How to Pool MI Confidence Intervals of Robust Mixed Model in R скачать в хорошем качестве

How to Pool MI Confidence Intervals of Robust Mixed Model in R

Struggling with pooling MI confidence intervals in R using robust mixed models? Learn how to solve common errors and gain insights in this comprehensive guide. --- This video is based on the question https://stackoverflow.com/q/70261773/ asked by the user 'MDSF' ( https://stackoverflow.com/u/13237975/ ) and on the answer https://stackoverflow.com/a/70263141/ provided by the user 'Ben Bolker' ( https://stackoverflow.com/u/190277/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions. Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: how to pool MI confidence intervals of robust mixed model in r? Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l... The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license. If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com. --- Solving MI Confidence Interval Pooling in R with Robust Mixed Models When working with mixed models in R, especially those that utilize the rlmer function from the robustlmm package, you might encounter some challenges, particularly when trying to pool results from multiple imputations (MIs). Specifically, the error message No tidy method for objects of class rlmerMod can be quite perplexing. This guide aims to break down the problem and offer a comprehensive solution to successfully pool MI confidence intervals of robust mixed models. Understanding the Problem The challenge arises when you attempt to pool results after fitting a robust linear mixed-effects model using the rlmer function on data that has been imputed multiple times using the mice package. Here's a brief overview of the steps leading to the issue: Preparing the Data: You set up a data table and introduce missing values intentionally. Multiple Imputation: The mice function is used to create multiple datasets with imputation. Model Fitting: A mixed-effects model is fitted using the rlmer function. Pooling: When you try to pool the results with the pool function, you encounter errors related to the rlmerMod class. The Error The specific error message reads: [[See Video to Reveal this Text or Code Snippet]] Additionally, you may receive warnings about infinite sample size assumptions, which signals further issues with degrees of freedom in your pooled results. Solution Steps Step 1: Load Required Libraries To resolve the error, we can leverage the capabilities of the broom.mixed library. This library includes functions that can help tidy model output, which is crucial for pooling insights. To start, you need to install and load the broom.mixed package: [[See Video to Reveal this Text or Code Snippet]] Step 2: Re-Pool the Model Results After loading the broom.mixed package, you can attempt to pool the model results again: [[See Video to Reveal this Text or Code Snippet]] This time, it should successfully pool the parameters without throwing the previous error. Step 3: Obtain Summary with Confidence Intervals Once you have pooled the results, you can summarize them, aiming to retrieve confidence intervals: [[See Video to Reveal this Text or Code Snippet]] However, if you still encounter NaN values for degrees of freedom and confidence intervals, it indicates that additional parameters may be required or adjustments might be necessary for defining your degrees of freedom method. Troubleshooting Further Issues If errors persist while using the tbl_regression function from the gtsummary package, ensure that the model and pooling are appropriately set up. You can also refer to the specific models in your original data, as differences may arise due to data structure or missingness patterns. Key Takeaways Use the broom.mixed package to obtain tidy outputs from mixed models involving rlmerMod. Make sure to check the results for any NaN values that might indicate other underlying issues. Always test your codes with reproducible examples to isolate and identify errors effectively. By following these steps, you can successfully pool MI confidence intervals of robust mixed models in R, enabling more powerful insights from your statistical analyses. If you run into further complications, consider seeking advice from the R community or exploring the documentation for the packages mentioned. Feel free to reach out with any questions or comments as you navigate this process!