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View Code? Open in Web Editor NEWA unified framework for data analysis with GLM/GLMM in R
Home Page: http://pakillo.github.io/LM-GLM-GLMM-intro/
A unified framework for data analysis with GLM/GLMM in R
Home Page: http://pakillo.github.io/LM-GLM-GLMM-intro/
Particularly with smallish samples
See https://twitter.com/carlislerainey/status/1686389777225113601 & https://doi.org/10.1017/psrm.2021.9
con easystats
mammal sleep and paperplanes datasets have some complications (e.g. non-linear patterns) that probably advise against using them for such a very introduction to linear models...
Instead switched from iris to my simulated trees dataset (e48427c). But keeping both branches for the record (iris and trees)
to produce tables and plots summarising models
https://modelsummary.com/
Many definitions! Use some standard readily available in CRAN packages, e.g.
Or some other example: Many at Stat2data package: https://cran.r-project.org/web/packages/Stat2Data/Stat2Data.pdf
allEffects
with categorical and continuous predictor is not straightforward (plots much better).y ~ Normal(a + bx, sigma2)
terminology over y = a + bx
. More robust and extrapolable to other distributions laterVery nice write-up on solving convergence problems in GLM/GLMM by M. Clark: https://m-clark.github.io/posts/2020-03-16-convergence/
Table generated with xtable, stargazer...
In the first example (Heght ~ DBH)
and maybe switch to Metropolis beamer theme?
https://doi.org/10.1073/pnas.1207156109 (HT F. Harrell)
especially to interpret model with interactions, etc
same group, different group, etc
trees dataset may be too biological for some
See lme4 manual (http://lme4.r-forge.r-project.org/book/Ch2.pdf) and Bolker's article.
Nested random effects (1|a/b) are actually equivalent to (1|a) + (1|b), as long as levels in b are clearly specified so they can't be confounded. See also https://m-clark.github.io/mixed-models-with-R/extensions.html#crossed-vs.nested
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