Statistics
Buy me a coffeeA guide to working with country-year panel data and Bayesian multilevel models
By Andrew Heiss
Added Sun Dec 12, 2021
What is this?
Excerpt from guide: “Here’s a basic guide to dealing with country-year panel data using Bayesian multilevel modeling!”
- Link to guide here: https://www.andrewheiss.com/blog/2021/12/01/multilevel-models-panel-data-guide/
Advanced-Research-Methods-for-Psychologists
What is this?
Excerpt from site: Lesson files used in the Advanced Research Methods for Psychologists - Practical Applications in R, taught at Ben-Gurion University on the Negev.
- Link to repo here: https://github.com/mattansb/Advanced-Research-Methods-foR-Psychologists
An introduction to statistics in R
What is this?
Excerpt from site: A series of tutorials by Mark Peterson for working in R
- Link to site here: http://petersonbiology.com/math230Notes/dataInR.html
Analysis of Factorial Designs for Psychologists
What is this?
Excerpt from site: This Github repo contains all lesson files used in the graduate-level course: Analysis of Factorial Designs foR Psychologists - Practical Applications in R, taught at Ben-Gurion University on the Negev (spring 2019 semester). This course assumes basic competence in R (importing, regression modeling, plotting, etc.), a long the lines of the prerequisite course, Advanced Research Methods foR Psychologists, which can be found here.
A Practical Extension of Introductory Statistics in Psychology using R
By Ekarin E. Pongpipat, Giuseppe G. Miranda, & Matthew J. Kmiecik
What is this?
Excerpt from e-book: the book will provide some evidence along with R code for others to see how the aforementioned analyses can be analyzed within the GLM framework with identical answers.
- Link to ebook: https://rpsystats.com/
Bayesian Data Analysis course
By Aki Vehtari
What is this?
Excerpt from course site: This is the web page for the Bayesian Data Analysis course at Aalto (CS-E5710) by Aki Vehtari.
- Link to course: https://avehtari.github.io/BDA_course_Aalto/
- Electronic book for course here: https://users.aalto.fi/~ave/BDA3.pdf
Bayesian Hierarchical Models in Ecology
By Steve Midway
Added Sun May 28th, 2023
What is this?
Learning objectives from ebook: Welcome to Bayesian Hierarchical Models in Ecology. This is an ebook that is also serving as the course materials for a graduate class of the same name. There will be numerous and on-going changes to this book, so please check back.
- Link to site: https://bookdown.org/steve_midway/BHME/
Bayesian statistics with R
Added Tuesday Oct 12th, 2021
What is this?
Learning objectives from site: Try and demystify Bayesian statistics, and MCMC methods; Make the difference between Bayesian and Frequentist analyses; Understand the Methods section of a paper that does Bayesian stuff; Run Bayesian analyses with R (in Jags)
- Link to site: https://oliviergimenez.github.io/bayesian-stats-with-R/
- Link to repo: https://github.com/oliviergimenez/bayesian-stats-with-R
- Link to recording: https://www.youtube.com/watch?v =uvU-TmEt8_M
Broadening Your Statistical Horizons Generalized Linear Models and Multilevel Models
What is this?
Awesome books for stats!! Fav book
Check how good your model is using “Performance”package
Added Thursday Dec 31st, 2020
What is this?
Excerpt from site: Utilities for computing measures to assess model quality, which are not directly provided by R’s ‘base’ or ‘stats’ packages. These include e.g. measures like r-squared, intraclass correlation coefficient (Nakagawa, Johnson & Schielzeth (2017) doi:10.1098/rsif.2017.0213), root mean squared error or functions to check models for overdispersion, singularity or zero-inflation and more. Functions apply to a large variety of regression models, including generalized linear models, mixed effects models and Bayesian models.
- Link to review video by Yury Zablotski here: https://www.youtube.com/watch?fbclid=IwAR296jgFcxLDNHE0zatJlMWGFb8RTVcWfYpRQ9261wzvlacp8M0BDs1K3iI&v=EPIxQ5i5oxs&feature=youtu.be
- Link to CRAN vignette here: https://cran.r-project.org/web/packages/performance/performance.pdf
- Link to repo here: https://easystats.github.io/performance/
Comparing Multiple Means in R: Repeated Measures ANOVA in R
Added Sun Sep 13th, 2020
What is this?
Excerpt from site: This chapter describes the different types of repeated measures ANOVA, including: One-way repeated-measures ANOVA, two-way repeated-measures ANOVA, three-way repeated-measures ANOVA.
- Link to site here: https://www.datanovia.com/en/lessons/repeated-measures-anova-in-r/
Conversion in Psychology-MsC course
What is this?
Excerpt from site: This book contains the quantitative research methods materials for students on the MSc Conversion in Psychological Studies/Science. The students are typically a diverse cohort and range from those with no STEM or programming background to engineering and computing science graduates. Compared to the undergraduate degree, the students are older, and there is a greater incidence of computer anxiety.
The focus for the MSc is to provide a basic but solid competency in core data skills and statistics that can be built on in further study. Students who wish to push themselves beyond the core competencies are encouraged to consult the MSc Data Skills course where they can learn about e.g., simulation and custom functions. To support those students who may have very limited computer literacy, the beginning stages are more supported than in the undergraduate programme e.g., with an increased use of screenshots and explanations for terminology.
- Course is here: https://psyteachr.github.io/msc-conv-f2f/
Computational Toolkit for Educational Scientists
What is this?
Excerpt from ebook: The first set of tools we will discuss will be related to statistical computation. Although there are many computational tools for statistical analysis, the first tools we will add to your computational toolkit is R. R is a free software environment for statistical computing and graphics. It can be installed on a variety of operating systems, including the MacOS, Windows, and UNIX platforms. To really make use of the computational power of R, we are also going to introduce you to RStudio, an open-source front-end1 to R.
The initial chapters of this document will address:
Installing R and RStudio; Getting started with R’s computational syntax; Wrangling data using functions from the dplyr package; and Visualizing data using functions from the ggplot2 package.
Crump Lab Statistics for Undergrads in Psychology Textbook
What is this?
This is a FREE Statistics for Undergrads in Psychology Textbook, on a creative commons license. Source code for everything available in the respective repos.
- Web-book is here: https://crumplab.github.io/statistics/
- Lab manual is here: https://crumplab.github.io/statisticsLab/
- Course website is here: https://crumplab.github.io/psyc3400/
DHARMa - Residual Diagnostics for HierARchical Models
What is this?
Excerpt from repo: The ‘DHARMa’ package uses a simulation-based approach to create readily interpretable scaled (quantile) residuals for fitted (generalized) linear mixed models. Currently supported are linear and generalized linear (mixed) models from ‘lme4’ (classes ‘lmerMod’, ‘glmerMod’), ‘glmmTMB’ ‘GLMMadaptive’ and ‘spaMM’, generalized additive models (‘gam’ from ‘mgcv’), ‘glm’ (including ‘negbin’ from ‘MASS’, but excluding quasi-distributions) and ‘lm’ model classes. Moreover, externally created simulations, e.g. posterior predictive simulations from Bayesian software such as ‘JAGS’, ‘STAN’, or ‘BUGS’ can be processed as well.
- Link to repo here: https://github.com/florianhartig/DHARMa
Five different “Cohen’s d” statistics for within-subject designs
What is this?
Excerpt from blog: What I’ll show here is that there are at least 5 different and non-equivalent ways that people might compute a d-like effect size (which they would invariably simply call “Cohen’s d”) for Jeff’s dataset, and the resulting effect sizes range from about 0.25 to 1.91. I’ll compare and contrast these procedures and ultimately choose one that I think is the least crazy, if you must compute a standardized effect size (more on that later).
GAM in R
By Noam Ross
What is this?
Excerpt from site: Generalized Additive Models in R. This short course will teach you how to use these flexible, powerful tools to model data and solve data science problems. GAMs offer offer a middle ground between simple linear models and complex machine-learning techniques, allowing you to model and understand complex systems.
- Link to the interactive course here: https://noamross.github.io/gams-in-r-course/
Generalized Additive Models
What is this?
Excerpt from site: The following provides a brief introduction to generalized additive models and some thoughts on getting started within the R environment. It doesn’t assume much more than a basic exposure to regression, and maybe a general idea of R, though not necessarily any particular expertise. The presentation is of a very applied nature, and such that the topics build upon the familiar and generalize to the less so, with the hope that one can bring the concepts they are comfortable with to the new material. The audience in mind is a researcher with typical applied science training.
