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 rpsychologist.com
Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models | R Psychologist
| | hbiostat.org
Incorrect Covariate Adjustment May Be More Correct than Adjusted Marginal Estimates - Statistical Thinking
3.3 parsecs away

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| | This article provides a demonstration that the perceived non-robustness of nonlinear models for covariate adjustment in randomized trials may be less of an issue than the non-transportability of marginal so-called robust estimators.
| | aosmith.rbind.io
Simulate! Simulate! - Part 4: A binomial generalized linear mixed model
3.6 parsecs away

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| | I walk through an example of simulating data from a binomial generalized linear mixed model with a logit link and then exploring estimates of over/underdispersion.
| | fharrell.com
Ordinal Models for Paired Data - Statistical Thinking
3.7 parsecs away

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| | This article briefly discusses why the rank difference test is better than the Wilcoxon signed-rank test for paired data, then shows how to generalize the rank difference test using the proportional odds ordinal logistic semiparametric regression model. To make the regression model work for non-independent (paired) measurements, the robust cluster sandwich covariance estimator is used for the log odds ratio. Power and type I assertion \alpha probabilities are compared with the paired t-test for n=25. The ordinal model yields \alpha=0.05 under the null and has power that is virtually as good as the optimum paired t-test. For non-normal data the ordinal model power exceeds that of the parametric test.
| | www.cedricscherer.com
A ggplot2 Tutorial for Beautiful Plotting in R - Cédric Scherer
24.6 parsecs away

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| An extensive tutorial containing a general introduction to ggplot2 as well as many examples how to modify a ggplot, step by step. It covers several topics such as different chart types, themes, design choices, plot combinations, and modification of axes, labels, and legends, custom fonts, interactive charts and many more.