Bühlmann, P. (2012). Statistical significance in high-dimensional linear models. arXiv:1202.1377v2. Craddock, N., et al. (2010). Genome-wide association study of cnvs in 16,000 cases of eight common diseases an
Bühlmann, P. (2012). Statistical significance in high-dimensional linear models. arXiv:1202.1377v2. Craddock, N., et al. (2010). Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature, 464, 713-720. Flint, J., & Mackay, T....
Bühlmann, P. (2012). Statistical significance in high-dimensional linear models. arXiv:1202.1377v2. Craddock, N., et al. (2010). Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls.Nature, 464, 713-720. Flint, J., & Mackay, T. ...
Bühlmann, P. (2012). Statistical significance in high-dimensional linear models. arXiv:1202.1377v2. Craddock, N., et al. (2010). Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls.Nature, 464, 713-720. Flint, J., & Mackay, T. ...
Bühlmann, P. (2012). Statistical significance in high-dimensional linear models. arXiv:1202.1377v2. Craddock, N., et al. (2010). Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls. _Nature_, 464, 713-720. ...
Bühlmann, P. (2012). Statistical significance in high-dimensional linear models. arXiv:1202.1377v2. Craddock, N., et al. (2010). Genome-wide association study of cnvs in 16,000 cases of eight common diseases and 3,000 shared controls. _Nature_, 464, 713-720. ...
## Models:## nofixedlmm:Mean.Pitch~1+(1|Group)## noranklmm:Mean.Pitch~Sex+(1|Group)## nosexlmm:Mean.Pitch~Social.Rank+(1|Group)## DfAICBIClogLik deviance Chisq Chi DfPr(>Chisq)## nofixedlmm3386390-190380## noranklmm4388393-1903800.4810.49## nosexlmm4388393-1903800.0001.00 ...
Non-Gaussian Mixed Models教程官网: https://drizopoulos.github.io/JMbayes2本次教程地址: https://drizopoulos.github.io/JMbayes2/articles/Non_Gaussian_Mixed_Models.html介绍利用GLMMadaptive软件包的多…
For example, if you find a significant 3-way interaction with a model of 3 fixed factors (like2 * 2 * 2), you then have 6 models to run:a1 -> b * c,a2 -> b * c,b1 -> a * c,b2 -> a * c,c1 -> a * b,c2 -> a * b. ...
在生态学研究领域,广义线性混合模型(Generalized Linear Mixed Models,简称GLMMs)是一种强大的统计工具,能够同时处理固定效应和随机效应,从而更准确地揭示生态系统中复杂关系的本质(点击文末“阅读原文”获取完整代码数据)。 拓端 2024/04/03 1.9K0 如何用潜类别混合效应模型(Latent Class Mixed Model ,LCMM)分析老年痴...