- Link to ebook: https://m-clark.github.io/generalized-additive-models/
grateful: Facilitate citation of R packages 💯
By Francisco Rodriguez-Sanchez & Connor P. Jackson
Added Mon Apr 24th, 2023
What is it?
Excerpt from package: The goal of grateful is to make it very easy to cite R and the R packages used in any analyses, so that package authors receive their deserved credit. By calling a single function, grateful will scan the project for R packages used and generate a BibTeX file containing all citations for those packages.
grateful can then generate a new document with citations in the desired output format (Word, PDF, HTML, Markdown). These references can be formatted for a specific journal, so that we can just paste them directly into our manuscript or report.
- Link to package: https://pakillo.github.io/grateful/
Handling missing values in R
Added Sun June 6th, 2021
What is this?
Excerpt from blog: Handling missing values in R, one of the common tasks in data analysis is handling missing values. In R, missing values are often represented by the symbol NA (not available) or some other value that represents missing values (i.e. 99). Impossible values (e.g., dividing by zero) are represented by the symbol NaN (not a number)
- Link to blog here: https://www.r-bloggers.com/2021/04/handling-missing-values-in-r/
Introduction to Empirical Bayes: Examples from Baseball Statistics
What is this?
Excerpt from ebook: This book is adapted from a series of ten posts on my blog, starting with Understanding the beta distribution and ending recently with Simulation of empirical Bayesian methods. In these posts I’ve introduced the empirical Bayesian approach to estimation, credible intervals, A/B testing, mixture models, and other methods, all through the example of baseball batting averages.
- Link to e-book here: https://gumroad.com/l/empirical-bayes
Introduction to Multilevel Modelling
By Mairead Shaw & Jessica Kay Flake
Added Sun April 30th 2023
What is this?
Excerpt from site: This website will teach you the fundamentals about multilevel modelling, from why and when you would use them and how to do so for various research questions and data structures.
- Link to website here: https://www.learn-mlms.com/
Learn Tidymodels
What is this?
Excerpt from site: After you know what you need to get started with tidymodels, you can learn more and go further. Find articles here to help you solve specific problems using the tidymodels framework.
- Link to site here: https://www.tidymodels.org/learn/
Learning Stats with Andy Wills and others:
- Group differences
- Evidence: What’s a P value?, t-tests by Andy Wills & Chris Berry
- More on T-tests
- More on Bayes Factors by Andy Wills & Chris Berry
- Inter-rater reliability by Michaela Gummerum & Andy Wills 💯
- More on Cohen’s Kappa 💯
- Within-subject differences by Andy Wills and Clare Walsh
- Factorial differences by Andy Wills and Clare Walsh
- Factorial differences, part 2 by Andy Wills and Clare Walsh
- Traditional ANOVA by Andy Wills
- Better tables by Paul Sharpe, Andy Wills
- Analysing scales by Paul Sharpe, Andy Wills, Sophie Homer
- Traditional non-parametric tests by Paul Sharpe
Learning Statistics
What is this?
The wonderful Danielle Navarro taught an introductory statistics class for psychology students. Her lecture notes for this class became a great book that is freely available.
- The book is here: https://learningstatisticswithr.com/lsr-0.6.pdf or https://learningstatisticswithr.com/book/
- The repository with all the source materials is here: https://github.com/djnavarro/rbook
Learning stats with Jamovi
What is this?
Technically not R but a wonderful resources anyways. If you would like to learn or teach people how to use JAMOVI, here is my favorite book!
- The book is here: https://drive.google.com/file/d/1aNNhC9rMUISCdcWCsNW9rf70Yv1scv6N/view
lm-table-maker
Added by Sun May 28th, 2023
What is this?
Excerpt from site: R function to generate publication-ready figures from linear models. Exports as html file
- Link to repo and function here: https://github.com/patrickmonari/lm-table-maker
Linear Mixed Effects Models - R
Added by Tue Oct 12th, 2021
What is this?
Excerpt from twitter post: an accessible guide to Linear Mixed Effects Models in R using the lmer package (with background stats intro & coding examples).
Methods and Algorithms for Correlation Analysis in R
By Dominique Makowski, Mattan S. Ben-Shachar, Indrajeet Patil, & Daniel Lüdecke
What is this?
