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Linear Mixed-Effects Modelling in R

This tutorial is an intermediate-deep dive into the world of mixed-effects modeling, where there are certain clustering variables in which data are correlated in a manner unique to that specific cluster itself, and usually no other cluster. This hierarchical modeling allows us to ensure that data are modeled according to these hierarchical clusters. Simple random effects and nested random effects are discussed. In addition, the advantage of mixed-effects over simple fixed effects modeling is also discussed. The code is in my GitHub repository: https://github.com/suhrudp/ My playlist `ABCs of R`:    • ABCs of R   The analysis shown here is done using the R Language for Statistical Computing in RStudio. For further clarification, feel free to reach out to me at: [email protected] My LinkedIn:   / suhrud-panchawagh-40a8731b4   My Portfolio: https://suhrudp.github.io/ Check out our very own website for consultations and other services: https://www.requirstats.in/ Introduction to R (https://www.r-project.org/about.html): R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues. R can be considered as a different implementation of S. There are some important differences, but much code written for S runs unaltered under R. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity. One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed. Great care has been taken over the defaults for the minor design choices in graphics, but the user retains full control. R is available as Free Software under the terms of the Free Software Foundation’s GNU General Public License in source code form. It compiles and runs on a wide variety of UNIX platforms and similar systems (including FreeBSD and Linux), Windows and MacOS. This 'Stats, stat' tutorial shows how statistical analysis can be done free of cost, by anyone, irrespective of their mathematical aptitude. These tutorials are made especially for medical students and residents or any researcher in health-care, who need data quickly analyzed, with tables, graphs, and plots conveying maximum information, and also reporting those results in a standard manner, so that writing the 'materials and methods' and 'results' section of the manuscript becomes a cake-walk. Why healthcare professionals, especially medical students and residents should care about biostatistics: Statistics, especially biostatistics, remains an enigma all throughout undergraduate days for medical students. Applying a few formulae which didn't even make sense during a 15 minute biostatistics exam or during your entrance/licensing exam was all that was expected. However, the beauty of statistics and probability lies in its practicality and the power that it wields to influence decision making for the benefit of all mankind.