You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Psy2R - an R package for better inference in multivariate statistical analysis
Short description and the goals for the OHBM BrainHack
We consistently use massive datasets across neuroscience and psychology. The routine gathering of big data requires that we are well equipped with tools that allow us to conduct appropriate multivariate statistics. This project aims to produce an R package that allows the researcher to overcome little discussed limitations of traditional multivariate statistical analyses.
Multivariate statistical analysis (e.g. MANOVA and repeated-measures ANOVA) typically follows a two stage procedure; an omnibus test of the global null hypothesis followed by post-hoc tests of specific effects. It is not well known that under certain circumstances, such as when the omnibus test is overpowered, that the type 1 error rate for this procedure is drastically inflated, sometimes to a type 1 error rate of 1! It is even less well known that this procedure can lead to an even lessor known type IV error, which is the incorrect interpretation of a correctly rejected hypothesis. This is caused when the follow-up contrasts are inadequate to test the question of interest, as can occur when testing simple effects.
It is possible to avoid these dragons by using an alternative procedure where all inferences are derived from simultaneous confidence intervals (SCIs) on contrasts of interests. The 'simultaneous' bit means that the same statistic contributes to both the omnibus and the contrast tests for significance, which controls the type 1 error rate. Even better, computing confidence intervals on contrasts of interests allows reseachers to move away from binary decision-making (is something significant or not?) to interpretations involving magnitude (how big is this effect likely to be at the population level?).
One piece of software (PSY) can produce SCIs appropriate for both planned analyses (where contrasts are defined independently of the data) and for more flexible analyses where contrasts are defined on a post-hoc basis. However, this software is only available for use on windows and cannot be scripted into reproducible workflows. Our goal is to build an R package that implements the functions of PSY, and to make this method of statistical inference available to the masses!
Our goals for OHBM Brainhack 2024 are:
Convert some key functions of the Psy source pascal code to R functions: these functions compute the largest contrast effect you could expect to get if the null hypothesis is true, when you have between and within repeated measures.
Explore the overlap between Psy, emmeans and MBESS, to make sure we recycle where appropriate
Replicate analyses between the original Psy software and the R implementation
Kelly Garner
Github: kel-github
Discord: @kel-accords
Main Hub
Seoul
Link to the Project pitch
No response
Other hubs covered by the leaders
Seoul
Hybrid (Asia / Pacific)
Hybrid (Europe / Middle East / Africa)
Hybrid (Americas)
Skills
Coding skills in R
Package development in R
Understanding of, experience in, or curiosity with multivariate statistical analysis
Understanding of, experience in, or curiosity with analysis of repeated-measures data (i.e. ANOVA+)
A desire for tools for better statistical inferences
A love of documentation and/or sensible naming of variables
Esoteric coding skills - i.e. pascal
Psy2R - an R package for better inference in multivariate statistical analysis. We're making a package to reduce type 1 and type 4 errors in multivariate analysis - for the people!
Short name for the Discord chat channel (~15 chars)
Psy2R
Please read and follow the OHBM Code of Conduct
I agree to follow the OHBM Code of Conduct during the hackathon
The text was updated successfully, but these errors were encountered:
Title
Psy2R - an R package for better inference in multivariate statistical analysis
Short description and the goals for the OHBM BrainHack
We consistently use massive datasets across neuroscience and psychology. The routine gathering of big data requires that we are well equipped with tools that allow us to conduct appropriate multivariate statistics. This project aims to produce an R package that allows the researcher to overcome little discussed limitations of traditional multivariate statistical analyses.
Multivariate statistical analysis (e.g. MANOVA and repeated-measures ANOVA) typically follows a two stage procedure; an omnibus test of the global null hypothesis followed by post-hoc tests of specific effects. It is not well known that under certain circumstances, such as when the omnibus test is overpowered, that the type 1 error rate for this procedure is drastically inflated, sometimes to a type 1 error rate of 1! It is even less well known that this procedure can lead to an even lessor known type IV error, which is the incorrect interpretation of a correctly rejected hypothesis. This is caused when the follow-up contrasts are inadequate to test the question of interest, as can occur when testing simple effects.
It is possible to avoid these dragons by using an alternative procedure where all inferences are derived from simultaneous confidence intervals (SCIs) on contrasts of interests. The 'simultaneous' bit means that the same statistic contributes to both the omnibus and the contrast tests for significance, which controls the type 1 error rate. Even better, computing confidence intervals on contrasts of interests allows reseachers to move away from binary decision-making (is something significant or not?) to interpretations involving magnitude (how big is this effect likely to be at the population level?).
One piece of software (PSY) can produce SCIs appropriate for both planned analyses (where contrasts are defined independently of the data) and for more flexible analyses where contrasts are defined on a post-hoc basis. However, this software is only available for use on windows and cannot be scripted into reproducible workflows. Our goal is to build an R package that implements the functions of PSY, and to make this method of statistical inference available to the masses!
Our goals for OHBM Brainhack 2024 are:
Link to the Project
https://github.com/kel-github/PSY2R
Image/Logo for the OHBM brainhack website
https://github.com/kel-github/PSY2R/blob/main/presentations/Psy2R-logo.jpeg
Project lead
Kelly Garner
Github: kel-github
Discord: @kel-accords
Main Hub
Seoul
Link to the Project pitch
No response
Other hubs covered by the leaders
Skills
Coding skills in R
Package development in R
Understanding of, experience in, or curiosity with multivariate statistical analysis
Understanding of, experience in, or curiosity with analysis of repeated-measures data (i.e. ANOVA+)
A desire for tools for better statistical inferences
A love of documentation and/or sensible naming of variables
Esoteric coding skills - i.e. pascal
Recommended tutorials for new contributors
https://docs.github.com/en/get-started/exploring-projects-on-github/contributing-to-a-project
https://github.com/kel-github/PSY2R/blob/main/resources/PSYHELP.pdf
https://marissabarlaz.github.io/portfolio/contrastcoding/
https://rvlenth.github.io/emmeans/reference/emmeans-package.html
https://www.youtube.com/watch?v=gfPP2pQ8Rms
Good first issues
garner-code/PSY2R#33
garner-code/PSY2R#34
garner-code/PSY2R#12
Twitter summary
Psy2R - an R package for better inference in multivariate statistical analysis. We're making a package to reduce type 1 and type 4 errors in multivariate analysis - for the people!
Short name for the Discord chat channel (~15 chars)
Psy2R
Please read and follow the OHBM Code of Conduct
The text was updated successfully, but these errors were encountered: