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freerangestats.info | ||
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juliasilge.com
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| | | | | A data science blog | |
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www.fromthebottomoftheheap.net
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| | | | | [AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i... | |
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www.seascapemodels.org
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| | | | | [AI summary] A tutorial explaining the basics of General Linear Models using statistical concepts like linear equations, R programming simulations, and assumption checking for biological sciences. | |
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0fps.net
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| | | (This is the sequel to the following post on SmoothLife. For background information go there, or read Stephan Rafler's paper on SmoothLife here.) Last time, we talked about an interesting generalization of Conway's Game of Life and walked through the details of how it was derived, and investigated some strategies for discretizing it. Today, let's... | ||
