Quick Start with bifrost

In bifrost: Branch-Level Inference Framework for Recognizing Optimal Shifts in Traits

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

Overview

bifrost performs branch-level inference of multi-regime, multivariate trait evolution on a phylogeny using penalized-likelihood multivariate GLS fits. The current implementation searches for evolutionary model shifts under a multi-rate Brownian Motion (BMM) model with proportional regime variance-covariance (VCV) scaling. It operates directly in trait space, works with fossil tip-dated trees, and is designed for high-dimensional datasets (p > n) and large trees (> 1000 tips).

If you have not installed the package yet, see the installation instructions on the package README.

In practical terms, bifrost asks:

• Where on the tree are shifts supported?
• When do those shifts occur?
• How does covariance and rate structure differ across inferred regimes?

Minimal worked example

The smallest end-to-end workflow is a simulated single-regime example. Running it is mainly a quick way to confirm that bifrost and its dependencies are installed correctly and that the core search routine runs on your machine. In this case, we typically expect bifrost to recover no shifts.

library(bifrost)
library(ape)
library(phytools)
library(mvMORPH)

set.seed(1)

# Simulate a tree
tr <- pbtree(n = 50, scale = 1)

# Simulate multivariate traits under a single-regime BM1 model (no shifts)
sigma <- diag(0.1, 2)  # 2 x 2 variance-covariance matrix for two traits
theta <- c(0, 0)       # ancestral means for the two traits

sim <- mvSIM(
  tree  = tr,
  nsim  = 1,
  model = "BM1",
  param = list(
    ntraits = 2,
    sigma   = sigma,
    theta   = theta
  )
)

# mvSIM returns either a matrix or a list of matrices depending on mvMORPH version
X <- if (is.list(sim)) sim[[1]] else sim
rownames(X) <- tr$tip.label

# Run bifrost's greedy search for shifts
res <- searchOptimalConfiguration(
  baseline_tree              = tr,
  trait_data                 = X,
  formula                    = "trait_data ~ 1",
  min_descendant_tips        = 10,
  num_cores                  = 1,
  shift_acceptance_threshold = 20,  # conservative GIC threshold
  IC                         = "GIC",
  plot                       = FALSE,
  store_model_fit_history    = FALSE,
  verbose                    = FALSE
)

# For this single-regime BM1 simulation, we typically expect no inferred shifts
res$shift_nodes_no_uncertainty
res$optimal_ic - res$baseline_ic
str(res$VCVs)

What to expect from this example:

• res$shift_nodes_no_uncertainty is typically integer(0),
• res$optimal_ic - res$baseline_ic is typically close to 0,
• res$VCVs contains a single regime-specific VCV for the baseline regime "0".

Before you run on real data

At minimum, provide:

1. A rooted phylogeny with branch lengths interpreted in units of time.
2. A multivariate trait matrix or data frame with taxa as row names.

Important checks before fitting:

• Tree and data alignment. rownames(trait_data) must match tree$tip.label exactly, including both names and order.
• Branch lengths. Branch lengths are interpreted in units of time; ultrametricity is not required.
• Starting tree format. The input can be a rooted phylo tree; it does not need to already be a simmap object. Internally, bifrost paints a single baseline regime "0" and stores inferred regimes using SIMMAP conventions.
• Multi-dimensional traits. bifrost works directly in trait space. For high-dimensional settings, tune mvMORPH::mvgls arguments such as method, penalty, target, and error to match your data structure.
• Formulas. The default formula = "trait_data ~ 1" fits an intercept-only multivariate model, but more general formulas are also supported when needed.

The arguments you will tune most often are:

• min_descendant_tips: minimum clade size required for a candidate shift.
• shift_acceptance_threshold: minimum IC improvement required to accept a shift. Larger values yield more conservative models.
• IC: model comparison metric, either "GIC" or "BIC".
• num_cores: number of workers used for candidate scoring.
• formula: model formula passed to mvgls.
• method, penalty, target, error, and related mvgls arguments passed through ....
• uncertaintyweights or uncertaintyweights_par: request serial or parallel per-shift IC support calculations.
• store_model_fit_history: retain the search path for later inspection and plotting.
• plot: draw or update SIMMAP plots during the search.
• verbose: emit progress messages during fitting.

