English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Phylogenetic trait evolution models allow for the estimation of evolutionarycorrelations between a set of traits observed in a sample of related organisms.By directly modeling the evolution of the traits along an estimablephylogenetic tree, the model's structure effectively controls for sharedevolutionary history. In these models, relevant correlations are usuallyassessed through the high posterior density interval of their marginaldistributions. However, the selected correlations alone may not provide thefull picture regarding trait relationships. Their association structure,expressed through a graph that encodes partial correlations, can in contrasthighlight sparsity patterns featuring direct associations between traits. Inorder to develop a model-based method to identify this association structure weexplore the use of Gaussian graphical models (GGM) for covariance selection. Wemodel the precision matrix with a G-Wishart conjugate prior, which results insparse precision estimates. Furthermore the model naturally allows for BayesFactor tests of association between the traits, with no additional computationrequired. We evaluate our approach through Monte Carlo simulations andapplications that examine the association structure and evolutionarycorrelations of phenotypic traits in Darwin's finches and genomic andphenotypic traits in prokaryotes. Our approach provides accurate graphestimates and lower errors for the precision and correlation parameterestimates, particularly for conditionally independent traits, which are thetarget for sparsity in GGMs.

Sparse precision matrix estimation in phenotypic trait evolution models