Statistical Inference and Prediction of Annotations in Phylogenetic Trees
Accuracy calculation as defined in Engelhardt et al. (2011)
Available methods from the APE package
Leave-one-out Cross Validation
Objects of class aphylo_estimates
Create an aphylo object with partial annotations
Model estimation using Markov Chain Monte Carlo
Model estimation using Maximum Likelihood Estimation
Annotated Phylogenetic Tree
Indexing aphylo objects
Information about aphylo and multiAphylo objects
Plot and print methods for aphylo objects
Formulas in aphylo
Statistical Inference in Annotated Phylogenetic Trees
Extensions to the as.phylo function
Area Under the Curve and Receiving Operating Curve
Functional balance of a tree
Default priors for aphylo_mcmc
Impute duplication events based on a vector of species
List each nodes' offspring or parent
Likelihood of an observed annotated phylogenetic tree
Switch labels acoording to mislabeling probabilities
Building Lists of Annotated Trees
Pointer to pruner
Reads PANTHER db trees
Plot Log-Likelihood function of the model
Multiavariate plot (surface)
Visualize predictions
Posterior probabilities based on parameter estimates
Calculate prediction score (quality of prediction)
Simulation of Annotated Phylogenetic Trees
Randomly drop leaf annotations
Read New Hampshire eXtended format for trees
Read PLI files from SIFTER
Simulate functions on a ginven tree
Random tree generation
Matrix of states
Write pli files used by SIFTER
Implements a parsimonious evolutionary model to analyze and predict gene-functional annotations in phylogenetic trees as described in Vega Yon et al. (2021) <doi:10.1371/journal.pcbi.1007948>. Focusing on computational efficiency, 'aphylo' makes it possible to estimate pooled phylogenetic models, including thousands (hundreds) of annotations (trees) in the same run. The package also provides the tools for visualization of annotated phylogenies, calculation of posterior probabilities (prediction) and goodness-of-fit assessment featured in Vega Yon et al. (2021).