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aosmith.rbind.io | ||
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www.rdatagen.net
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| | | | | Simulation can be super helpful for estimating power or sample size requirements when the study design is complex. This approach has some advantages over an analytic one (i.e.one based on a formula), particularly the flexibility it affords in setting up the specific assumptions in the planned study, such as time trends, patterns of missingness, or effects of different levels of clustering. A downside is certainly the complexity of writing the code as well as the computation time, which can be a bit painful. My goal here is to show that at least writing the code need not be overwhelming. | |
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www.fromthebottomoftheheap.net
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| | | | | [AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i... | |
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r-video-tutorial.blogspot.com
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| | | | | Power analysis is extremely important in statistics since it allows us to calculate how many chances we have of obtaining realistic result... | |
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easystats.github.io
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| | | In this tutorial, we will introduce multilevel correlations (or hierarchical / random-effects correlations) and how to compute them using the new correlations package from the easystats suite. You can install the updated version and load the package as follows: install.packages("correlation") library(correlation) Data Imagine we have an experiment in which 10 individuals completed a task with 100 trials. For each of the 1000 total trials, we measured two things, V1 and V2, and our research aims at investingating the link between these two variables. | ||