Excerpt from site: Correlations tests are arguably one of the most commonly used statistical procedures, and are used as a basis in many applications such as exploratory data analysis, structural modeling, data engineering etc. In this context, we present correlation, a toolbox for the R language(R Core Team, 2019) and part of the easy stats collection, focused on correlation analysis.
- Link to paper here: https://www.researchgate.net/publication/342978399_Methods_and_Algorithms_for_Correlation_Analysis_in_R
- Link to repo here: https://github.com/easystats/correlation
Model Summary
What is this?
modelsummary creates beautiful and customizable tables to summarize statistical models in R. Results from several models are presented side-by-side. Tables can be echoed to the R console or viewed in the RStudio Viewer. They can be saved to HTML, PDF, Text/Markdown, LaTeX, MS Word, RTF, JPG, and PNG formats. Tables can easily be embedded in dynamic document pipelines like Rmarkdown, knitr, or Sweave.
- Link to site: https://vincentarelbundock.github.io/modelsummary/
Modern Statistics with R: From wrangling and exploring data to inference and predictive modelling
By Måns Thulin
Added Sun April 30th 2023
What is this?
Excerpt from e-book: This is the online version of the book Modern Statistics with R. It is free to use, and always will be. Printed copies are available where books are sold (ISBN 9789152701515).
The past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. The aim of Modern Statistics with R is to introduce you to key parts of the modern statistical toolkit.
- Link to e-book here: https://modernstatisticswithr.com/index.html
Pckages for Exploratory Data Analysis💯
What is this?
This is a list of all the packages mentioned in the “The Landscape of R Packages for Automated Exploratory Data Analysis” article by Mateusz Staniak & Przemysław Biecek. Check the paper out!
- The arsenal package (Heinzen et al., 2019): https://cran.r-project.org/web/packages/arsenal/arsenal.pdf
- The autoEDA package (Horn, 2018a): https://github.com/XanderHorn/autoEDA
- The DataExplorer (Cui, 2019): https://cran.r-project.org/package=DataExplorer
- The dataMaid (Petersen and Ekstrom, 2018): https://cran.r-project.org/package=dataMaid
- The dlookr (Ryu, 2019) package: https://cran.r-project.org/package=dlookr
- The ExPanDaR package (Gassen, 2018): https://github.com/joachim-gassen/ExPanDaR
- The explore package (Krasser, 2019): https://cran.r-project.org/package=explore
- The exploreR package (Coates, 2016): https://cran.r-project.org/package=exploreR
- The package funModeling (Casas, 2019): https://cran.r-project.org/package=funModeling
- The inspectdf package (Rushworth, 2019): https://cran.r-project.org/package=inspectdf
- The RtutoR package (Nair, 2018a): https://cran.r-project.org/package=RtutoR
- The SmartEDA package (Ubrangala et al., 2018): https://cran.r-project.org/package=SmartEDA
- The summarytools package (Comtois, 2019): https://cran.r-project.org/package=summarytools
- The package visdat (Tierney, 2017): https://cran.r-project.org/web/packages/visdat/index.html
- The xray (Seibelt, 2017) package: https://cran.r-project.org/package=xray
- The package tableone (Yoshida and Bohn., 2018): https://cran.r-project.org/package=tableone
- The describe function from describer package (Hendricks, 2015): https://cran.r-project.org/package=describer
- The skimr (Quinn et al., 2019) package: https://cran.r-project.org/package=skimr
- The prettyR (Lemon and Grosjean, 2018) package: https://cran.r-project.org/package=prettyR
- The package Hmisc (Harrell Jr et al., 2019) Describe function: https://cran.r-project.org/package=Hmisc
PSYCH 252: Statistical Methods at Stanford University
What is this?
Excerpt from site: This course offers an introduction to advanced topics in statistics with the focus of understanding data in the behavioral and social sciences. It is a practical course in which learning statistical concepts and building models in R go hand in hand.
- Link to e-course here: https://psych252.github.io/
PsyTeachR University of Glasgow
What is this?
