H
Holger Schielzeth
Researcher at University of Jena
Publications - 117
Citations - 20398
Holger Schielzeth is an academic researcher from University of Jena. The author has contributed to research in topics: Population & Zebra finch. The author has an hindex of 31, co-authored 106 publications receiving 15524 citations. Previous affiliations of Holger Schielzeth include University of Cambridge & Max Planck Society.
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A general and simple method for obtaining R2 from generalized linear mixed-effects models
TL;DR: In this article, the authors make a case for the importance of reporting variance explained (R2) as a relevant summarizing statistic of mixed-effects models, which is rare, even though R2 is routinely reported for linear models and also generalized linear models (GLM).
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Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.
TL;DR: Two types of repeatability (ordinary repeatability and extrapolated repeatability) are compared in relation to narrow‐sense heritability and two methods for calculating standard errors, confidence intervals and statistical significance are addressed.
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Simple means to improve the interpretability of regression coefficients
TL;DR: In this article, the authors focus on parameter estimation (point estimates as well as confidence intervals) rather than on significance thresholds for linear regression models and propose a simple alternative to the more complicated calculation of standard errors from contrasts and main effects.
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The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded.
TL;DR: This paper generalizes the methods called for Poisson and binomial GLMMs to all other non-Gaussian distributions, in particular to negative binomial and gamma distributions that are commonly used for modelling biological data and can be used across disciplines and regardless of statistical environments.
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rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models
TL;DR: al. as discussed by the authors introduced the R package rptR for the estimation of ICC and R for Gaussian, binomial and Poisson-distributed data, which allows the quantification of coefficients of determination R2 as well as of raw variance components.