihood mode ("1")
command_vec1 <- c("1", "1")

To run the analysis using the data files primate.discrete1 and primate.tree1, you would pass these files as the first and second argument to the bayestraits function, and then pass the command vector as the third argument.

results_1 <- bayestraits(primate.discrete1, primate.tree1, command_vec1)

You can include any valid BayesTraits commands in the command vector, but avoid commands that specify names for the output files, since the naming of output files is handled within the bayestraits function. If you specify file names for the BayesTraits output, it WILL screw things up because the bayestraits function won’t be able to find those files to scan the results into R.

There are two additional logical arguments that you can pass to bayestraits. First, the silent argument tells bayestraits whether to display output from BayesTraits in the R console - the default is silent = TRUE, which silences the output from BayesTraits. Second, the remove_files argument tells bayestraits whether to remove the output files from the working directory - the default is remove_files = TRUE, which results in all the files produced by btw and BayesTraits being deleted from disk after the results are scanned into R.

Now let’s dive into the output.

BayesTraits output

The result of running bayestraits is a named list, with each element containing the parsed output from a single BayesTraits output file. For the simple model we just ran, there is only one output file, the Log file. The parsed output from the log file can be pulled from the results list like this:

log_file_1 <- results_1$Log

The information from the log file is stored as a named list with two elements: 1) a block of information about the options specified for the model, and 2) a table of results. In maximum likelihood mode, there is one row of results for each tree in the tree file. In MCMC mode, there is one row for each sample from the posterior distribution. These elements can be accessed as with any named list:

model_options_1 <- log_file_1$options
model_results_1 <- log_file_1$results

If we run an MCMC analysis, we get an additional output file called the Schedule file, which is used for monitoring chain mixing. Like log files, the schedule file is stored as a named list in R and can be accessed by name.

# commands to run multistate model ("1") in MCMC mode ("2")
command_vec_2 <- c("1", "2")

# run analysis
results_2 <- bayestraits(primate.discrete1, primate.tree1, command_vec_2)

# extract log and schedule file results
log_2 <- results_2$Log
schedule_2 <- results_2$Schedule

The number of items in the schedule list will depend on the model settings.

There are several more types of output files produced by BayesTraitsV3. The AncStates file is unique to the geographic model implemented in BayesTraitsV3. We can fit a geographic model and access the AncStates output like this:

# commands to run geographic model ("13") in maximum likelihood mode
command_vec_3 <- c("13")

# run analysis
results_3 <- bayestraits(primate.continuous2, primate.tree100, command_vec_3)

# extract log, schedule, and AncStates file results
log_3 <- results_3$Log
schedule_3 <- results_3$Schedule
ancestors_3 <- results_3$AncStates

The next analysis introduces the Stones file, which is produced when the stepping stone sampler is used.

# commands to run Multistate model ("1") in MCMC mode ("2") with stepping stone sampler
command_vec_4 <- c("1", "2", "Stones 100 1000")

# run analysis
results_4 <- bayestraits(primate.discrete1, primate.tree1, command_vec_4)

# extract log, schedule, and stones file results
log_4 <- results_4$Log
schedule_4 <- results_4$Schedule
stones_4 <- results_4$Stones

Finally, the OutputTrees file is produced when the SaveTrees command is used. This is just a nexus file.

# commands to run continuous random walk trait model ("4") in MCMC mode ("2"), estimating lambda 
# and saving initial trees, transformed trees
command_vec_5 <- c("4", "2", "lambda", "SaveTrees")

# run analysis
results_5 <- bayestraits(primate.continuous1, primate.tree100, command_vec_5)

# extract log, schedule, and output trees
log_5 <- results_5$Log
schedule_5 <- results_5$Schedule
output_trees_5 <- results_5$OutputTrees

It is also possible to tell BayesTraits to save the initial sample of trees using the SaveInitialTrees command, and this requires you to specify a file name for the saved trees. You can tell BayesTraits to do this in the command vector, but btw won’t handle the saved initial trees in any way - they will just be saved in your working directory, and if you wish to bring them into R, you can do it using

Parsing existing BayesTraits output

In case you have some BayesTraits output that you want to read into R without actually running bayestraits, I have provided several parsing functions that can be used for this purpose. Previously the code for these functions was embedded inside the functions for running BayesTraits so you couldn’t use them independently. There are four parsing functions:

  1. parse_log - parses a BayesTraits log file
  2. parse_schedule - parses a BayesTraits schedule file
  3. parse_stones - parses a BayesTraits stones file
  4. parse_ancstates - parses a BayesTraits AncStates file

Each function takes a file path as input and produces output identical to the output produced by bayestraits, except for a single output file rather than all of the output files from an analysis. There are no special functions for importing the tree files because you can just use from the ape package.

Other btw functions

There are a few more functions included in btw. An important one is killbt, which simply kills all BayesTraits processes running on your system. If you accidentally start a long BayesTraits processes and realize that you made a mistake, you have to kill that process otherwise it will keep running in the background and can really bog down your system. You can execute this function by hitting ESC or Ctrl-z to escape the function within R and typing killbt() in the R console. Sometimes you can’t escape the function without restarting your R session. That’s fine, but keep in mind that the process can keep running after R shuts down, so the first thing you will want to do when you restart R is run killbt().

Another couple of handy shortcuts are lrtest and bftest, which perform likelihood ratio and Bayes Factor tests respectively. Each function takes a pair of model results as arguments, e.g., lrtest(model_results_1, model_results_2). It isn’t difficult to extract the relevant quantities from the model results and perform the tests on your own, but these functions can save you some typing.

Trouble shooting

Before messaging me, check that you are specifying all of the BayesTraits parameters correctly. Go to your command line program (e.g., Terminal on a Mac, Command Prompt in Windows) and make sure you can run BayesTraits using the commands exactly as you have specified them in your command vector.

If you are specifying the commands correctly and bayestraits is still not working properly, then there may be a problem with the parsing functions. You can investigate this by running BayesTraits from the command line and trying to use the parsing functions to pull each output file into R. If one of these fails, then you know the problem is with that function.


There is one feature of BayesTraitsV3 that btw does not support: the variable rates model. I don’t think this is a great loss because I’m skeptical about the model - I think it produces vastly overparameterized models (see Ho & Ané 2014 for an excellent discussion of this issue). On a practical note, the output of this model is a nightmare and I had a hard time understanding it. Pagel & Meade, perhaps recognizing this, created a web tool to process the output from their variable rates model, so if you want to run their variable rates model you can use their web tool.

If you use btw and you notice any ways in which it can be improved, please let me know in a comment or e-mail (comments are preferred since it can be useful for others to see your comment and my response). Feedback from users was a big part of why I produced this update - it let me know that my code is being used and gave me specific ideas for how to improve it!

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