Excerpt from site: The psyTeachR team at the University of Glasgow School of Psychology and Institute of Neuroscience and Psychology has successfully made the transition to teaching reproducible research using R across all undergraduate and postgraduate levels. Our curriculum now emphasizes essential ‘data science’ graduate skills that have been overlooked in traditional approaches to teaching, including programming skills, data visualisation, data wrangling and reproducible reports. Students learn about probability and inference through data simulation as well as by working with real datasets.
This website contains our open materials for teaching reproducible research.
Courses books:
- Link to site here: https://psyteachr.github.io/
- Link to Level 1 Data Skills here: https://psyteachr.github.io/ug1-practical/
- Link to Level 2 Research Methods and Statistics Practical Skills here: https://psyteachr.github.io/ug2-practical/
- Link to Level 3 Learning Statistical Models Through Simulation in R here: https://psyteachr.github.io/ug3-stats/
- Link to MSc Conversion in Psychological Studies/Science here: https://psyteachr.github.io/msc-conv-f2f/
- Link to Data Skills for Reproducible Science here: https://psyteachr.github.io/msc-data-skills/
R course
What is this?
Excerpt from course: The goal of this course is to give you the skills to do the statistics that are in current published papers, and make pretty figures to show off your results. While we will go over the mathematical concepts behind the statistics, this is NOT meant to be a classical statistics class. We will focus more on making the connection between the mathematical equation and the R code, and what types of variables fit into each slot of the equation.
- Link to course here: https://pagepiccinini.com/r-course/
R for Data Analysis
Added Fri Apr 14th, 2023
What is this?
Excerpt from e-book: The purpose of this book is to inspire and enable anyone who reads it to reconsider the methods they currently employ to analyse data. This is not to suggest that the methodologies outlined will be useful or sufficient for everyone who reads it. Some analyses can be performed quickly without the need for additional computation while others will require advanced analytics techniques not outlined in this book; however, the aspiration is that all will be equipped with novel tools and ideas for approaching data analysis.
- Link to e-book here: https://trevorfrench.github.io/R-for-Data-Analysis/?es_id=aaa755b1d8
R For SNA
By Eric Brewe
What is this?
Excerpt from site: This workshop was designed to help get you started on using R to analyze social network data.
- Link to three workshops: https://ericbrewe.com/courses/rforsna/
- Link to Workshop 1: https://ericbrewe.com/courses/rforsna/ws1/
- Link to Workshop 2: https://ericbrewe.com/courses/rforsna/ws2/
- Link to Workshop 3: https://ericbrewe.com/courses/rforsna/ws3/
r-statistics.co
What is this?
Excerpt from site: An educational resource for those seeking knowledge related to machine learning and statistical computing in R. Here, you will find quality articles, with working R code and examples, where, the goal is to make the #rstats concepts clear and as simple as possible.
This is built by keeping in mind, statisticians who are new to R programming language, R programmers without a stats background, analysts who work in SAS or python, college grads and developers who are relatively new to both R and stats/ML.
- Link to website here: https://r-statistics.co/
“report” Package
What is this?
Excerpt from site: report’s primary goal is to bridge the gap between R’s output and the formatted results contained in your manuscript. It automatically produces reports of models and dataframes according to best practices guidelines (e.g., APA’s style), ensuring standardization and quality in results reporting.
- Link to repo here: https://easystats.github.io/report/index.html
Reproducible Statistics for Psychologists with R-Lab Tutorials
What is this?
Excerpt from site: This is a series of labs/tutorials currently under development (2020-2021) for a two-semester graduate-level statistics sequence in Psychology @ Brooklyn College of CUNY. The goal of these tutorials is to 1) develop a deeper conceptual understanding of the principles of statistical analysis and inference; and 2) develop practical skills for data-analysis, such as using the increasingly popular statistical software environment R to code reproducible analyses.
- Link to website here: https://crumplab.github.io/rstatsforpsych/index.html
Research Methods in Practice 1
By Ben Whalley, Ellie Lloyd, Maggie Brennan, and Andy Wills
What is this?
Excerpt from ebook: We cover qualitative approaches, where you search for common themes within spoken or written material, and quantitative approaches where you use one or more measures to predict an outcome. Each week you will work in groups to solve problems and work towards a practical presentation of your work at the end of the semester (worth 20% of the mark), with two pieces of written coursework during the module (80% of the mark).
