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Published 20 hours ago • verified publisher icon Pakillo
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A unified framework for data analysis with GLM/GLMM in R

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- https://github.com/seananderson/glmm-course

Pakillo avatar Pakillo

Use modelStudio or easystats to examine fitted models

https://modeloriented.github.io/modelStudio/

Pakillo avatar Pakillo

Add dabestr visualisations to interpret effect sizes

https://github.com/ACCLAB/dabestr

Pakillo avatar Pakillo

Centering predictors can change significance of effects in models with interactions

Pakillo avatar Pakillo

Add average predictive comparisons

especially to interpret model with interactions, etc

Pakillo avatar Pakillo

Table 2 fallacy

Pakillo avatar Pakillo

Emphasize distribution notation (y ~ N(mu, sigma), y ~ Bin(N, p), y ~ Pois(lambda)) etc over y = a + bx

Pakillo avatar Pakillo

Use Beta1, Beta2, Beta3 etc rather than alpha, beta, gamma, for parameters?

Pakillo avatar Pakillo

Convergence problems

Very nice write-up on solving convergence problems in GLM/GLMM by M. Clark: https://m-clark.github.io/posts/2020-03-16-convergence/

Pakillo avatar Pakillo

Visualising shrinkage in mixed models

https://m-clark.github.io/posts/2019-05-14-shrinkage-in-mixed-models/ https://www.tjmahr.com/plotting-partial-pooling-in-mixed-effects-models/

Pakillo avatar Pakillo

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verified publisher icon Pakillo

Metadata

A unified framework for data analysis with GLM/GLMM in R

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