Abstract
We present an open source package for performing evolutionary quantitative genetics analyses in the R environment for statistical computing. Evolutionary theory shows that evolution depends critically on the available variation in a given population. When dealing with many quantitative traits this variation is expressed in the form of a covariance matrix, particularly the additive genetic covariance matrix or sometimes the phenotypic matrix, when the genetic matrix is unavailable and there is evidence the phenotypic matrix is sufficiently similar to the genetic matrix. Given this mathematical representation of available variation, the EvolQG package provides functions for calculation of relevant evolutionary statistics; estimation of sampling error; corrections for this error; matrix comparison via correlations, distances and matrix decomposition; analysis of modularity patterns; and functions for testing evolutionary hypotheses on taxa diversification.
Highlights
Quantitative genetics deals with the evolution and inheritance of continuous traits, like body size, bone lengths, gene expressions or any other inheritable characteristic that can be measured on a continuous scale, or which can be transformed to a continuous scale
Summary We have described a suite of functions dedicated to analyzing multivariate data sets within an evolutionary quantitative genetics framework
These functions focus on the central role that covariance and correlation matrices play in this framework; we provide functions that perform both descriptive statistics and hypothesis testing related to such matrices within an evolutionary context
Summary
This article is included in the RPackage gateway. Any reports and responses or comments on the article can be found at the end of the article. We provide detailed responses below, and in response to their criticism we have added several observations on the conditions for applying the methods described in the article, along with more detailed recommendations for sample sizes and premises. We added new functions the reviewers felt were missing, along with descriptions and comments. New functions: Bayesian Random skewers decomposition from Aguirre et al, 2014;. Eigentensor covariance matrix decomposition described in Hine et al, 2009, and the bayesian modification from Aguirre et al, 2014. Previous functions descriptions that have been made clearer: The meaning of significance in the Random Skewers comparison;. G-matrix it is possible to test if morphological differentiation of extant taxa is compatible with genetic drift or stabilizing selection (e.g., 2,34). The function CalcRepeatability() performs the calculation described in 29 for a set of multivariate traits measured at least twice for each individual
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