# Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood

@article{Houle2015EstimatingSE, title={Estimating sampling error of evolutionary statistics based on genetic covariance matrices using maximum likelihood}, author={David Houle and Karin Meyer}, journal={Journal of Evolutionary Biology}, year={2015}, volume={28} }

We explore the estimation of uncertainty in evolutionary parameters using a recently devised approach for resampling entire additive genetic variance–covariance matrices (G). Large‐sample theory shows that maximum‐likelihood estimates (including restricted maximum likelihood, REML) asymptotically have a multivariate normal distribution, with covariance matrix derived from the inverse of the information matrix, and mean equal to the estimated G. This suggests that sampling estimates of G from…

## 60 Citations

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ThevolQG 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.

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- BiologyF1000Research
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