- Link to ebook here: https://benwhalley.github.io/rmip/
Running Multiple Linear Regression Models in for-Loop
Added Sun June 6th, 2021
What is this?
Excerpt from site: In this article, I’ll show how to estimate multiple regression models in a for-loop in the R programming language.
- Link to article here: https://statisticsglobe.com/r-multiple-regressions-in-for-loop
Statistical Analysis and Visualizations Using R
By Okan Bulut
Added Thu April 15th, 2021
What is this?
Excerpt from site: This full-day course is intended to provide participants with a hands-on training in exploring, visualizing, and analyzing data using the R programming language.1 To control R, participants will use RStudio, which is a free, user-friendly program with a console, syntax-highlighting editor that supports direct code execution, and a variety of robust tools for plotting.
- Link to course here: https://okanbulut.github.io/rbook/
Statistical Modeling and Computation for Educational Scientists
What is this?
Excerpt from book: The content in this “book”, as the title suggests, is related to statistical modeling and computation. More specifically, the content focuses on using the General Linear Model (GLM) to provide statistical evidence that can help answer substantive questions in the educational and social sciences. It is a book intended for applied practitioners in the educational or social sciences. The statistical content is hopefully presented in a manner that these domian scientists will find useful, including practical suggestions for analysis and the presentation of results intended to help researchers clearly communicate the results of a data analysis.
- Link to book here: https://zief0002.github.io/modeling/
Statistical Thinking for the 21st Century 💯
What is this?
Excerpt from site: The goal of this book is to the tell the story of statistics as it is used today by researchers around the world. It’s a different story than the one told in most introductory statistics books, which focus on teaching how to use a set of tools to acheive very specific goals. This book focuses on understanding the basic ideas of statistical thinking — a systematic way of thinking about how we describe the world and make decisions and predictions, all in the context of the inherent uncertainty that exists in the real world. It also brings to bear current methods that have only become feasible in light of the amazing increases in computational power that have happened in the last few decades. Analyses that would have taken years in the 1950’s can now be completed in a few seconds on a standard laptop computer, and this power unleashes the ability to use computer simulation to ask questions in new and powerful ways.
- Link to site here: https://statsthinking21.org/
- Link to core text here: https://statsthinking21.github.io/statsthinking21-core-site/
- Link to R repo here: https://github.com/statsthinking21/statsthinking21-R
- Link to Python repo here (under work now): https://github.com/statsthinking21/statsthinking21-python
Statistical rethinking with brms, ggplot2, and the tidyverse: Second edition by Solomon Kurz
What is this?
Excerpt from e-book: This ebook is based on the second edition of Richard McElreath’s (2020a) text, Statistical rethinking: A Bayesian course with examples in R and Stan. My contributions show how to fit the models he covered with Paul Bürkner’s brms package (Bürkner, 2017, 2018, 2020c), which makes it easy to fit Bayesian regression models in R (R Core Team, 2020) using Hamiltonian Monte Carlo. I also prefer plotting and data wrangling with the packages from the tidyverse (Wickham, 2019; Wickham et al., 2019). So we’ll be using those methods, too.
- Link to ebook: https://bookdown.org/content/4857/
Statistical Rethinking 2 with Stan and R
What is this?
Excerpt from e-book: Vincent is trying to replicate (nearly) all the models in Richard McElreath’s Statistical Rethinking (2nd ed.) book using Stan, R, rstan, tidybayes, and ggplot2. This is work in progress.
- Link to ebook here: https://vincentarelbundock.github.io/rethinking2/
- Link to repo here: https://github.com/vincentarelbundock/rethinking2
STAT 545 Data wrangling, exploration, and analysis with R 💯
By Jenny Bryan
What is this?
Excerpt from site: This site is about everything that comes up during data analysis except for statistical modelling and inference. This might strike you as strange, given R’s statistical roots. First, let me assure you we believe that modelling and inference are important. But the world already offers a lot of great resources for doing statistics with R.
- Link to the site: https://stat545.com/
Statistics of DOOM Youtube Channel
What is this?
Excerpt from site: Support Statistics of DOOM! This page and the YouTube channel to help people learn statistics by including step-by-step instructions for SPSS, R, Excel, and other programs. Demonstrations are provided including power, data screening, analysis, write up tips, effect sizes, and graphs. Help guides and course materials are also provided!
