## Articles in the R category

In this post I show how to make marginal inferences on the untransformed scaled when using multilevel models with a non-linear transformation applied to the dependent variable (a log-transformation is used as an example). Cluster-specific versus population-average (conditional versus marginal) effects are compared using both average effects on the untransformed scale and using relative (multiplicative) effects.

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The next version of powerlmm (0.4.0) will soon be released, besides bug fixes this version also includes several new simulation features. In this post I will show two examples that cover the major new features.

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My R package 'powerlmm' has now been update to version 0.3.0. It adds support for a more flexible effect size specifiation.

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This post contains the slides from a talk I gave recently at Stockholm University

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My R package 'powerlmm' has now been update to version 0.2.0. It contains several improvements, and new features.

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Non-randomized comparisons are common in RCTs. In this post I show some examples of confounding and collider bias, using treatment adherence as an example. I present a small simulation study that show that common regression models used in clinical psychology, makes little sense, and that Bayesian instrumental variable regression can be easily fit using the R package brms.

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In this post I compare the performance of Amazon EC2 instances vs my HP workstation and my MacBook Pro, when doing Monte Carlo simulations.

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Over the summer I've been working on finishing my new R package 'powerlmm', which is now almost complete. It provides flexible power calculations for typical two- and three-level longitudinal linear mixed models, with unbalanced treatment groups and cluster sizes, as well as with missing data and random slopes at both the subject and cluster-level.

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This post explains how Cohen's d relates to the proportion of overlap between two normal distributions, and why I use a different measure then Cohen in my Cohen's d visualization.

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I often get asked about how to fit different longitudinal models in lme/lmer. In this post I cover several different two-level, three-level and partially nested models.

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