Blog Archives

Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models

August 5, 2018
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Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models

When a multilevel model includes either a non-linear transformation (such as the log-transformation) of the response variable, or of the expectations via a GLM link-function, then the interpretation of the results will be different compared to a standard Gaussian multilevel model; specifically, the estimates will be on a transformed scale and not in the original units, and the effects...

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Do you really need a multilevel model? A preview of powerlmm 0.4.0

May 4, 2018
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Do you really need a multilevel model? A preview of powerlmm 0.4.0

In this post I will show some of the new simulation features that will be available in powerlmm 0.4.0. You can already install the dev version from GitHub. # GitHub devtools::install_github("rpsychologist/powerlmm") The revamped simulation functions offer 3 major new features: Compare multiple model formulas, including OLS models (no random effects). Evaluate a "forward" or "backward" model selection strategy using LRT. Apply a data transformation during...

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Power analysis for longitudinal multilevel models: powerlmm 0.3.0 is now out on CRAN

April 17, 2018
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My R package powerlmm 0.3.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm. New features This version adds support for raw effect sizes, and new standardized effect sizes using the function cohend(...). Here's an example that use the different types. p

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Slides from my talk on how to do power analysis for longitudinal 2- and 3-level models.

April 11, 2018
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Here's the slides from a talk I gave recently at Stockholm University: "Power Analysis for Longitudinal 2- and 3-Level Models: Challenges and Some Solutions Using the R Package powerlmm". The slides gives several code examples for a lot of powerlmm's f...

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Power analysis for longitudinal multilevel models: powerlmm 0.2.0 is now out on CRAN

March 21, 2018
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My R packge powerlmm 0.2.0 is now out on CRAN. It can be installed from CRAN https://cran.r-project.org/package=powerlmm or GitHub https://github.com/rpsychologist/powerlmm. Changes in version 0.2.0 New features Analytical power calculations now suppo...

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Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

February 1, 2018
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Confounded dose-response effects of treatment adherence: fitting Bayesian instrumental variable models using brms

Something that never ceases to amaze (depress) me, is how extremely common it is to see casual claims in RCTs, that are not part of the randomization. For instance, the relationship between treatment adherence and outcome, or between alliance and outcome, are often analyzed but seldom experimentally manipulated. This is basically observational research disguised as experimental, but without DAGs,...

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Are parallel simulations in the cloud worth it? Benchmarking my MBP vs my Workstation vs Amazon EC2

January 26, 2018
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Are parallel simulations in the cloud worth it? Benchmarking my MBP vs my Workstation vs Amazon EC2

If you tend to do lots of large Monte Carlo simulations, you've probably already discovered the benefits of multi-core CPUs and parallel computation. A simulation that takes 4 weeks without parallelization, can easily be done in 1 week on a quad core laptop with parallelization. However, for even larger simulations reducing the computation time down from e.g. 8 months...

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Introducing ‘powerlmm’ an R package for power calculations for longitudinal multilevel models

August 24, 2017
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Over the years I've produced quite a lot of code for power calculations and simulations of different longitudinal linear mixed models. Over the summer I bundled together these calculations for the designs I most typically encounter into an R package. T...

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Where Cohen went wrong – the proportion of overlap between two normal distributions

January 23, 2017
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Where Cohen went wrong – the proportion of overlap between two normal distributions

I've received many emails regarding the percent of overlap reported in my Cohen's d visualization. Observant readers, have noted that I report a different number than Cohen (and other authors). For instance, if we open p. 22 in Cohen's Statistical power analysis for the behavior sciences, we see that Cohen writes that d = 0.5 means a 33 %...

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Where Cohen went wrong – the proportion of overlap between two normal distributions

January 23, 2017
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Where Cohen went wrong – the proportion of overlap between two normal distributions

I've received many emails regarding the percent of overlap reported in my Cohen's d visualization. Observant readers, have noted that I report a different number than Cohen (and other authors). For instance, if we open p. 22 in Cohen's Statistical power analysis for the behavior sciences, we see that Cohen writes that d = 0.5 means a 33 %...

Read more »

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