- Link to Youtube Channel here: https://www.youtube.com/c/StatisticsofDOOM/videos
Summary and Analysis of Extension Program Evaluation in R
Added Sun Sep 13th, 2020
What is this?
Excerpt from book: This book is written for students at the undergraduate level with no prior knowledge of the analysis of experiments, and with no prior knowledge of computer programming. This being said, students with no background in these areas will need to apply care and dedication in order to understand the material and the computer code used in examples. These students may also need to explore the optional readings to obtain a better foundation in statistical thinking and theory.
- Link to book here: https://rcompanion.org/handbook/index.html
Summary of Mixed Models as HTML Table
Added Sun Dec 31st, 2020
What is this?
Excerpt from site: This vignette shows examples for using tab_model() to create HTML tables for mixed models. Basically, tab_model() behaves in a very similar way for mixed models as for other, simple regression models, as shown in this vignette.
- Link to vignette here: https://cran.r-project.org/web/packages/sjPlot/vignettes/tab_mixed.html
Teaching Methods with R
What is this?
Excerpt from site: Research Methods in R is a set of guides on how to use R as your central research methods tool. The target audience is psychology undergraduate students. Research Methods in R is Creative Commons, so you are free to reuse these materials and adapt them as you wish, as long as you attribute them to their authors, and as long as your modifications have a Creative Commons licence. They come with absolutely no warranty of any kind.
- Link to site here: https://ajwills72.github.io/rminr/
Teacup Giraffes_Intro to Statistics🦒
By Hasse Walum & Desirée De Leon
What is this?
The site’s purpose is to introduce you to statistics with R. Very concise and clear 😄 !
- Link to the “Introduction to the Normal Distribution” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/02_bellCurve.html
- Link to the “Measures of centrality: Mean, Median, & Mode” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/03_mean.html
- Link to the “The Spread of the Data: Variance * Standard Deviation” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/04_variance.html
- Link to the “A tale of two variables: Covariance & Correlation” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/05_correlation.html
- Link to the “Introduction to Inference: Standard Error” module here: https://tinystats.github.io/teacups-giraffes-and-statistics/06_standardError.html
Test your model!! Performance package
What is this?
Test if your model is a good model! The primary goal of the performance package is to provide utilities for computing indices of model quality and goodness of fit. This includes measures like r-squared (R2), root mean squared error (RMSE) or intraclass correlation coefficient (ICC), but also functions to check (mixed) models for overdispersion, zero-inflation, convergence or singularity.
- Link to information: https://easystats.github.io/performance/index.html
Visualizing research trends
By Dan Quintana
What is this?
Dan Quintana made a screencast of how to visualise research trends for a paper introduction or grant application using the {europepmc}
- Link to screencast: https://twitter.com/dsquintana/status/1166083585771802626?s=20
- Link to code: https://gist.github.com/dsquintana/b512b715786088339b61a7fb79367d5e
(What to do) When Predictors Co-Vary
What is this?
Excerpt from blog: Co-varying predictors can be a messy business. They make estimates unstable, reducing our statistical power and making interpretation more difficult. In this post I will demonstrate how ignoring the presence of co-variation between predictors when exploring our models can lead to odd results and how we might deal with this issue.
- Link to blog here: https://shouldbewriting.netlify.app/posts/2020-08-11-when-predictors-covary/
Power Analysis
This section has all resources I encounter to run power analysis regardless of them using R or not.
powerLATE:
Excerpt from site: powerLATE implements the generalized power analysis for the local average treatment effect (LATE), proposed by Bansak (2020).
Power analysis is in the context of estimating the LATE (also known as the complier average causal effect, or CACE), with calculations based on a test of the null hypothesis that the LATE equals 0 with a two-sided alternative. The method uses standardized effect sizes to place a conservative bound on the power under minimal assumptions. powerLATE allows users to recover power, sample size requirements, or minimum detectable effect sizes. It also allows users to work with absolute effects rather than effect sizes, to specify an additional assumption to narrow the bounds, and to incorporate covariate adjustment.
Simulation for Power Analysis by Nick Huntington-Klein:
Excerpt from site: In this document we’ll talk about power analysis in general and how it’s done, and then we’ll go into how to perform a power analysis using simulation in R, making use of tools from the tidyverse.