Author: Richèl J.C. Bilderbeek
Date: 2024-06-24

# babette demo

#### Richèl J.C. Bilderbeek

#### 2024-06-24

## Introduction

This vignette briefly demonstrates multiple features of `babette`, without going into much detail.

First, load the library:

    library(babette)

This vignette shows how to:

- Let `babette` run ‘BEAST2’
- Plot the posterior estimates
- Show the effective sample sizes (ESS)
- Show the summary statistics
- Plot the posterior phylogenies

In all cases, this is done for a short MCMC chain length of 10K:

    inference_model <- create_test_inference_model()

Also, in all cases, we use the same BEAST2 options:

    beast2_options <- create_beast2_options(verbose = TRUE)

## Let `babette` run ‘BEAST2’

For an alignment, we’ll use a `babette` example alignment.

    if (is_beast2_installed()) {
      out <- bbt_run_from_model(
        fasta_filename = get_babette_path("anthus_aco_sub.fas"),
        inference_model = inference_model,
        beast2_options = beast2_options
      )
      bbt_delete_temp_files(
        inference_model = inference_model,
        beast2_options = beast2_options
      )
    }

## Plot the posterior estimates

    if (is_beast2_installed()) {
      library(ggplot2)
      p <- ggplot(
        data = out$estimates,
        aes(x = Sample)
      )
      p + geom_line(aes(y = TreeHeight), color = "green")
      p + geom_line(aes(y = YuleModel), color = "red")
      p + geom_line(aes(y = birthRate), color = "blue")
    }

## Show the effective sample sizes (ESS)

Effective sample sizes, with 20% burn-in removed:

    if (is_beast2_installed()) {
      traces <- remove_burn_ins(
        traces = out$estimates,
        burn_in_fraction = 0.2
      )
      esses <- t(
        calc_esses(
          traces,
          sample_interval = inference_model$mcmc$tracelog$log_every
        )
      )
      colnames(esses) <- "ESS"
      knitr::kable(esses)
    }

For a reliable inference, use an ESS of at least 200.

## Show the summary statistics

    if (is_beast2_installed()) {
      sum_stats <- t(
        calc_summary_stats(
          traces$posterior,
          sample_interval = inference_model$mcmc$tracelog$log_every
        )
      )
      colnames(sum_stats) <- "Statistic"
      knitr::kable(sum_stats)
    }

## Plot the posterior phylogenies

    if (is_beast2_installed()) {
      plot_densitree(out$anthus_aco_sub_trees, width = 2)
    }