Practical starting points:

• use larger min_descendant_tips values to avoid tiny-clade shifts,
• start with a conservative shift_acceptance_threshold for exploratory work,
• compare "GIC" and "BIC" on the same dataset when model-selection sensitivity matters,
• perform sensitivity analyses rather than relying on a single run.

The ic_uncertainty_threshold argument is currently reserved for future pruning and uncertainty workflows and can typically be left at its default.

How the search works

searchOptimalConfiguration() performs a greedy, step-wise search:

1. Fit a baseline single-regime model.
2. Generate one-shift candidate trees for internal nodes satisfying min_descendant_tips.
3. Score candidate models, optionally in parallel.
4. Iteratively accept shifts that improve the chosen information criterion by at least shift_acceptance_threshold.
5. Optionally compute per-shift IC support by removing each accepted shift in turn and refitting.

The accepted shift set defines the optimal no-uncertainty configuration under the selected criterion ("GIC" or "BIC").

High-level workflow

The figure below summarizes the main stages of the greedy search and post-processing workflow implemented in bifrost.

svg_uri <- knitr::image_uri("quick-start/bifrost-workflow.svg")
cat(sprintf(
  '<p align="center"><img src="%s" alt="Workflow diagram showing setup, baseline scoring, greedy search, post-processing, and output steps in bifrost." style="max-width:100%%; height:auto;" /></p>',
  svg_uri
))

Because the search is greedy, it can converge to a local optimum. In practice, it is useful to repeat analyses with different min_descendant_tips, thresholds, and IC choices to assess robustness.

Core functions

• searchOptimalConfiguration()
  The main end-to-end function for candidate generation, parallel scoring, greedy shift acceptance, optional IC-weight calculations, and result assembly.
• plot_ic_acceptance_matrix()
  Visualize shift acceptance and information-criterion behavior across search iterations.

Internally, the search also relies on unexported helpers such as generatePaintedTrees(), fitMvglsAndExtractGIC(), fitMvglsAndExtractBIC(), calculateAllDeltaGIC(), paintSubTree_mod(), addShiftToModel(), removeShiftFromTree(), paintSubTree_removeShift(), whichShifts(), and extractRegimeVCVs(). Most users will not need to call these directly.

Reading the result

searchOptimalConfiguration() returns a named list that typically includes:

• Inputs and fitted objects: user_input, tree_no_uncertainty_transformed, tree_no_uncertainty_untransformed, and model_no_uncertainty.
• Search summary: shift_nodes_no_uncertainty, optimal_ic, baseline_ic, IC_used, num_candidates, and model_fit_history.
• Support and covariance summaries: VCVs, ic_weights, and warnings when present.

The two returned trees distinguish transformed versus original branch lengths, and ic_weights stores per-shift support summaries such as IC weights and evidence ratios when those calculations are requested.

Interpretation guidance:

• lower IC is better, so large positive values of baseline_ic - optimal_ic indicate stronger support for heterogeneity relative to the baseline model,
• values of optimal_ic - baseline_ic close to 0 suggest little support for adding shifts,
• shift_nodes_no_uncertainty identifies where supported changes in rate and covariance structure occur,
• VCVs summarize the estimated evolutionary covariance structure within each inferred regime,
• ic_weights help rank how strongly each accepted shift is supported in the final configuration,
• model_fit_history is especially useful for diagnosing search behavior with plot_ic_acceptance_matrix().

Practical guidance

When moving from this smoke test to a real dataset:

• set plot = FALSE unless you specifically want interactive plotting during the search,
• use larger min_descendant_tips values to avoid tiny-clade shifts and shrink the candidate space,
• start with a conservative shift_acceptance_threshold, then compare nearby settings or both "GIC" and "BIC" if model-selection sensitivity matters,
• plan for higher memory use on large or high-dimensional analyses,
• record sessionInfo() and the mvMORPH version, and consider using renv for fully reproducible runs,
• archive the tree, trait matrix, formula, thresholds, and random seed used for final fits,
• repeat searches under nearby settings before treating individual inferred shifts as robust.

Once this smoke test runs cleanly, the next step is usually to move to a real dataset and inspect the returned shifts, IC support values, and regime-specific VCVs in more detail. For a full empirical workflow, continue to the jaw-shape vignette; for broader conceptual background on the package website, see Brownian Motion and Multivariate Shifts and Whole-Tree Models, PCA, and bifrost.

bifrost documentation built on April 17, 2026, 9:07 a.m.