Constant rate birth (CRB) model

library(treepplr)
library(ape)
library(ggplot2)
library(magrittr)

Data

We will use an example (random) tree that comes with the package.

tree <- ape::read.tree(system.file(
  "extdata/crb_tree_15_tips.tre", package = "treepplr"))
ape::plot.phylo(tree, cex = 0.5)

We need to convert the tree to a TreePPL readable format and read the CRB model.

data <- tp_phylo_2_json(tree)
model <- tp_model(system.file("extdata/crb.tppl", package = "treepplr"))

Run treeppl

Compile and run the TreePPL program with standard inference settings.

output_list <- tp_treeppl(model = model, data = data)

Plot posterior

TreePPL outputs the log weight of each sample, so first we need to get the normalized weights and then we can plot the posterior distribution produced.

# turn list into a data frame where each row represents one sample 
# and calculate normalized weights from log weights
output <-  tp_parse(output_list) %>% 
  dplyr::mutate(weight = exp(log_weight - max(.$log_weight)))

ggplot2::ggplot(output) +
  ggplot2::geom_histogram(aes(samples, y = after_stat(density), weight=weight), 
                          col = "white", fill = "lightblue") +
  ggplot2::geom_density(aes(samples, weight=weight)) +
  ggplot2::theme_bw()